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Hu G, Zhang H, Xie N, Hussien AG. CMRLCCOA: Multi-Strategy Enhanced Coati Optimization Algorithm for Engineering Designs and Hypersonic Vehicle Path Planning. Biomimetics (Basel) 2024; 9:399. [PMID: 39056840 PMCID: PMC11275167 DOI: 10.3390/biomimetics9070399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 07/28/2024] Open
Abstract
The recently introduced coati optimization algorithm suffers from drawbacks such as slow search velocity and weak optimization precision. An enhanced coati optimization algorithm called CMRLCCOA is proposed. Firstly, the Sine chaotic mapping function is used to initialize the CMRLCCOA as a way to obtain better-quality coati populations and increase the diversity of the population. Secondly, the generated candidate solutions are updated again using the convex lens imaging reverse learning strategy to expand the search range. Thirdly, the Lévy flight strategy increases the search step size, expands the search range, and avoids the phenomenon of convergence too early. Finally, utilizing the crossover strategy can effectively reduce the search blind spots, making the search particles constantly close to the global optimum solution. The four strategies work together to enhance the efficiency of COA and to boost the precision and steadiness. The performance of CMRLCCOA is evaluated on CEC2017 and CEC2019. The superiority of CMRLCCOA is comprehensively demonstrated by comparing the output of CMRLCCOA with the previously submitted algorithms. Besides the results of iterative convergence curves, boxplots and a nonparametric statistical analysis illustrate that the CMRLCCOA is competitive, significantly improves the convergence accuracy, and well avoids local optimal solutions. Finally, the performance and usefulness of CMRLCCOA are proven through three engineering application problems. A mathematical model of the hypersonic vehicle cruise trajectory optimization problem is developed. The result of CMRLCCOA is less than other comparative algorithms and the shortest path length for this problem is obtained.
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Affiliation(s)
- Gang Hu
- Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, China; (H.Z.); (N.X.)
- School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
| | - Haonan Zhang
- Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, China; (H.Z.); (N.X.)
| | - Ni Xie
- Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, China; (H.Z.); (N.X.)
| | - Abdelazim G. Hussien
- Department of Computer and Information Science, Linköping University, 58183 Linköping, Sweden;
- Faculty of Science, Fayoum University, Faiyum 63514, Egypt
- Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
- MEU Research Unit, Middle East University, Amman 11831, Jordan
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2
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Ravichandran S, Manoharan P, Jangir P, Selvarajan S. Resistance-capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems. Sci Rep 2023; 13:15909. [PMID: 37741875 PMCID: PMC10517984 DOI: 10.1038/s41598-023-42969-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 09/17/2023] [Indexed: 09/25/2023] Open
Abstract
The primary objective of this study is to delve into the application and validation of the Resistance Capacitance Optimization Algorithm (RCOA)-a new, physics-inspired metaheuristic optimization algorithm. The RCOA, intriguingly inspired by the time response of a resistance-capacitance circuit to a sudden voltage fluctuation, has been earmarked for solving complex numerical and engineering design optimization problems. Uniquely, the RCOA operates without any control/tunable parameters. In the first phase of this study, we evaluated the RCOA's credibility and functionality by deploying it on a set of 23 benchmark test functions. This was followed by thoroughly examining its application in eight distinct constrained engineering design optimization scenarios. This methodical approach was undertaken to dissect and understand the algorithm's exploration and exploitation phases, leveraging standard benchmark functions as the yardstick. The principal findings underline the significant effectiveness of the RCOA, especially when contrasted against various state-of-the-art algorithms in the field. Beyond its apparent superiority, the RCOA was put through rigorous statistical non-parametric testing, further endorsing its reliability as an innovative tool for handling complex engineering design problems. The conclusion of this research underscores the RCOA's strong performance in terms of reliability and precision, particularly in tackling constrained engineering design optimization challenges. This statement, derived from the systematic study, strengthens RCOA's position as a potentially transformative tool in the mathematical optimization landscape. It also paves the way for further exploration and adaptation of physics-inspired algorithms in the broader realm of optimization problems.
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Affiliation(s)
- Sowmya Ravichandran
- Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Premkumar Manoharan
- Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, 560078, India.
| | - Pradeep Jangir
- Rajasthan Rajya Vidyut Prasaran Nigam Limited, Sikar, Rajasthan, 332025, India
| | - Shitharth Selvarajan
- Department of Computer Science, Kebri Dehar University, 001, Kebri Dehar, Ethiopia.
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3
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Lin Y, Heidari AA, Wang S, Chen H, Zhang Y. An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems. Biomimetics (Basel) 2023; 8:441. [PMID: 37754192 PMCID: PMC10526405 DOI: 10.3390/biomimetics8050441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/07/2023] [Accepted: 09/12/2023] [Indexed: 09/28/2023] Open
Abstract
The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is subject to limitations, including inadequate diversity, premature convergence, and susceptibility to local optima. To overcome these challenges, this study introduces two adjusted strategies to enhance the original HGS algorithm. The first adaptive strategy combines the Logarithmic Spiral (LS) technique with Opposition-based Learning (OBL), resulting in the LS-OBL approach. This strategy plays a pivotal role in reducing the search space and maintaining population diversity within HGS, effectively augmenting the algorithm's exploration capabilities. The second adaptive strategy, the dynamic Rosenbrock Method (RM), contributes to HGS by adjusting the search direction and step size. This adjustment enables HGS to escape from suboptimal solutions and enhances its convergence accuracy. Combined, these two strategies form the improved algorithm proposed in this study, referred to as RLHGS. To assess the efficacy of the introduced strategies, specific experiments are designed to evaluate the impact of LS-OBL and RM on enhancing HGS performance. The experimental results unequivocally demonstrate that integrating these two strategies significantly enhances the capabilities of HGS. Furthermore, RLHGS is compared against eight state-of-the-art algorithms using 23 well-established benchmark functions and the CEC2020 test suite. The experimental results consistently indicate that RLHGS outperforms the other algorithms, securing the top rank in both test suites. This compelling evidence substantiates the superior functionality and performance of RLHGS compared to its counterparts. Moreover, RLHGS is applied to address four constrained real-world engineering optimization problems. The final results underscore the effectiveness of RLHGS in tackling such problems, further supporting its value as an efficient optimization method.
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Affiliation(s)
- Yaoyao Lin
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China; (Y.L.); (A.A.H.)
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China; (Y.L.); (A.A.H.)
| | - Shuihua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK;
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China; (Y.L.); (A.A.H.)
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK;
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4
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Socio-inspired evolutionary algorithms: a unified framework and survey. Soft comput 2023. [DOI: 10.1007/s00500-023-07929-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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5
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Pan JS, Yue L, Chu SC, Hu P, Yan B, Yang H. Binary Bamboo Forest Growth Optimization Algorithm for Feature Selection Problem. ENTROPY (BASEL, SWITZERLAND) 2023; 25:314. [PMID: 36832680 PMCID: PMC9955014 DOI: 10.3390/e25020314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/27/2023] [Accepted: 02/05/2023] [Indexed: 06/18/2023]
Abstract
Inspired by the bamboo growth process, Chu et al. proposed the Bamboo Forest Growth Optimization (BFGO) algorithm. It incorporates bamboo whip extension and bamboo shoot growth into the optimization process. It can be applied very well to classical engineering problems. However, binary values can only take 0 or 1, and for some binary optimization problems, the standard BFGO is not applicable. This paper firstly proposes a binary version of BFGO, called BBFGO. By analyzing the search space of BFGO under binary conditions, the new curve V-shaped and Taper-shaped transfer function for converting continuous values into binary BFGO is proposed for the first time. A long-mutation strategy with a new mutation approach is presented to solve the algorithmic stagnation problem. Binary BFGO and the long-mutation strategy with a new mutation are tested on 23 benchmark test functions. The experimental results show that binary BFGO achieves better results in solving the optimal values and convergence speed, and the variation strategy can significantly enhance the algorithm's performance. In terms of application, 12 data sets derived from the UCI machine learning repository are selected for feature-selection implementation and compared with the transfer functions used by BGWO-a, BPSO-TVMS and BQUATRE, which demonstrates binary BFGO algorithm's potential to explore the attribute space and choose the most significant features for classification issues.
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Affiliation(s)
- Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
- Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
| | - Longkang Yue
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Pei Hu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Bin Yan
- College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Hongmei Yang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
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6
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Abdel-Basset M, El-Shahat D, Jameel M, Abouhawwash M. Exponential distribution optimizer (EDO): a novel math-inspired algorithm for global optimization and engineering problems. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10403-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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7
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A league-knock-out tournament quantum particle swarm optimization algorithm for nonlinear constrained optimization problems and applications. EVOLVING SYSTEMS 2023. [DOI: 10.1007/s12530-023-09485-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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8
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Acharya D, Das DK. A novel Human Conception Optimizer for solving optimization problems. Sci Rep 2022; 12:21631. [PMID: 36517488 PMCID: PMC9751073 DOI: 10.1038/s41598-022-25031-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022] Open
Abstract
Computational techniques are widely used to solve complex optimization problems in different fields such as engineering, finance, biology, and so on. In this paper, the Human Conception Optimizer (HCO) is proposed as a novel metaheuristic algorithm to solve any optimization problems. The idea of this algorithm is based on some biological principles of the human conception process, such as the selective nature of cervical gel in the female reproductive system to allow only healthy sperm cells into the cervix, the guidance nature of mucus gel to help sperm track a genital tracking path towards the egg in the Fallopian tube, the asymmetric nature of flagellar movement which allows sperm cells to move in the reproductive system, the sperm hyperactivation process to make them able to fertilize an egg. Thus, the strategies pursued by the sperm in searching for the egg in the Fallopian tube are modeled mathematically. The best sperm which will meet the position of the egg will be the solution of the algorithm. The performance of the proposed HCO algorithm is examined with a set of basic benchmark test functions called IEEE CEC-2005 and IEEE CEC-2020. A comparative study is also performed between the HCO algorithm and other available algorithms. The significance of the results is verified with statistical test methods. To validate the proposed HCO algorithm, two real-world engineering optimization problems are examined. For this purpose, a complex 14 over-current relay based IEEE 8 bus distribution system is considered. With the proposed algorithm, an improvement of 50% to 60% in total relay operating times is observed comparing with some existing results for the same system. Another engineering problem of designing an optimal proportional integral derivative (PID) controller for a blower driven patient hose mechanical ventilator (MV) is examined. A significant improvement in terms of response time, settling time is observed in the MV system by comparing with existing results.
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Affiliation(s)
- Debasis Acharya
- Department of Electrical and Electronics Engineering, National Institute of Technology Nagaland, Dimapur, 797103, India
| | - Dushmanta Kumar Das
- Department of Electrical and Electronics Engineering, National Institute of Technology Nagaland, Dimapur, 797103, India.
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9
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Quantum-inspired metaheuristic algorithms: comprehensive survey and classification. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10280-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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10
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Tanhaeean M, Tavakkoli-Moghaddam R, Akbari AH. Boxing Match Algorithm: a new meta-heuristic algorithm. Soft comput 2022. [DOI: 10.1007/s00500-022-07518-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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11
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Wu H, Zhang X, Song L, Zhang Y, Gu L, Zhao X. Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5191758. [PMID: 36337271 PMCID: PMC9630037 DOI: 10.1155/2022/5191758] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 07/21/2022] [Accepted: 08/18/2022] [Indexed: 08/26/2023]
Abstract
This paper proposes a new meta-heuristic algorithm, named wild geese migration optimization (GMO) algorithm. It is inspired by the social behavior of wild geese swarming in nature. They maintain a special formation for long-distance migration in small groups for survival and reproduction. The mathematical model is established based on these social behaviors to solve optimization problems. Meanwhile, the performance of the GMO algorithm is tested on the stable benchmark function of CEC2017, and its potential for dealing with practical problems is studied in five engineering design problems and the inverse kinematics solution of robot. The test results show that the GMO algorithm has excellent computational performance compared to other algorithms. The practical application results show that the GMO algorithm has strong applicability, more accurate optimization results, and more competitiveness in challenging problems with unknown search space, compared with well-known algorithms in the literature. The proposal of GMO algorithm enriches the team of swarm intelligence optimization algorithms and also provides a new solution for solving engineering design problems and inverse kinematics of robots.
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Affiliation(s)
- Honggang Wu
- School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Xinming Zhang
- School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
- School of Mechatronic Engineering and Automation, Foshan University, Foshan 528225, China
| | - Linsen Song
- School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Yufei Zhang
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
| | - Lidong Gu
- School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Xiaonan Zhao
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
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12
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Akinola OA, Ezugwu AE, Oyelade ON, Agushaka JO. A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets. Sci Rep 2022; 12:14945. [PMID: 36056062 PMCID: PMC9440036 DOI: 10.1038/s41598-022-18993-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 08/23/2022] [Indexed: 11/08/2022] Open
Abstract
The dwarf mongoose optimization (DMO) algorithm developed in 2022 was applied to solve continuous mechanical engineering design problems with a considerable balance of the exploration and exploitation phases as a metaheuristic approach. Still, the DMO is restricted in its exploitation phase, somewhat hindering the algorithm's optimal performance. In this paper, we proposed a new hybrid method called the BDMSAO, which combines the binary variants of the DMO (or BDMO) and simulated annealing (SA) algorithm. In the modelling and implementation of the hybrid BDMSAO algorithm, the BDMO is employed and used as the global search method and the simulated annealing (SA) as the local search component to enhance the limited exploitative mechanism of the BDMO. The new hybrid algorithm was evaluated using eighteen (18) UCI machine learning datasets of low and medium dimensions. The BDMSAO was also tested using three high-dimensional medical datasets to assess its robustness. The results showed the efficacy of the BDMSAO in solving challenging feature selection problems on varying datasets dimensions and its outperformance over ten other methods in the study. Specifically, the BDMSAO achieved an overall result of 61.11% in producing the highest classification accuracy possible and getting 100% accuracy on 9 of 18 datasets. It also yielded the maximum accuracy obtainable on the three high-dimensional datasets utilized while achieving competitive performance regarding the number of features selected.
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Affiliation(s)
- Olatunji A Akinola
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa
| | - Absalom E Ezugwu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa.
| | - Olaide N Oyelade
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa
| | - Jeffrey O Agushaka
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa
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13
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Akinola OO, Ezugwu AE, Agushaka JO, Zitar RA, Abualigah L. Multiclass feature selection with metaheuristic optimization algorithms: a review. Neural Comput Appl 2022; 34:19751-19790. [PMID: 36060097 PMCID: PMC9424068 DOI: 10.1007/s00521-022-07705-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 08/02/2022] [Indexed: 11/24/2022]
Abstract
Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.
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Affiliation(s)
- Olatunji O. Akinola
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
| | - Absalom E. Ezugwu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
| | - Jeffrey O. Agushaka
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
| | - Raed Abu Zitar
- Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, 38044 Abu Dhabi, United Arab Emirates
| | - Laith Abualigah
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan
- Faculty of Inforsmation Technology, Middle East University, Amman, 11831 Jordan
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14
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Abstract
Wild horse optimizer (WHO) is a recently proposed metaheuristic algorithm that simulates the social behavior of wild horses in nature. Although WHO shows competitive performance compared to some algorithms, it suffers from low exploitation capability and stagnation in local optima. This paper presents an improved wild horse optimizer (IWHO), which incorporates three improvements to enhance optimizing capability. The main innovation of this paper is to put forward the random running strategy (RRS) and the competition for waterhole mechanism (CWHM). The random running strategy is employed to balance exploration and exploitation, and the competition for waterhole mechanism is proposed to boost exploitation behavior. Moreover, the dynamic inertia weight strategy (DIWS) is utilized to optimize the global solution. The proposed IWHO is evaluated using twenty-three classical benchmark functions, ten CEC 2021 test functions, and five real-world optimization problems. High-dimensional cases (D = 200, 500, 1000) are also tested. Comparing nine well-known algorithms, the experimental results of test functions demonstrate that the IWHO is very competitive in terms of convergence speed, precision, accuracy, and stability. Further, the practical capability of the proposed method is verified by the results of engineering design problems.
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15
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Zheng R, Jia H, Abualigah L, Liu Q, Wang S. An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:473-512. [PMID: 34903000 DOI: 10.3934/mbe.2022023] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (RMOP) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other well-known optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.
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Affiliation(s)
- Rong Zheng
- School of Information Engineering, Sanming University, Sanming 365004, China
| | - Heming Jia
- School of Information Engineering, Sanming University, Sanming 365004, China
| | - Laith Abualigah
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
- School of Computer Science, Universiti Sains Malaysia, Penang 11800, Malaysia
| | - Qingxin Liu
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Shuang Wang
- School of Information Engineering, Sanming University, Sanming 365004, China
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16
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Ghalehasadi A, Ashrafi S, Alizadeh D, Meriç N. Gamma ray interactions based optimization algorithm: Application in radioisotope identification. NUCLEAR ENGINEERING AND TECHNOLOGY 2021. [DOI: 10.1016/j.net.2021.05.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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17
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The Archerfish Hunting Optimizer: A Novel Metaheuristic Algorithm for Global Optimization. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-06208-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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18
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19
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A Novel Quantum Firefly Algorithm for Global Optimization. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-05608-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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20
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Manna AK, Akhtar M, Shaikh AA, Bhunia AK. Optimization of a deteriorated two-warehouse inventory problem with all-unit discount and shortages via tournament differential evolution. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107388] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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21
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Naik A, Satapathy SC, Abraham A. Modified Social Group Optimization—a meta-heuristic algorithm to solve short-term hydrothermal scheduling. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106524] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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22
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A comparative study of social group optimization with a few recent optimization algorithms. COMPLEX INTELL SYST 2020. [DOI: 10.1007/s40747-020-00189-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
AbstractFrom the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. Optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms.
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Al-Betar MA, Alyasseri ZAA, Awadallah MA, Abu Doush I. Coronavirus herd immunity optimizer (CHIO). Neural Comput Appl 2020; 33:5011-5042. [PMID: 32874019 PMCID: PMC7451802 DOI: 10.1007/s00521-020-05296-6] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 08/12/2020] [Indexed: 12/22/2022]
Abstract
In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. In order to protect other members of society from the disease, social distancing is suggested by health experts. Herd immunity is a state the population reaches when most of the population is immune which results in the prevention of disease transmission. These concepts are modeled in terms of optimization concepts. CHIO mimics the herd immunity strategy as well as the social distancing concepts. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. This is to determine how the newly generated solution updates its genes with social distancing strategies. CHIO is evaluated using 23 well-known benchmark functions. Initially, the sensitivity of CHIO to its parameters is studied. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. For more validations, three real-world engineering optimization problems extracted from IEEE-CEC 2011 are used. Again, CHIO is proved to be efficient. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains.
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Affiliation(s)
- Mohammed Azmi Al-Betar
- Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, P.O. Box 50, Al-Huson, Irbid Jordan
- Department of Information Technology - MSAI, College of Engineering and Information Technology Ajman University, Ajman, UAE
| | - Zaid Abdi Alkareem Alyasseri
- Center for Artificial Intelligence, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Malaysia
- ECE Department, Faculty of Engineering, University of Kufa, Najaf, Iraq
| | | | - Iyad Abu Doush
- Computer Science Department, American University of Kuwait, Salmiya, Kuwait
- Computer Science Department, Yarmouk University, Irbid, Jordan
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Dadvar AA, Vahidi J, Hajizadeh Z, Maleki A, Reza Bayati M. Experimental study on classical and metaheuristics algorithms for optimal nano-chitosan concentration selection in surface coating and food packaging. Food Chem 2020; 335:127681. [PMID: 32739803 DOI: 10.1016/j.foodchem.2020.127681] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 07/03/2020] [Accepted: 07/24/2020] [Indexed: 11/26/2022]
Abstract
In this study the Lagrange interpolation optimization algorithm based on two variables with respect to all experimental replicates (POA), was compared with two other heuristics methods (WOA and GOA). Modification of the apple surface by an edible nano coating solution in food packaging was used as case study. The experiment was performed as a factorial test based on completely randomized design by 100 permutations data sets. Results showed a significant difference between the three optimization methods (POA, WOA and GOA) which indicates the necessity of optimization and also efficiency of the present POA. The optimum result by POA, similar to a rose petal property, could rise 72% in surface contact angle (CA). The scanning electron microscopy (SEM) images of the derived surfaces showed almost a uniform spherical nanoparticles morphology. Remarkable advantages of this new approach are no additional material requirement, healthful, easy, inexpensive, fast and affordable technique for surface improvement.
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Affiliation(s)
- Ali Akbar Dadvar
- Department of Mathematics, Iran University of Science and Technology, Tehran, Iran; Catalysts and Organic Synthesis Research Laboratory, Department of Chemistry, Iran University of Science and Technology, Tehran 16846-13114, Iran
| | - Javad Vahidi
- Department of Mathematics, Iran University of Science and Technology, Tehran, Iran
| | - Zoleikha Hajizadeh
- Catalysts and Organic Synthesis Research Laboratory, Department of Chemistry, Iran University of Science and Technology, Tehran 16846-13114, Iran
| | - Ali Maleki
- Catalysts and Organic Synthesis Research Laboratory, Department of Chemistry, Iran University of Science and Technology, Tehran 16846-13114, Iran.
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Nasir M, Sadollah A, Choi YH, Kim JH. A comprehensive review on water cycle algorithm and its applications. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05112-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Differential evolution based global best algorithm: an efficient optimizer for solving constrained and unconstrained optimization problems. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-2426-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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Sandpiper optimization algorithm: a novel approach for solving real-life engineering problems. APPL INTELL 2019. [DOI: 10.1007/s10489-019-01507-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Dhiman G, Kumar V. Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2018.11.024] [Citation(s) in RCA: 277] [Impact Index Per Article: 55.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Dhiman G, Kumar V. Spotted Hyena Optimizer for Solving Complex and Non-linear Constrained Engineering Problems. HARMONY SEARCH AND NATURE INSPIRED OPTIMIZATION ALGORITHMS 2019. [DOI: 10.1007/978-981-13-0761-4_81] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Dhiman G, Kaur A. A Hybrid Algorithm Based on Particle Swarm and Spotted Hyena Optimizer for Global Optimization. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2019. [DOI: 10.1007/978-981-13-1592-3_47] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Shayanfar H, Gharehchopogh FS. Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.07.033] [Citation(s) in RCA: 166] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Morshedian A, Razmara J, Lotfi S. A novel approach for protein structure prediction based on an estimation of distribution algorithm. Soft comput 2018. [DOI: 10.1007/s00500-018-3130-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Nenavath H, Jatoth RK. Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3376-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Artificial neural network weights optimization based on social-based algorithm to realize sediment over the river. Soft comput 2014. [DOI: 10.1007/s00500-014-1258-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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