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Huang H, Zheng B, Wei X, Zhou Y, Zhang Y. NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm. Sci Rep 2024; 14:4310. [PMID: 38383608 PMCID: PMC10881516 DOI: 10.1038/s41598-024-54991-0] [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: 04/01/2023] [Accepted: 02/19/2024] [Indexed: 02/23/2024] Open
Abstract
Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchmark functions for experiments. By comprehensive comparison of the test function results and Friedman test results, the results obtained by using the NSCSO algorithm to solve the MOP problem have better performance. Compares the NSCSO algorithm with other multi-objective optimization algorithms in six different engineering design problems. The results show that NSCSO not only performs well in multi-objective function tests, but also obtains realistic solutions in multi-objective engineering example problems.
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Affiliation(s)
- Huajuan Huang
- College of Artificial Intelligence, Guangxi Minzu University, Nanning, 530006, China
| | - Baofeng Zheng
- College of Electronic Information, Guangxi Minzu University, Nanning, 530006, China
| | - Xiuxi Wei
- College of Artificial Intelligence, Guangxi Minzu University, Nanning, 530006, China.
| | - Yongquan Zhou
- College of Artificial Intelligence, Guangxi Minzu University, Nanning, 530006, China
- Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi Minzu University, Nanning, 530006, China
| | - Yuedong Zhang
- College of Electronic Information, Guangxi Minzu University, Nanning, 530006, China
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Biazar SM, Shehadeh HA, Ghorbani MA, Golmohammadi G, Saha A. Soil temperature forecasting using a hybrid artificial neural network in Florida subtropical grazinglands agro-ecosystems. Sci Rep 2024; 14:1535. [PMID: 38233414 PMCID: PMC10794231 DOI: 10.1038/s41598-023-48025-4] [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: 05/31/2023] [Accepted: 11/21/2023] [Indexed: 01/19/2024] Open
Abstract
Soil temperature is a key meteorological parameter that plays an important role in determining rates of physical, chemical and biological reactions in the soil. Ground temperature can vary substantially under different land cover types and climatic conditions. Proper prediction of soil temperature is thus essential for the accurate simulation of land surface processes. In this study, two intelligent neural models-artificial neural networks (ANNs) and Sperm Swarm Optimization (SSO) were used for estimating of soil temperatures at four depths (5, 10, 20, 50 cm) using seven-year meteorological data acquired from Archbold Biological Station in South Florida. The results of this study in subtropical grazinglands of Florida showed that the integrated artificial neural network and SSO models (MLP-SSO) were more accurate tools than the original structure of artificial neural network methods for soil temperature forecasting. In conclusion, this study recommends the hybrid MLP-SSO model as a suitable tool for soil temperature prediction at different soil depths.
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Affiliation(s)
- Seyed Mostafa Biazar
- Department of Soil, Water and Ecosystem Sciences, University of Florida, IFAS/RCREC, Ona, FL, USA
| | - Hisham A Shehadeh
- Department of Artificial Intelligence and Computer Science, College of Computer Science and Informatics, Amman Arab University, Amman, Jordan
| | | | - Golmar Golmohammadi
- Department of Soil, Water and Ecosystem Sciences, University of Florida, IFAS/RCREC, Ona, FL, USA.
| | - Amartya Saha
- Archbold Biological Station, Buck Island Ranch, Lake Placid, FL, 33852, USA
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Shehadeh HA. Chernobyl disaster optimizer (CDO): a novel meta-heuristic method for global optimization. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08261-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Daoud MS, Shehab M, Al-Mimi HM, Abualigah L, Zitar RA, Shambour MKY. Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:2431-2449. [PMID: 36597494 PMCID: PMC9801167 DOI: 10.1007/s11831-022-09872-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into; GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.
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Affiliation(s)
| | - Mohammad Shehab
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953 Jordan
| | - Hani M. Al-Mimi
- Department of Cybersecurity, Al-Zaytoonah University, Amman, Jordan
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq, 25113 Jordan
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan
- Faculty of Information Technology, Middle East University, Amman, 11831 Jordan
- Applied Science Research Center, Applied Science Private University, Amman, 11931 Jordan
- School of Computer Sciences, Universiti Sains Malaysia, 11800 George Town, Pulau Pinang Malaysia
- Center for Engineering Application &
Technology Solutions, Ho Chi Minh City Open University, Ho Chi Minh, Viet Nam
| | - Raed Abu Zitar
- Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Mohd Khaled Yousef Shambour
- The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research, Umm Al-Qura University, Mecca, Saudi Arabia
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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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A Chaotic Search-Based Hybrid Optimization Technique for Automatic Load Frequency Control of a Renewable Energy Integrated Power System. SUSTAINABILITY 2022. [DOI: 10.3390/su14095668] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
In this work, a chaotic search-based hybrid Sperm Swarm Optimized-Gravitational Search Algorithm (CSSO-GSA) is proposed for automatic load frequency control (ALFC) of a hybrid power system (HPS). The HPS model is developed using multiple power sources (thermal, bio-fuel, and renewable energy (RE)) that generate power to balance the system’s demand. To regulate the frequency of the system, the control parameters of the proportional-integral-derivative (PID) controller for ALFC are obtained by minimizing the integral time absolute error of HPS. The effectiveness of the proposed technique is verified with various combinations of power sources (all sources, thermal with bio-fuel, and thermal with RE) connected into the system. Further, the robustness of the proposed technique is investigated by performing a sensitivity analysis considering load variation and weather intermittency of RE sources in real-time. However, the type of RE source does not have any severe impact on the controller but the uncertainties present in RE power generation required a robust controller. In addition, the effectiveness of the proposed technique is validated with comparative and stability analysis. The results show that the proposed CSSO-GSA strategy outperforms the SSO, GSA, and hybrid SSO-GSA methods in terms of steady-state and transient performance indices. According to the results of frequency control optimization, the main performance indices such as settling time (ST) and integral time absolute error (ITAE) are significantly improved by 60.204% and 40.055% in area 1 and 57.856% and 39.820% in area 2, respectively, with the proposed CSSO-GSA control strategy compared to other existing control methods.
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Shehadeh HA, Mustafa HMJ, Tubishat M. A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions. INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING 2022. [DOI: 10.4018/ijamc.292507] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this paper, we propose a hybrid algorithm combining two different metaheuristic methods, “Genetic Algorithms (GA)” and “Sperm Swarm Optimization (SSO)”, for the global optimization of multimodal benchmarks functions. The proposed Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) operates based on incorporates concepts from GA and SSO in which generates individuals in a new iteration not only by crossover and mutation operations as proposed in GA, but also by techniques of local search of SSO. The main idea behind this hybridization is to reduce the probability of trapping in local optimum of multi modal problem. Our algorithm is compared against GA, and SSO metaheuristic optimization algorithms. The experimental results using a suite of multimodal benchmarks functions taken from the literature have evinced the superiority of the proposed HGASSO approach over the other approaches in terms of quality of results and convergence rates in which obtained good results in solving the multimodal benchmarks functions that include cosine, sine, and exponent in their formulation.
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Affiliation(s)
- Hisham A. Shehadeh
- Faculty of Computer Science and Informatics, Amman Arab University, Jordan
| | | | - Mohammad Tubishat
- School of Computing and Technology, Asia Pacific University of Technology and Innovation, Malaysia
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Shehadeh HA. A hybrid sperm swarm optimization and gravitational search algorithm (HSSOGSA) for global optimization. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05880-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications. ENERGIES 2018. [DOI: 10.3390/en11010097] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Prior studies in Wireless Sensor Network (WSN) optimization mostly concentrate on maximizing network coverage and minimizing network energy consumption. However, there are other factors that could affect the WSN Quality of Service (QoS). In this paper, four objective functions that affect WSN QoS, namely end-to-end delay, end-to-end latency, network throughput and energy efficiency are studied. Optimal value of packet payload size that is able to minimize the end-to-end delay and end-to-end latency, while also maximizing the network throughput and energy efficiency is sought. To do this, a smart grid application case study together with a WSN QoS model is used to find the optimal value of the packet payload size. Our proposed method, named Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP), along with other three state-of-the-art multi-objective optimization algorithms known as OMOPSO, NSGA-II and SPEA2, are utilized in this study. Different packet payload sizes are supplied to the algorithms and their optimal value is derived. From the experiments, the knee point and the intersection point of all the obtained Pareto fronts for all the algorithms show that the optimal packet payload size that manages the trade-offs between the four objective functions is equal to 45 bytes. The results also show that the performance of our proposed MOSFP method is highly competitive and found to have the best average value compared to the other three algorithms. Furthermore, the overall performance of MOSFP on four objective functions outperformed OMOPSO, NSGA-II and SPEA2 by 3%, 6% and 51%, respectively.
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