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An Overview of the Application of Harmony Search for Chemical Engineering Optimization. INTERNATIONAL JOURNAL OF CHEMICAL ENGINEERING 2022. [DOI: 10.1155/2022/1928343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Harmony search algorithm and its variants have been used in several applications in medicine, telecommunications, computer science, and engineering. This article reviews the global and multi-objective optimization for chemical engineering using harmony search. The main features of the HS method and several of its popular variants and hybrid versions including their relevant algorithm characteristics are described and discussed. A variety of global and multi-objective optimization problems from chemical engineering and their resolution using HS-based methods are also included. These problems involve thermodynamic calculations (phase stability analysis, phase equilibrium calculations, parameter estimation, and azeotrope calculation), heat exchanger design, distillation simulation, life cycle analysis, and water distribution systems, among others. Remarks on future developments of HS and its related algorithms for global and multi-objective optimization in chemical engineering are also provided in this review. HS is a reliable and promising stochastic optimizer to resolve challenging global and multi-objective optimization problems for process systems engineering.
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Shehab M, Abu-Hashem MA, Shambour MKY, Alsalibi AI, Alomari OA, Gupta JND, Alsoud AR, Abuhaija B, Abualigah L. A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:765-797. [PMID: 36157973 PMCID: PMC9490733 DOI: 10.1007/s11831-022-09817-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive review of the BA, as well as evaluates its main characteristics by comparing it with other optimization algorithms. The review paper highlights the performance of BA in different applications and the modifications that have been conducted by researchers (i.e., variants of BA). At the end, the conclusions focus on the current work on BA, highlighting its weaknesses, and suggest possible future research directions. The review paper will be helpful for the researchers and practitioners of BA 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
| | - Muhannad A. Abu-Hashem
- Department of Geomatics, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mohd Khaled Yousef Shambour
- The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research, Umm Al-Qura University, Mecca, Saudi Arabia
| | - Ahmed Izzat Alsalibi
- Department of Information Technology, Faculty of Engineering and Information Technology, Israa University, Gaza, Palestine
| | | | | | - Anas Ratib Alsoud
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan
| | - Belal Abuhaija
- Department of Computer Science, Wenzhou-Kean University, Wenzhou, China
| | - Laith Abualigah
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan
- Faculty of Information Technology, Middle East University, Amman, 11831 Jordan
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Shehab M, Abualigah L. Opposition-based learning multi-verse optimizer with disruption operator for optimization problems. Soft comput 2022. [DOI: 10.1007/s00500-022-07470-5] [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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Shi B, Yue Z, Yin S, Wang W, Yu H, Huang Z, Wang J. Adaptive binary multi-objective harmony search algorithm for channel selection and cross-subject generalization in motor imagery-based BCI. J Neural Eng 2022; 19. [PMID: 35772393 DOI: 10.1088/1741-2552/ac7d73] [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: 01/05/2022] [Accepted: 06/30/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Multi-channel EEG data containing redundant information and noise may result in low classification accuracy and high computational complexity, which limits the practicality of motor imagery-based BCI systems. Therefore, channel selection can improve BCI performance and contribute to user convenience. Additionally, cross-subject generalization is a key topic in the channel selection of motor imagery-based BCI. APPROACH In this study, an adaptive binary multi-objective harmony search (ABMOHS) algorithm is proposed to select the optimal set of channels. Furthermore, a new adaptive crosssubject generalization model (ACGM) is proposed. Three public motor imagery datasets were used to validate the effectiveness of the proposed method. MAIN RESULTS The Wilcoxon signed-rank test was performed on the test accuracies, and the results indicated that the ABMOHS method significantly outperformed all channels (p<0.001), the C3-Cz-C4 channels (p<0.001), and 20 channels (p<0.001) in the sensorimotor cortex. The ABMOHS algorithm based on Fisher's linear discriminant analysis (FLDA) and support vector machine (SVM) classifiers greatly reduces the number of selected channels, especially for larger channel sizes (Dataset 2), and obtains a comparative classification performance. Although there was no significant difference in test classification performance between ABMOHS and non-dominated sorting genetic algorithm II (NSGA-II) when FLDA and SVM were used, ABMOHS required less computational time than NSGA-II. Furthermore, the number of channels obtained by ABMOHS algorithm were significantly smaller than those obtained by CSP-Rank and correlation-based channel selection algorithm (CCS). Additionally, the generalization of ACGM to untrained subjects shows that the mean test classification accuracy of ACGM created by a small sample of trained subjects is significantly better than that of Special-16 and Special-32. SIGNIFICANCE The proposed method can reduce the calibration time in the training phase and improve the practicability of MI-BCI.
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Affiliation(s)
- Bin Shi
- School of Mechanical Engineering, Xi'an Jiaotong University, Institute of Robotics and Intelligent System, Xi'an, 710049, CHINA
| | - Zan Yue
- Xi'an Jiaotong University, Institute of Robotics and Intelligent System, Xi'an, Shaanxi, 710049, CHINA
| | - Shuai Yin
- Xi'an Jiaotong University, Institute of Robotics and Intelligent System, Xi'an, Shaanxi, 710049, CHINA
| | - Weizhen Wang
- Institute of Robotics and Intelligent Systems, Xi'an Jiaotong University, No.28, West Xianning Road, Xi'an, Shaanxi, 710049, CHINA
| | - Haoyong Yu
- Department of Bioengineering Faculty of Engineering, National University of Singapore, 5 Engineering Drive 1, E6, National University of Singapore, Singapore, 117608, SINGAPORE
| | - Zhen Huang
- Panyu Center Hospital, Department of Rehabilitation Medicine, Guangzhou, 511400, CHINA
| | - Jing Wang
- Xian Jiaotong University, Institute of Robotics and Intelligent Systems, Xi'an, Shaanxi, 710049, CHINA
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Zhang X, Meng QH, Zeng M. A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds. J Neural Eng 2022; 19. [PMID: 35732136 DOI: 10.1088/1741-2552/ac7b4a] [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: 01/31/2022] [Accepted: 06/22/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The classification of olfactory-induced electroencephalogram (olfactory EEG) signals has potential applications in disease diagnosis, emotion regulation, multimedia, and so on. To achieve high-precision classification, numerous EEG channels are usually used, but this also brings problems such as information redundancy, overfitting and high computational load. Consequently, channel selection is necessary to find and use the most effective channels. APPROACH In this study, we proposed a multi-strategy fusion binary harmony search (MFBHS) algorithm and combined it with the Riemannian geometry (RG) classification framework to select the optimal channel sets for olfactory EEG signal classification. MFBHS was designed by simultaneously integrating three strategies into the binary harmony search (BHS) algorithm, including an opposition-based learning strategy (OBL) for generating high-quality initial population, an adaptive parameter strategy (APS) for improving search capability, and a bitwise operation strategy (BOS) for maintaining population diversity. It performed channel selection directly on the covariance matrix of EEG signals, and used the number of selected channels and the classification accuracy computed by a Riemannian classifier to evaluate the newly generated subset of channels. MAIN RESULTS With five different classification protocols designed based on two public olfactory EEG datasets, the performance of MFBHS was evaluated and compared with some state-of-the-art algorithms. Experimental results reveal that our method can minimize the number of channels while achieving high classification accuracy compatible with using all the channels. In addition, cross-subject generalization tests of MFBHS channel selection show that subject-independent channels obtained through training can be directly used on untrained subjects without greatly compromising classification accuracy. SIGNIFICANCE The proposed MFBHS algorithm is a practical technique for effective use of EEG channels in olfactory recognition.
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Affiliation(s)
- Xiaonei Zhang
- Tianjin University, School of Electrical and Information Engineering, Tianjin, 300072, CHINA
| | - Qing-Hao Meng
- Tianjin University, School of Electrical and Information Engineering, Tianjin, 300072, CHINA
| | - Ming Zeng
- Tianjin University, School of Electrical and Information Engineering, Tianjin, 300072, CHINA
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Wang Z, Qin C, Wan B, Song WW. A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization. ENTROPY (BASEL, SWITZERLAND) 2021; 23:874. [PMID: 34356415 PMCID: PMC8304592 DOI: 10.3390/e23070874] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/25/2021] [Accepted: 06/30/2021] [Indexed: 11/21/2022]
Abstract
Over previous decades, many nature-inspired optimization algorithms (NIOAs) have been proposed and applied due to their importance and significance. Some survey studies have also been made to investigate NIOAs and their variants and applications. However, these comparative studies mainly focus on one single NIOA, and there lacks a comprehensive comparative and contrastive study of the existing NIOAs. To fill this gap, we spent a great effort to conduct this comprehensive survey. In this survey, more than 120 meta-heuristic algorithms have been collected and, among them, the most popular and common 11 NIOAs are selected. Their accuracy, stability, efficiency and parameter sensitivity are evaluated based on the 30 black-box optimization benchmarking (BBOB) functions. Furthermore, we apply the Friedman test and Nemenyi test to analyze the performance of the compared NIOAs. In this survey, we provide a unified formal description of the 11 NIOAs in order to compare their similarities and differences in depth and a systematic summarization of the challenging problems and research directions for the whole NIOAs field. This comparative study attempts to provide a broader perspective and meaningful enlightenment to understand NIOAs.
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Affiliation(s)
- Zhenwu Wang
- Department of Computer Science and Technology, China University of Mining and Technology, Beijing 100083, China;
| | - Chao Qin
- Department of Computer Science and Technology, China University of Mining and Technology, Beijing 100083, China;
| | - Benting Wan
- School of Software and IoT Engineering, Jiangxi University of Finance & Economics, Nanchang 330013, China;
| | - William Wei Song
- School of Software and IoT Engineering, Jiangxi University of Finance & Economics, Nanchang 330013, China;
- Department of Information Systems, Dalarna University, S-791 88 Falun, Sweden
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Agrawal P, Ganesh T, Mohamed AW. Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00351-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractThis article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.
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Gholami J, Pourpanah F, Wang X. Feature selection based on improved binary global harmony search for data classification. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106402] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Ayala HVH, Habineza D, Rakotondrabe M, dos Santos Coelho L. Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.105990] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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10
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Zhu Q, Tang X, Li Y, Yeboah MO. An improved differential-based harmony search algorithm with linear dynamic domain. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.06.017] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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11
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Shehab M, Abualigah L, Al Hamad H, Alabool H, Alshinwan M, Khasawneh AM. Moth–flame optimization algorithm: variants and applications. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04570-6] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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12
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Tsakirakis E, Marinaki M, Marinakis Y, Matsatsinis N. A similarity hybrid harmony search algorithm for the Team Orienteering Problem. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.04.038] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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14
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Luo K, Ma J, Zhao Q. Enhanced self-adaptive global-best harmony search without any extra statistic and external archive. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.01.019] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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15
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Abu Doush I, Quran AL, Al-Betar MA, Awadallah MA. MAX-SAT Problem using Hybrid Harmony Search Algorithm. JOURNAL OF INTELLIGENT SYSTEMS 2018. [DOI: 10.1515/jisys-2016-0129] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Maximum Satisfiability problem is an optimization variant of the Satisfiability problem (SAT) denoted as MAX-SAT. The aim of this problem is to find Boolean variable assignment that maximizes the number of satisfied clauses in the Boolean formula. In case the number of variables per clause is equal or greater than three, then this problem is considered NP-complete. Hence, many researchers have developed techniques to deal with MAX-SAT. In this paper, we investigate the impact of different hybrid versions of binary harmony search (HS) algorithm on solving MAX 3-SAT problem. Therefore, we propose two novel hybrid binary HS algorithms. The first hybridizes Flip heuristic with HS, and the second uses Tabu search combined with Flip heuristic. Furthermore, a distinguished feature of our proposed approaches is using an objective function that is updated dynamically based on the stepwise adaptation of weights (SAW) mechanism to evaluate the MAX-SAT solution using the proposed hybrid versions. The performance of the proposed approaches is evaluated over standard MAX-SAT benchmarks, and the results are compared with six evolutionary algorithms and three stochastic local search algorithms. The obtained results are competitive and show that the proposed novel approaches are effective.
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Sadollah A, Sayyaadi H, Yoo DG, Lee HM, Kim JH. Mine blast harmony search: A new hybrid optimization method for improving exploration and exploitation capabilities. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.04.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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17
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Lu S, Liu X, Pei J, T. Thai M, M. Pardalos P. A hybrid ABC-TS algorithm for the unrelated parallel-batching machines scheduling problem with deteriorating jobs and maintenance activity. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.02.018] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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18
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Lai X, Hao JK, Glover F, Lü Z. A two-phase tabu-evolutionary algorithm for the 0–1 multidimensional knapsack problem. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.01.026] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Wang L, Hu H, Liu R, Zhou X. An improved differential harmony search algorithm for function optimization problems. Soft comput 2018. [DOI: 10.1007/s00500-018-3139-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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20
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Gao C, Chen S, Li X, Huang J, Zhang Z. A Physarum-inspired optimization algorithm for load-shedding problem. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.07.043] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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21
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A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.04.050] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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22
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Rizk-Allah RM, Hassanien AE. New binary bat algorithm for solving 0–1 knapsack problem. COMPLEX INTELL SYST 2017. [DOI: 10.1007/s40747-017-0050-z] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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23
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Tuo S, Yong L, Deng F, Li Y, Lin Y, Lu Q. HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems. PLoS One 2017; 12:e0175114. [PMID: 28403224 PMCID: PMC5389630 DOI: 10.1371/journal.pone.0175114] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2016] [Accepted: 03/21/2017] [Indexed: 11/18/2022] Open
Abstract
Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application.
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Affiliation(s)
- Shouheng Tuo
- School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, P.R. China
- * E-mail:
| | - Longquan Yong
- School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, P.R. China
| | - Fang’an Deng
- School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, P.R. China
| | - Yanhai Li
- School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, P.R. China
| | - Yong Lin
- School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, P.R. China
| | - Qiuju Lu
- School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, P.R. China
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An improved fruit fly optimization algorithm for solving the multidimensional knapsack problem. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2016.11.023] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Maroosi A, Muniyandi RC, Sundararajan E, Zin AM. A parallel membrane inspired harmony search for optimization problems: A case study based on a flexible job shop scheduling problem. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.08.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Guo Z, Yang H, Wang S, Zhou C, Liu X. Adaptive harmony search with best-based search strategy. Soft comput 2016. [DOI: 10.1007/s00500-016-2424-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Banitalebi A, Aziz MIA, Aziz ZA. A self-adaptive binary differential evolution algorithm for large scale binary optimization problems. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.05.037] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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28
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Ouaddah A, Boughaci D. Harmony search algorithm for image reconstruction from projections. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.02.031] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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29
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Bhattacharjee KK, Sarmah SP. Modified swarm intelligence based techniques for the knapsack problem. APPL INTELL 2016. [DOI: 10.1007/s10489-016-0822-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Zhang X, Wu C, Li J, Wang X, Yang Z, Lee JM, Jung KH. Binary artificial algae algorithm for multidimensional knapsack problems. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.02.027] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Huang G. Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization algorithm. SWARM AND EVOLUTIONARY COMPUTATION 2016; 27:31-67. [PMID: 32288989 PMCID: PMC7104270 DOI: 10.1016/j.swevo.2015.09.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Revised: 08/05/2015] [Accepted: 09/21/2015] [Indexed: 05/04/2023]
Abstract
To solve some complicated function optimization problems, an artificial infectious disease optimization algorithm based on the SEIQR epidemic model is constructed, it is called as the SEIQR algorithm, or SEIQRA in short. The algorithm supposes that some human individuals exist in an ecosystem; each individual is characterized by a number of features; an infectious disease (SARS) exists in the ecosystem and spreads among individuals, the disease attacks only a part of features of an individual. Each infected individual may pass through such states as susceptibility (S), exposure (E), infection (I), quarantine (Q) and recovery (R). State S, E, I, Q and R can automatically and dynamically divide all people in the ecosystem into five classes, it provides the diversity for SEIQRA; that people can be attacked by the infectious disease and then transfer it to other people can cause information exchange among people, information exchange can make a person to transit from one state to another; state transitions can be transformed into operators of SEIQRA; the algorithm has 13 legal state transitions, which corresponds to 13 operators; the transmission rules of the infectious disease among people is just the logic to control state transitions of individuals among S, E, I, Q and R, it is just the synergy of SEIQRA, the synergy can be transformed into the logic structure of the algorithm. The 13 operators in the algorithm provide a native opportunity to integrate many operations with different purposes; these operations include average, differential, expansion, chevy, reflection and crossover. The 13 operators are executed equi-probably; a stable heart rhythm of the algorithm is realized. Because the infectious disease can only attack a small part of organs of a person when it spreads among people, the part variables iteration strategy (PVI) can be ingeniously applied, thus enabling the algorithm to possess of high performance of computation, high suitability for solving some kinds of complicated optimization problems, especially high dimensional optimization problems. Results show that SEIQRA has characteristics of strong search capability and global convergence, and has a high convergence speed for some complicated functions optimization problems.
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Affiliation(s)
- Guangqiu Huang
- School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China
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34
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35
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Wang L, Yang R, Ni H, Ye W, Fei M, Pardalos PM. A human learning optimization algorithm and its application to multi-dimensional knapsack problems. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.06.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Zhang B, Pan QK, Zhang XL, Duan PY. An effective hybrid harmony search-based algorithm for solving multidimensional knapsack problems. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.01.022] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
37
|
|
38
|
A multi-population harmony search algorithm with external archive for dynamic optimization problems. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2014.02.084] [Citation(s) in RCA: 83] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
39
|
Huang YF, Lin SM, Wu HY, Li YS. Music genre classification based on local feature selection using a self-adaptive harmony search algorithm. DATA KNOWL ENG 2014. [DOI: 10.1016/j.datak.2014.07.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
40
|
Shreem SS, Abdullah S, Nazri MZA. Hybridising harmony search with a Markov blanket for gene selection problems. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2013.10.012] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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