IWOA: An improved whale optimization algorithm for optimization problems
Graphical abstract Graphical Abstract AbstractThe whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. In order to overco...
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| Published in: | Journal of computational design and engineering Vol. 6; no. 3; pp. 243 - 259 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Oxford University Press
01.07.2019
한국CDE학회 |
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| ISSN: | 2288-5048, 2288-4300, 2288-5048 |
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| Abstract | Graphical abstract
Graphical Abstract
AbstractThe whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. In order to overcome this limitation of WOA, in this paper WOA is hybridized with differential evolution (DE) which has good exploration ability for function optimization problems. The proposed method is named Improved WOA (IWOA). The proposed method, combines exploitation of WOA with exploration of DE and therefore provides a promising candidate solution. In addition, IWOA+ is presented in this paper which is an extended form of IWOA. IWOA+ utilizes re-initialization and adaptive parameter which controls the whole search process to obtain better solutions. IWOA and IWOA+ are validated on a set of 25 benchmark functions, and they are compared with PSO, DE, BBO, DE/BBO, PSO/GSA, SCA, MFO and WOA. Furthermore, the effects of dimensionality and population size on the performance of our proposed algorithms are studied. The results demonstrate that IWOA and IWOA+ outperform the other algorithms in terms of quality of the final solution and convergence rate.
Highlights
The exploration ability of WOA is improved via hybridizing it with DE's mutation.A new adaptive strategy is utilized for balancing the exploration and exploitation abilities.Re-initialization is used to increase the diversity of population.Two improvements are presented for WOA through balancing its exploration and exploitation.The results show that the proposed algorithms can improve the performance of WOA significantly. |
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| AbstractList | The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is pre-sented based on the social hunting behavior of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. In order to overcome this limitation of WOA, in this paper WOA is hybridized with differential evolution (DE) which has good exploration ability for function optimization problems. The proposed method is named Improved WOA (IWOA). The proposed method, combines exploitation of WOA with exploration of DE and therefore provides a promising candidate solution. In addition, IWOA+ is presented in this paper which is an extended form of IWOA. IWOA+ utilizes re-initialization and adaptive parameter which controls the whole search process to obtain better solutions. IWOA and IWOA+ are validated on a set of 25 benchmark functions, and they are compared with PSO, DE, BBO, DE/BBO, PSO/GSA, SCA, MFO and WOA. Furthermore, the effects of dimensionality and population size on the performance of our proposed algorithms are studied. The results demonstrate that IWOA and IWOA+ outperform the other algorithms in terms of quality of the final solution and convergence rate. KCI Citation Count: 52 Graphical abstract Graphical Abstract AbstractThe whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. In order to overcome this limitation of WOA, in this paper WOA is hybridized with differential evolution (DE) which has good exploration ability for function optimization problems. The proposed method is named Improved WOA (IWOA). The proposed method, combines exploitation of WOA with exploration of DE and therefore provides a promising candidate solution. In addition, IWOA+ is presented in this paper which is an extended form of IWOA. IWOA+ utilizes re-initialization and adaptive parameter which controls the whole search process to obtain better solutions. IWOA and IWOA+ are validated on a set of 25 benchmark functions, and they are compared with PSO, DE, BBO, DE/BBO, PSO/GSA, SCA, MFO and WOA. Furthermore, the effects of dimensionality and population size on the performance of our proposed algorithms are studied. The results demonstrate that IWOA and IWOA+ outperform the other algorithms in terms of quality of the final solution and convergence rate. Highlights The exploration ability of WOA is improved via hybridizing it with DE's mutation.A new adaptive strategy is utilized for balancing the exploration and exploitation abilities.Re-initialization is used to increase the diversity of population.Two improvements are presented for WOA through balancing its exploration and exploitation.The results show that the proposed algorithms can improve the performance of WOA significantly. The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. In order to overcome this limitation of WOA, in this paper WOA is hybridized with differential evolution (DE) which has good exploration ability for function optimization problems. The proposed method is named Improved WOA (IWOA). The proposed method, combines exploitation of WOA with exploration of DE and therefore provides a promising candidate solution. In addition, IWOA+ is presented in this paper which is an extended form of IWOA. IWOA+ utilizes re-initialization and adaptive parameter which controls the whole search process to obtain better solutions. IWOA and IWOA+ are validated on a set of 25 benchmark functions, and they are compared with PSO, DE, BBO, DE/BBO, PSO/GSA, SCA, MFO and WOA. Furthermore, the effects of dimensionality and population size on the performance of our proposed algorithms are studied. The results demonstrate that IWOA and IWOA+ outperform the other algorithms in terms of quality of the final solution and convergence rate. Highlights The exploration ability of WOA is improved via hybridizing it with DE's mutation. A new adaptive strategy is utilized for balancing the exploration and exploitation abilities. Re-initialization is used to increase the diversity of population. Two improvements are presented for WOA through balancing its exploration and exploitation. The results show that the proposed algorithms can improve the performance of WOA significantly. |
| Author | Mostafa Bozorgi, Seyed Yazdani, Samaneh |
| Author_xml | – sequence: 1 givenname: Seyed surname: Mostafa Bozorgi fullname: Mostafa Bozorgi, Seyed email: s.m.bozorgi@iau-tnb.ac.ir organization: Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran – sequence: 2 givenname: Samaneh surname: Yazdani fullname: Yazdani, Samaneh email: s_yazdani@iau-tnb.ac.ir organization: Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran |
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| Cites_doi | 10.1109/4235.985692 10.1016/j.swevo.2011.02.002 10.1109/TEVC.2004.830335 10.1016/j.enconman.2010.01.014 10.1007/s00500-010-0591-1 10.1016/j.neucom.2017.10.037 10.1016/j.knosys.2015.12.022 10.1016/j.asoc.2018.07.055 10.1109/ICMIC.2016.7804267 10.1155/2012/561406 10.1016/j.ast.2017.06.026 10.1016/0378-4754(87)90065-6 10.1109/TEVC.2008.919004 10.1109/4235.771163 10.1002/9780470512517 10.1109/ICCIA.2010.6141614 10.1016/j.advengsoft.2016.01.008 10.1109/NABIC.2009.5393690 10.1504/IJMMNO.2013.055204 10.1016/j.knosys.2015.07.006 10.1016/j.eswa.2017.04.023 10.1016/j.eswa.2018.08.027 10.1016/j.asoc.2014.12.028 10.1016/j.epsr.2017.09.001 10.1016/j.cnsns.2012.05.010 10.1016/j.swevo.2017.05.001 10.1016/j.engappai.2013.11.003 10.1109/ACCESS.2017.2695498 10.1016/j.jcde.2017.12.006 10.1016/j.advengsoft.2013.12.007 10.1061/(ASCE)CP.1943-5487.0000163 |
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| Keywords | Differential evolution Swarm intelligence Meta-heuristic algorithm Whale optimization algorithm Optimization |
| Language | English |
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| References | Ghasemi (2020071700170525200_b0075) 2014; 29 Ling (2020071700170525200_b0110) 2017; 5 Mirjalili (2020071700170525200_b0125) 2015; 89 Brest (2020071700170525200_b0025) 2006 Findler (2020071700170525200_b0065) 1987; 29 Yao (2020071700170525200_b0170) 1999; 3 Gandomi (2020071700170525200_b0070) 2012; 17 Wu (2020071700170525200_b0160) 2018; 275 Gong (2020071700170525200_b0080) 2011; 15 Kaur (2020071700170525200_b0100) 2018 Simon (2020071700170525200_b0150) 2008 Hadavandi (2020071700170525200_b0085) 2018; 72 Mirjalili (2020071700170525200_b0130) 2016; 96 Clerc (2020071700170525200_b0035) 2002; 6 Eberhart (2020071700170525200_b0050) 2004; 8 Mafarja (2020071700170525200_b0120) 2017 Sun (2020071700170525200_b0155) 2018; 114 Aziz (2020071700170525200_b0010) 2017; 83 El-abd (2020071700170525200_b0055) 2017; 37 Engelbrecht (2020071700170525200_b0060) 2007 Yang (2020071700170525200_b0165) 2009 Bentouati (2020071700170525200_b0020) 2016 Lucas (2020071700170525200_b0115) 2010; 51 Cheng (2020071700170525200_b0030) 2012; 26 Aljarah (2020071700170525200_b0005) 2018 Derrac (2020071700170525200_b0045) 2011; 1 Mirjalili (2020071700170525200_b0140) 2016; 95 ben oualid Medani (2020071700170525200_b0015) 2018; 163 Das (2020071700170525200_b0040) 2008; 94 Lin (2020071700170525200_b0105) 2012; 2012 Mirjalili (2020071700170525200_b0135) 2010 Hafezi (2020071700170525200_b0090) 2015; 29 Mirjalili (2020071700170525200_b0145) 2014; 69 Yu (2020071700170525200_b0175) 2017; 69 Jamil (2020071700170525200_b0095) 2013; 4 |
| References_xml | – volume: 6 start-page: 58 issue: 1 year: 2002 ident: 2020071700170525200_b0035 article-title: The particle swarm - Explosion, stability, and convergence in a multidimensional complex space publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.985692 – volume: 1 start-page: 3 issue: 1 year: 2011 ident: 2020071700170525200_b0045 article-title: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2011.02.002 – volume: 8 start-page: 201 issue: 3 year: 2004 ident: 2020071700170525200_b0050 article-title: Guest editorial special issue on particle swarm optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2004.830335 – volume: 51 start-page: 1407 issue: 7 year: 2010 ident: 2020071700170525200_b0115 article-title: Application of an imperialist competitive algorithm to the design of a linear induction motor publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2010.01.014 – volume: 15 start-page: 645 issue: 4 year: 2011 ident: 2020071700170525200_b0080 article-title: DE/BBO: A hybrid differential evolution with biogeography-based optimization for global numerical optimization publication-title: Soft Computing doi: 10.1007/s00500-010-0591-1 – volume: 275 start-page: 2055 year: 2018 ident: 2020071700170525200_b0160 article-title: Path planning for solar-powered UAV in urban environment publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.10.037 – volume: 96 start-page: 120 year: 2016 ident: 2020071700170525200_b0130 article-title: SCA: A Sine Cosine Algorithm for solving optimization problems publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2015.12.022 – volume: 72 start-page: 1 year: 2018 ident: 2020071700170525200_b0085 article-title: A Grey Wolf Optimizer-based neural network coupled with response surface method for modeling the strength of siro-spun yarn in spinning mills publication-title: Applied Soft Computing Journal doi: 10.1016/j.asoc.2018.07.055 – volume-title: Soft Computing year: 2018 ident: 2020071700170525200_b0005 article-title: Optimizing connection weights in neural networks using the whale optimization algorithm – start-page: 1048 volume-title: 2016 8th international conference on modelling, identification and control (ICMIC) year: 2016 ident: 2020071700170525200_b0020 doi: 10.1109/ICMIC.2016.7804267 – volume: 2012 start-page: 561406 year: 2012 ident: 2020071700170525200_b0105 article-title: Hybrid particle swarm optimization and its application to multimodal 3D medical image registration publication-title: Computational Intelligence and Neuroscience doi: 10.1155/2012/561406 – volume: 69 start-page: 149 year: 2017 ident: 2020071700170525200_b0175 article-title: Automatic carrier landing system based on active disturbance rejection control with a novel parameters optimizer publication-title: Aerospace Science and Technology doi: 10.1016/j.ast.2017.06.026 – volume: 29 start-page: 41 issue: 1 year: 1987 ident: 2020071700170525200_b0065 article-title: Pattern search for optimization publication-title: Mathematics and Computers in Simulation doi: 10.1016/0378-4754(87)90065-6 – start-page: 215 volume-title: 2006 IEEE int. conf. evol. comput. year: 2006 ident: 2020071700170525200_b0025 article-title: Self-adaptive differential evolution algorithm in constrained real-parameter optimization – volume-title: IEEE Transactions on Evolutionary Computation year: 2008 ident: 2020071700170525200_b0150 article-title: Biogeography-based optimization doi: 10.1109/TEVC.2008.919004 – volume: 3 start-page: 82 issue: 2 year: 1999 ident: 2020071700170525200_b0170 article-title: Evolutionary programming made faster publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.771163 – volume-title: Computational intelligence: An introduction year: 2007 ident: 2020071700170525200_b0060 doi: 10.1002/9780470512517 – start-page: 374 volume-title: Proceedings of ICCIA 2010 - 2010 international conference on computer and information application year: 2010 ident: 2020071700170525200_b0135 article-title: A new hybrid PSOGSA algorithm for function optimization doi: 10.1109/ICCIA.2010.6141614 – volume: 95 start-page: 51 year: 2016 ident: 2020071700170525200_b0140 article-title: The whale optimization algorithm publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2016.01.008 – start-page: 214 volume-title: 2009 World congress on nature & biologically inspired computing (NaBIC) year: 2009 ident: 2020071700170525200_b0165 article-title: Cuckoo search via levy flights doi: 10.1109/NABIC.2009.5393690 – volume: 4 start-page: 150 issue: 2 year: 2013 ident: 2020071700170525200_b0095 article-title: A literature survey of benchmark functions for global optimization problems publication-title: International Journal of Mathematical Modelling and Numerical Optimisation doi: 10.1504/IJMMNO.2013.055204 – volume: 89 start-page: 228 year: 2015 ident: 2020071700170525200_b0125 article-title: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2015.07.006 – volume: 83 start-page: 242 year: 2017 ident: 2020071700170525200_b0010 article-title: Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.04.023 – volume: 114 start-page: 563 year: 2018 ident: 2020071700170525200_b0155 article-title: A modified whale optimization algorithm for large-scale global optimization problems publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2018.08.027 – volume: 29 start-page: 196 year: 2015 ident: 2020071700170525200_b0090 article-title: A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price publication-title: Applied Soft Computing Journal doi: 10.1016/j.asoc.2014.12.028 – volume: 163 start-page: 696 year: 2018 ident: 2020071700170525200_b0015 article-title: Whale optimization algorithm based optimal reactive power dispatch: A case study of the Algerian power system publication-title: Electric Power Systems Research doi: 10.1016/j.epsr.2017.09.001 – volume: 17 start-page: 4831 issue: 12 year: 2012 ident: 2020071700170525200_b0070 article-title: Krill herd: A new bio-inspired optimization algorithm publication-title: Communications in Nonlinear Science and Numerical Simulation doi: 10.1016/j.cnsns.2012.05.010 – volume: 37 start-page: 27 year: 2017 ident: 2020071700170525200_b0055 article-title: Global-best brain storm optimization algorithm publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2017.05.001 – volume: 29 start-page: 54 year: 2014 ident: 2020071700170525200_b0075 article-title: A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2013.11.003 – volume: 5 start-page: 6168 year: 2017 ident: 2020071700170525200_b0110 article-title: Lévy flight trajectory-based whale optimization algorithm for global optimization publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2695498 – volume-title: Journal of Computational Design and Engineering year: 2018 ident: 2020071700170525200_b0100 article-title: Chaotic whale optimization algorithm doi: 10.1016/j.jcde.2017.12.006 – volume: 69 start-page: 46 year: 2014 ident: 2020071700170525200_b0145 article-title: Grey wolf optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2013.12.007 – volume: 26 start-page: 612 year: 2012 ident: 2020071700170525200_b0030 article-title: Hybrid artificial intelligence-based PBA for benchmark functions and facility layout design optimization publication-title: Journal of Computing in Civil Engineering doi: 10.1061/(ASCE)CP.1943-5487.0000163 – volume: 94 start-page: 113 issue: 2008 year: 2008 ident: 2020071700170525200_b0040 article-title: Swarm intelligence algorithms in bioinformatics publication-title: Studies in Computational Intelligence – start-page: 1 volume-title: Neurocomputing year: 2017 ident: 2020071700170525200_b0120 article-title: Hybrid Whale Optimization Algorithm with simulated annealing for feature selection |
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AbstractThe whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on... The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback... The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is pre-sented based on the social hunting behavior of humpback... |
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| Title | IWOA: An improved whale optimization algorithm for optimization problems |
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