An astrophysics-inspired Grey wolf algorithm for numerical optimization and its application to engineering design problems
•Three novel strategies are proposed which utilize the concepts of astrophysics and prey weight.•The proposed strategies have been tested on thirteen standard benchmark test functions and compared with existing techniques.•The proposed strategies provide better results than the other techniques for...
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| Veröffentlicht in: | Advances in engineering software (1992) Jg. 112; S. 231 - 254 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.10.2017
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| ISSN: | 0965-9978 |
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| Abstract | •Three novel strategies are proposed which utilize the concepts of astrophysics and prey weight.•The proposed strategies have been tested on thirteen standard benchmark test functions and compared with existing techniques.•The proposed strategies provide better results than the other techniques for noisy and scalable environment.•The effect of control parameter has also been investigated on the proposed strategies.•The proposed strategies have been evaluated on seven well-known constrained engineering design problems.
In this paper, modified schemes are proposed for preventing a grey wolf optimizer (GWO) from premature exploration and convergence on optimization problems. Three novel strategies are developed to improve the performance of existing GWO. The first strategy uses the concept of prey weight. The second strategy uses the astrophysics concepts, which guide the grey wolves toward more promising areas of the search space. The beauty of this strategy is to let each grey wolf learn from not only movement of sun (symbolizes prey) in the search space but also the wolves are made to explore and exploit simultaneously. Third strategy combines the both, first and second strategies to take advantages of prey weight and astrophysics strategies. The proposed improvements in GWO have been evaluated on thirteen benchmark test functions. The performance of the proposed modifications has been compared with other five recently developed state-of-the-art techniques. The effects of scalability, noise, and control parameter have also been investigated. The statistical tests have been performed to validate the significance of modified variants. The proposed variants are also applied for seven well-known constrained engineering design problems. The experimental results depict the supremacy of the proposed modified algorithm in solving engineering design problems when compared with several existing techniques. |
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| AbstractList | •Three novel strategies are proposed which utilize the concepts of astrophysics and prey weight.•The proposed strategies have been tested on thirteen standard benchmark test functions and compared with existing techniques.•The proposed strategies provide better results than the other techniques for noisy and scalable environment.•The effect of control parameter has also been investigated on the proposed strategies.•The proposed strategies have been evaluated on seven well-known constrained engineering design problems.
In this paper, modified schemes are proposed for preventing a grey wolf optimizer (GWO) from premature exploration and convergence on optimization problems. Three novel strategies are developed to improve the performance of existing GWO. The first strategy uses the concept of prey weight. The second strategy uses the astrophysics concepts, which guide the grey wolves toward more promising areas of the search space. The beauty of this strategy is to let each grey wolf learn from not only movement of sun (symbolizes prey) in the search space but also the wolves are made to explore and exploit simultaneously. Third strategy combines the both, first and second strategies to take advantages of prey weight and astrophysics strategies. The proposed improvements in GWO have been evaluated on thirteen benchmark test functions. The performance of the proposed modifications has been compared with other five recently developed state-of-the-art techniques. The effects of scalability, noise, and control parameter have also been investigated. The statistical tests have been performed to validate the significance of modified variants. The proposed variants are also applied for seven well-known constrained engineering design problems. The experimental results depict the supremacy of the proposed modified algorithm in solving engineering design problems when compared with several existing techniques. |
| Author | Kumar, Dinesh Kumar, Vijay |
| Author_xml | – sequence: 1 givenname: Vijay surname: Kumar fullname: Kumar, Vijay email: vijaykumarchahar@gmail.com, vijay_kumar@thapar.edu organization: Computer Science and Engineering Department, Thapar University, Patiala, Punjab, India – sequence: 2 givenname: Dinesh surname: Kumar fullname: Kumar, Dinesh email: dinesh_chutani@yahoo.com organization: Computer Science and Engineering Department, GJUS&T, Hisar, Haryana, India |
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| Cites_doi | 10.1016/j.ins.2009.03.004 10.1109/4235.771163 10.1016/j.knosys.2015.12.022 10.1007/s00521-015-1870-7 10.1080/01969722.2014.929349 10.1016/j.knosys.2015.07.006 10.1109/MCI.2006.329691 10.1016/j.advengsoft.2013.12.007 10.1007/s00521-014-1806-7 10.1109/ICITEED.2015.7408911 10.1080/15325008.2015.1041625 10.1115/1.2919393 10.1016/j.neucom.2015.06.083 10.1109/JSEE.2015.00037 10.1109/TEVC.2003.814902 10.1016/j.asoc.2012.11.026 10.1155/2016/7950348 10.1016/j.compstruc.2012.07.010 |
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| Keywords | Astrophysics concept Constrained design problems Grey wolf optimizer Function optimization Meta-heuristics |
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| References | Mirjalili, Mirjalili, Hatamlou (bib0018) 2016; 27 Kumar, Chhabra, Kumar (bib0015) 2014; 45 Sadollah, Bahreininejad, Eskandar, Hamdi (bib0023) 2013; 13 Jitkongchuen, D., 2015, A Hybrid differential evolution with grey wolf optimizer for continuous global optimization, International Conference on Information Technology and Electrical Engineering, pp. 51–54, Chiang Mai. Rashedi, Nezamabadi-pour, Saryazdi (bib0021) 2009; 179 Ray, Liew (bib0022) 2003; 7 Wen, Shao-Hong, Jian-Jun, Wen-Zhuan, Ming-Zhu (bib0026) 2016; 31 Mirjalili (bib0017) 2015; 89 Yao, Liu, Lin (bib0028) 1999; 3 Arora (bib0001) 1989 Korayem, Khorsid, Kassem (bib0013) 2015; 83 Emary, Zawbaa, Grosan (bib0005) 2017 Goldberg (bib0007) 1989 Zhu, Xu, Li, Wu, Liu (bib0030) 2015; 26 Kishor, Singh (bib0012) 2016 Mittal, Singh, Sohi (bib0020) 2016 Dorigo, Birattari, Stutzle (bib0002) 2006; 1 Mirjalili (bib0019) 2016; 96 Emary, Zawbaa, Hassanien (bib0004) 2016; 172 Kamboj (bib0009) 2015 Kannan, Kramer (bib0010) 1994; 116 El-Fergany, Hasanien (bib0003) 2015; 43 Eskandar, Sadollah, Bahreininejad, Hamdi (bib0006) 2012; 110 Zhang, Zhou (bib0029) 2015 Kennedy, Eberhart (bib0011) 1995; 4 Wen (bib0027) 2016 Sarafrazi, Nezamaadi-pour, Seydnejad (bib0025) 2015; 27 Mirjalili, Mirjalili, Lewis (bib0016) 2014; 69 Saremi, Mirjalili, Mirjalili (bib0024) 2015; 26 Kishor (10.1016/j.advengsoft.2017.05.008_bib0012) 2016 Wen (10.1016/j.advengsoft.2017.05.008_bib0027) 2016 Mirjalili (10.1016/j.advengsoft.2017.05.008_bib0019) 2016; 96 Zhu (10.1016/j.advengsoft.2017.05.008_bib0030) 2015; 26 Zhang (10.1016/j.advengsoft.2017.05.008_bib0029) 2015 Sarafrazi (10.1016/j.advengsoft.2017.05.008_bib0025) 2015; 27 10.1016/j.advengsoft.2017.05.008_bib0008 Mirjalili (10.1016/j.advengsoft.2017.05.008_bib0017) 2015; 89 Goldberg (10.1016/j.advengsoft.2017.05.008_bib0007) 1989 Arora (10.1016/j.advengsoft.2017.05.008_bib0001) 1989 Emary (10.1016/j.advengsoft.2017.05.008_bib0004) 2016; 172 Emary (10.1016/j.advengsoft.2017.05.008_bib0005) 2017 Kannan (10.1016/j.advengsoft.2017.05.008_bib0010) 1994; 116 Yao (10.1016/j.advengsoft.2017.05.008_bib0028) 1999; 3 Eskandar (10.1016/j.advengsoft.2017.05.008_bib0006) 2012; 110 Mirjalili (10.1016/j.advengsoft.2017.05.008_bib0018) 2016; 27 Dorigo (10.1016/j.advengsoft.2017.05.008_bib0002) 2006; 1 Mittal (10.1016/j.advengsoft.2017.05.008_bib0020) 2016 Korayem (10.1016/j.advengsoft.2017.05.008_bib0013) 2015; 83 Kumar (10.1016/j.advengsoft.2017.05.008_bib0015) 2014; 45 Kamboj (10.1016/j.advengsoft.2017.05.008_bib0009) 2015 Rashedi (10.1016/j.advengsoft.2017.05.008_bib0021) 2009; 179 El-Fergany (10.1016/j.advengsoft.2017.05.008_bib0003) 2015; 43 Sadollah (10.1016/j.advengsoft.2017.05.008_bib0023) 2013; 13 Wen (10.1016/j.advengsoft.2017.05.008_bib0026) 2016; 31 Mirjalili (10.1016/j.advengsoft.2017.05.008_bib0016) 2014; 69 Kennedy (10.1016/j.advengsoft.2017.05.008_bib0011) 1995; 4 Saremi (10.1016/j.advengsoft.2017.05.008_bib0024) 2015; 26 Ray (10.1016/j.advengsoft.2017.05.008_bib0022) 2003; 7 |
| References_xml | – volume: 43 start-page: 1548 year: 2015 end-page: 1559 ident: bib0003 article-title: Single and multi-objective optimal power flow using grey wolf optimizer and differential evolution algorithms publication-title: Electr Power Compon Syst – volume: 45 start-page: 486 year: 2014 end-page: 511 ident: bib0015 article-title: Variance-based harmony search algorithm for unimodal and multimodal optimization problems with application to clustering publication-title: Cybern Syst – volume: 83 start-page: 1 year: 2015 end-page: 10 ident: bib0013 article-title: Using grey wolf algorithm to solve the capacitated vehicle routing problem publication-title: Mater Sci Eng – year: 1989 ident: bib0001 article-title: Introduction to optimum design – reference: Jitkongchuen, D., 2015, A Hybrid differential evolution with grey wolf optimizer for continuous global optimization, International Conference on Information Technology and Electrical Engineering, pp. 51–54, Chiang Mai. – volume: 110 start-page: 151 year: 2012 end-page: 166 ident: bib0006 article-title: Water cycle algorithm: a novel metaheuristic method for solving constrained engineering optimization problems publication-title: Comput Struct – start-page: 1 year: 2017 end-page: 14 ident: bib0005 article-title: Experienced grey wolf optimization through reinforcement learning and neural networks publication-title: IEEE Trans Neural Netw Learn Syst – volume: 4 start-page: 1942 year: 1995 end-page: 1948 ident: bib0011 article-title: Particle swarm optimization publication-title: IEEE International Conference on Neural Networks – start-page: 1 year: 2016 end-page: 16 ident: bib0020 article-title: Modified grey wolf optimizer for global engineering optimization publication-title: Appl Comput Intell Soft Comput – volume: 27 start-page: 495 year: 2016 end-page: 513 ident: bib0018 article-title: Multi-Verse optimizer: a nature-inspired algorithm for global optimization publication-title: Neural Comput Appl – volume: 31 start-page: 1991 year: 2016 end-page: 1997 ident: bib0026 article-title: Hybrid grey wolf optimization algorithm for high dimensional optimization publication-title: Control Decis – volume: 1 start-page: 28 year: 2006 end-page: 39 ident: bib0002 article-title: Ant colony optimization publication-title: IEEE Comput Intell Mag – volume: 172 start-page: 371 year: 2016 end-page: 381 ident: bib0004 article-title: Binary Grey wolf optimization approaches for feature selection publication-title: Neurocomputing – volume: 179 start-page: 2232 year: 2009 end-page: 2248 ident: bib0021 article-title: GSA: a gravitational search algorithm publication-title: Inf Sci – volume: 3 start-page: 82 year: 1999 end-page: 102 ident: bib0028 article-title: Evolutionary programming made faster publication-title: IEEE Trans Evol Comput – start-page: 1037 year: 2016 end-page: 1049 ident: bib0012 article-title: Empirical study of grey wolf optimizer publication-title: International Conference on Soft Computing for Problem Solving – start-page: 1 year: 2015 end-page: 17 ident: bib0029 article-title: Grey wolf optimizer based on powell local optimization method for clustering analysis publication-title: Discrete Dyn Nature Soc – start-page: 1 year: 2015 end-page: 13 ident: bib0009 article-title: A novel hybrid PSO-GWO approach for unit commitment problem publication-title: Neural Comput Appl – volume: 89 start-page: 228 year: 2015 end-page: 249 ident: bib0017 article-title: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm publication-title: Knowledge-based Syst – volume: 13 start-page: 2592 year: 2013 end-page: 2612 ident: bib0023 article-title: Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems publication-title: Appl Soft Comput – year: 1989 ident: bib0007 article-title: Genetic algorithms in Search, optimization and machine learning – volume: 69 start-page: 46 year: 2014 end-page: 61 ident: bib0016 article-title: Grey wolf optimizer publication-title: Adv Eng Softw – volume: 7 start-page: 386 year: 2003 end-page: 396 ident: bib0022 article-title: Society and civilization: an optimization algorithm based on the simulation of social behavior publication-title: IEEE Trans Evol Comput – start-page: 643 year: 2016 end-page: 648 ident: bib0027 article-title: Grey wolf optimizer based on nonlinear adjustment control parameter publication-title: International Conference on Sensors, Mechatronics and Automation – volume: 116 start-page: 405 year: 1994 end-page: 411 ident: bib0010 article-title: An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design publication-title: J Mech Des. – volume: 96 start-page: 120 year: 2016 end-page: 133 ident: bib0019 article-title: SCA: a sine cosine algorithm for solving optimization problems publication-title: Knowledge-based Syst – volume: 26 start-page: 1257 year: 2015 end-page: 1263 ident: bib0024 article-title: Evolutionary population dynamics and grey wolf optimizer publication-title: Neural Comput Appl – volume: 27 start-page: 288 year: 2015 end-page: 296 ident: bib0025 article-title: A novel hyrid algorithm of GSA with kepler algorithm for numerical optimization publication-title: Comput Inf Sci – volume: 26 start-page: 317 year: 2015 end-page: 328 ident: bib0030 article-title: Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC publication-title: J Syst Eng Electron – volume: 179 start-page: 2232 issue: 13 year: 2009 ident: 10.1016/j.advengsoft.2017.05.008_bib0021 article-title: GSA: a gravitational search algorithm publication-title: Inf Sci doi: 10.1016/j.ins.2009.03.004 – start-page: 1 year: 2015 ident: 10.1016/j.advengsoft.2017.05.008_bib0029 article-title: Grey wolf optimizer based on powell local optimization method for clustering analysis publication-title: Discrete Dyn Nature Soc – volume: 3 start-page: 82 year: 1999 ident: 10.1016/j.advengsoft.2017.05.008_bib0028 article-title: Evolutionary programming made faster publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.771163 – volume: 27 start-page: 288 year: 2015 ident: 10.1016/j.advengsoft.2017.05.008_bib0025 article-title: A novel hyrid algorithm of GSA with kepler algorithm for numerical optimization publication-title: Comput Inf Sci – start-page: 1 year: 2017 ident: 10.1016/j.advengsoft.2017.05.008_bib0005 article-title: Experienced grey wolf optimization through reinforcement learning and neural networks publication-title: IEEE Trans Neural Netw Learn Syst – volume: 96 start-page: 120 year: 2016 ident: 10.1016/j.advengsoft.2017.05.008_bib0019 article-title: SCA: a sine cosine algorithm for solving optimization problems publication-title: Knowledge-based Syst doi: 10.1016/j.knosys.2015.12.022 – start-page: 1037 year: 2016 ident: 10.1016/j.advengsoft.2017.05.008_bib0012 article-title: Empirical study of grey wolf optimizer – volume: 27 start-page: 495 year: 2016 ident: 10.1016/j.advengsoft.2017.05.008_bib0018 article-title: Multi-Verse optimizer: a nature-inspired algorithm for global optimization publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1870-7 – volume: 45 start-page: 486 year: 2014 ident: 10.1016/j.advengsoft.2017.05.008_bib0015 article-title: Variance-based harmony search algorithm for unimodal and multimodal optimization problems with application to clustering publication-title: Cybern Syst doi: 10.1080/01969722.2014.929349 – start-page: 643 year: 2016 ident: 10.1016/j.advengsoft.2017.05.008_bib0027 article-title: Grey wolf optimizer based on nonlinear adjustment control parameter – volume: 89 start-page: 228 year: 2015 ident: 10.1016/j.advengsoft.2017.05.008_bib0017 article-title: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm publication-title: Knowledge-based Syst doi: 10.1016/j.knosys.2015.07.006 – volume: 1 start-page: 28 issue: 4 year: 2006 ident: 10.1016/j.advengsoft.2017.05.008_bib0002 article-title: Ant colony optimization publication-title: IEEE Comput Intell Mag doi: 10.1109/MCI.2006.329691 – volume: 69 start-page: 46 year: 2014 ident: 10.1016/j.advengsoft.2017.05.008_bib0016 article-title: Grey wolf optimizer publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2013.12.007 – start-page: 1 year: 2015 ident: 10.1016/j.advengsoft.2017.05.008_bib0009 article-title: A novel hybrid PSO-GWO approach for unit commitment problem publication-title: Neural Comput Appl – volume: 26 start-page: 1257 year: 2015 ident: 10.1016/j.advengsoft.2017.05.008_bib0024 article-title: Evolutionary population dynamics and grey wolf optimizer publication-title: Neural Comput Appl doi: 10.1007/s00521-014-1806-7 – ident: 10.1016/j.advengsoft.2017.05.008_bib0008 doi: 10.1109/ICITEED.2015.7408911 – volume: 43 start-page: 1548 issue: 13 year: 2015 ident: 10.1016/j.advengsoft.2017.05.008_bib0003 article-title: Single and multi-objective optimal power flow using grey wolf optimizer and differential evolution algorithms publication-title: Electr Power Compon Syst doi: 10.1080/15325008.2015.1041625 – volume: 31 start-page: 1991 issue: 11 year: 2016 ident: 10.1016/j.advengsoft.2017.05.008_bib0026 article-title: Hybrid grey wolf optimization algorithm for high dimensional optimization publication-title: Control Decis – volume: 4 start-page: 1942 year: 1995 ident: 10.1016/j.advengsoft.2017.05.008_bib0011 article-title: Particle swarm optimization – volume: 116 start-page: 405 year: 1994 ident: 10.1016/j.advengsoft.2017.05.008_bib0010 article-title: An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design publication-title: J Mech Des. doi: 10.1115/1.2919393 – year: 1989 ident: 10.1016/j.advengsoft.2017.05.008_bib0001 – volume: 172 start-page: 371 year: 2016 ident: 10.1016/j.advengsoft.2017.05.008_bib0004 article-title: Binary Grey wolf optimization approaches for feature selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.06.083 – year: 1989 ident: 10.1016/j.advengsoft.2017.05.008_bib0007 – volume: 26 start-page: 317 year: 2015 ident: 10.1016/j.advengsoft.2017.05.008_bib0030 article-title: Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC publication-title: J Syst Eng Electron doi: 10.1109/JSEE.2015.00037 – volume: 83 start-page: 1 year: 2015 ident: 10.1016/j.advengsoft.2017.05.008_bib0013 article-title: Using grey wolf algorithm to solve the capacitated vehicle routing problem publication-title: Mater Sci Eng – volume: 7 start-page: 386 year: 2003 ident: 10.1016/j.advengsoft.2017.05.008_bib0022 article-title: Society and civilization: an optimization algorithm based on the simulation of social behavior publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2003.814902 – volume: 13 start-page: 2592 year: 2013 ident: 10.1016/j.advengsoft.2017.05.008_bib0023 article-title: Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2012.11.026 – start-page: 1 year: 2016 ident: 10.1016/j.advengsoft.2017.05.008_bib0020 article-title: Modified grey wolf optimizer for global engineering optimization publication-title: Appl Comput Intell Soft Comput doi: 10.1155/2016/7950348 – volume: 110 start-page: 151 year: 2012 ident: 10.1016/j.advengsoft.2017.05.008_bib0006 article-title: Water cycle algorithm: a novel metaheuristic method for solving constrained engineering optimization problems publication-title: Comput Struct doi: 10.1016/j.compstruc.2012.07.010 |
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| Snippet | •Three novel strategies are proposed which utilize the concepts of astrophysics and prey weight.•The proposed strategies have been tested on thirteen standard... |
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| StartPage | 231 |
| SubjectTerms | Astrophysics concept Constrained design problems Function optimization Grey wolf optimizer Meta-heuristics |
| Title | An astrophysics-inspired Grey wolf algorithm for numerical optimization and its application to engineering design problems |
| URI | https://dx.doi.org/10.1016/j.advengsoft.2017.05.008 |
| Volume | 112 |
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