Chaotic grasshopper optimization algorithm for global optimization
Grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The present study introduces chaos theory into the optimization process of GOA so as to accelerate its global convergence speed. The chaotic maps are employed to balance the...
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| Published in | Neural computing & applications Vol. 31; no. 8; pp. 4385 - 4405 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.08.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0941-0643 1433-3058 |
| DOI | 10.1007/s00521-018-3343-2 |
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| Abstract | Grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The present study introduces chaos theory into the optimization process of GOA so as to accelerate its global convergence speed. The chaotic maps are employed to balance the exploration and exploitation efficiently and the reduction in repulsion/attraction forces between grasshoppers in the optimization process. The proposed chaotic GOA algorithms are benchmarked on thirteen test functions. The results show that the chaotic maps (especially circle map) are able to significantly boost the performance of GOA. |
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| AbstractList | Grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The present study introduces chaos theory into the optimization process of GOA so as to accelerate its global convergence speed. The chaotic maps are employed to balance the exploration and exploitation efficiently and the reduction in repulsion/attraction forces between grasshoppers in the optimization process. The proposed chaotic GOA algorithms are benchmarked on thirteen test functions. The results show that the chaotic maps (especially circle map) are able to significantly boost the performance of GOA. |
| Author | Arora, Sankalap Anand, Priyanka |
| Author_xml | – sequence: 1 givenname: Sankalap surname: Arora fullname: Arora, Sankalap email: sankalap10156@davuniversity.org organization: DAV University – sequence: 2 givenname: Priyanka surname: Anand fullname: Anand, Priyanka organization: Lovely Professional University |
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| Cites_doi | 10.3233/JIFS-16798 10.1023/A:1022602019183 10.1016/j.jmatprotec.2008.06.028 10.1109/ICEC.1996.542373 10.1016/j.ins.2012.04.039 10.1177/003754970107600201 10.1016/j.advengsoft.2013.12.007 10.1016/j.asoc.2014.10.010 10.1007/s00521-014-1597-x 10.1109/4235.771163 10.1109/TPWRS.2006.873410 10.1007/s00521-017-2988-6 10.1023/A:1022995128597 10.1155/2014/924652 10.1016/j.swevo.2011.02.002 10.1162/106365601750190398 10.1109/81.933333 10.1007/s13369-017-2471-9 10.1007/s00521-014-1751-5 10.1016/j.ins.2009.03.004 10.1155/2013/696491 10.1016/j.advengsoft.2017.01.004 10.1016/j.cnsns.2012.07.017 10.1007/s00521-015-2037-2 10.1016/j.ins.2012.06.033 10.1007/s00521-014-1613-1 10.1016/j.cnsns.2011.08.021 10.1016/j.ins.2011.03.018 10.1016/B978-1-55860-335-6.50043-X 10.1023/A:1008202821328 10.1007/s10732-008-9080-4 10.1016/j.chaos.2006.04.057 10.1109/PDGC.2014.7030711 10.1016/j.jcde.2017.02.005 10.1016/0960-0779(94)90033-7 10.1016/j.cnsns.2012.06.009 |
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| References | TavazoeiMSHaeriMComparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithmsAppl Math Comput200718721076108523231141114.65335 Brownlee J (2011) Clever algorithms: nature-inspired programming recipes. 1st edn. Lulu, North Carolina Yang X-S (2010) Firefly algorithm, levy flights and global optimization. In: Research and development in intelligent systems XXVI, pp 209–218 RashediENezamabadi-PourHSaryazdiSGsa: a gravitational search algorithmInf Sci200917913223222481177.9037810.1016/j.ins.2009.03.004 Storn R (1996) Differential evolution design of an IIR filter. In: Proceedings of IEEE international conference on evolutionary computation, pp 268–273 HanXChangXAn intelligent noise reduction method for chaotic signals based on genetic algorithms and lifting wavelet transformsInf Sci201321810311810.1016/j.ins.2012.06.033 Wang G, Guo L (2013) A novel hybrid bat algorithm with harmony search for global numerical optimization. J Appl Math 2013:21. https://doi.org/10.1155/2013/696491 DerracJGarcíaSMolinaDHerreraFA practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithmsSwarm Evol Comput20111131810.1016/j.swevo.2011.02.002 YangDLiGChengGOn the efficiency of chaos optimization algorithms for global optimizationChaos Solitons Fractals200734413661375228678010.1016/j.chaos.2006.04.057 AroraSSinghSNode localization in wireless sensor networks using butterfly optimization algorithmArab J Sci Eng2017423325333510.1007/s13369-017-2471-9 GandomiAYangX-STalatahariSAlaviAFirefly algorithm with chaosCommun Nonlinear Sci Numer Simul2013181899829743651254.9208910.1016/j.cnsns.2012.06.009 dos CoelhoL SantosMarianiVCCombining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effectIEEE Trans Power Syst200621298910.1109/TPWRS.2006.873410 IlonenJKamarainenJ-KLampinenJDifferential evolution training algorithm for feed-forward neural networksNeural Process Lett20031719310510.1023/A:1022995128597 HansenNOstermeierACompletely derandomized self-adaptation in evolution strategiesEvol Comput20019215919510.1162/106365601750190398 GandomiAHYunGJYangX-STalatahariSChaos-enhanced accelerated particle swarm optimizationCommun Nonlinear Sci Numer Simul201318232734029718331323.9008210.1016/j.cnsns.2012.07.017 HeidariAAAbbaspourRAJordehiARAn efficient chaotic water cycle algorithm for optimization tasksNeural Comput Appl2017281578510.1007/s00521-015-2037-2 AroraSSinghSThe firefly optimization algorithm: convergence analysis and parameter selectionInt J Comput Appl20136934852 JordehiARA chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problemsNeural Comput Appl201526482783310.1007/s00521-014-1751-5 Skalak DB (1994) Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: Proceedings of the eleventh international conference on machine learning, pp 293–301 JordehiARChaotic bat swarm optimisation (cbso)Appl Soft Comput20152652353010.1016/j.asoc.2014.10.010 Arora S, Singh S, Singh S, Sharma B (2014) Mutated firefly algorithm. In: International conference on parallel, distributed and grid computing (PDGC), pp 33–38 GarcíaSMolinaDLozanoMHerreraFA study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the cec2005 special session on real parameter optimizationJ Heuristics20091566176441191.6882810.1007/s10732-008-9080-4 Eberhart RC, Kennedy J et al (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, New York, vol 1, pp 39–43 SaremiSMirjaliliSLewisAGrasshopper optimisation algorithm: theory and applicationAdv Eng Softw2017105304710.1016/j.advengsoft.2017.01.004 Arora S, Singh S (2015) Butterfly algorithm with levy flights for global optimization. In: International conference on signal processing, computing and control (ISPCC), pp 220–224 Lu H, Wang X, Fei Z, Qiu M (2014) The effects of using chaotic map on improving the performance of multiobjective evolutionary algorithms. Math Prob Eng 2014:16. https://doi.org/10.1155/2014/924652 WilcoxonFKattiSWilcoxRACritical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank testSel Tables Math Stat197011712590121.36201 SaremiSMirjaliliSLewisABiogeography-based optimisation with chaosNeural Comput Appl20142551077109710.1007/s00521-014-1597-x Kohli M, Arora S (2017) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Design Eng. https://doi.org/10.1016/j.jcde.2017.02.005 HeDHeCJiangL-GZhuH-WHuG-RChaotic characteristics of a one-dimensional iterative map with infinite collapsesIEEE Trans Circuits Syst I Fundam Theory Appl200148790090618410050993.3703310.1109/81.933333 Yang X-S, Gandomi AH, Talatahari S, Alavi AH (eds) (2012) Metaheuristics in water, geotechnical and transport engineering. Elsevier, Newnes GeemZWKimJHLoganathanGA new heuristic optimization algorithm: harmony searchSimulation2001762606810.1177/003754970107600201 ZhengW-MKneading plane of the circle mapChaos Solitons Fractals199447122112330817.5803010.1016/0960-0779(94)90033-7 AroraSSinghSAn improved butterfly optimization algorithm with chaosJ Intell Fuzzy Syst2017321107910881366.9022210.3233/JIFS-16798 NaanaaAFast chaotic optimization algorithm based on spatiotemporal maps for global optimizationAppl Math Comput201526940241133967871410.90275 GoldbergDEHollandJHGenetic algorithms and machine learningMach Learn198832959910.1023/A:1022602019183 MirjaliliSMirjaliliSMLewisAGrey wolf optimizerAdv Eng Softw201469466110.1016/j.advengsoft.2013.12.007 HanXChangXA chaotic digital secure communication based on a modified gravitational search algorithm filterInf Sci2012208142710.1016/j.ins.2012.04.039 YaoXLiuYLinGEvolutionary programming made fasterIEEE Trans Evol Comput1999328210210.1109/4235.771163 Aarts E, Korst J (1988) Simulated annealing and Boltzmann machines. Wiley, New York ChuangL-YTsaiS-WYangC-HChaotic catfish particle swarm optimization for solving global numerical optimization problemsAppl Math Comput2011217166900691627756811213.65087 JiaDZhengGKhanMKAn effective memetic differential evolution algorithm based on chaotic local searchInf Sci2011181153175318710.1016/j.ins.2011.03.018 JordehiARA chaotic-based big bang-big crunch algorithm for solving global optimisation problemsNeural Comput Appl20142561329133510.1007/s00521-014-1613-1 YildizARAn effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industryJ Mater Process Technol200920962773278010.1016/j.jmatprotec.2008.06.028 YangX-SIntroduction to mathematical optimization: from linear programming to metaheuristics2008CambridgeCambridge International Science Publishing1159.90004 StornRPriceKDifferential evolution-a simple and efficient heuristic for global optimization over continuous spacesJ Global Optim199711434135914795530888.9013510.1023/A:1008202821328 SayedGIHassanienAEAzarATFeature selection via a novel chaotic crow search algorithmNeural Comput Appl201710.1007/s00521-017-2988-6 TalatahariSAzarBFSheikholeslamiRGandomiAImperialist competitive algorithm combined with chaos for global optimizationCommun Nonlinear Sci Numer Simul20121731312131928437971241.9019310.1016/j.cnsns.2011.08.021 S Saremi (3343_CR24) 2017; 105 D Yang (3343_CR25) 2007; 34 DE Goldberg (3343_CR14) 1988; 3 S Arora (3343_CR10) 2017; 42 W-M Zheng (3343_CR43) 1994; 4 GI Sayed (3343_CR11) 2017 X Han (3343_CR38) 2012; 208 3343_CR13 E Rashedi (3343_CR19) 2009; 179 AR Jordehi (3343_CR5) 2015; 26 ZW Geem (3343_CR20) 2001; 76 3343_CR17 3343_CR16 AR Jordehi (3343_CR35) 2015; 26 AA Heidari (3343_CR33) 2017; 28 S Mirjalili (3343_CR23) 2014; 69 S Talatahari (3343_CR34) 2012; 17 L Santos Coelho dos (3343_CR8) 2006; 21 J Ilonen (3343_CR12) 2003; 17 D He (3343_CR41) 2001; 48 N Hansen (3343_CR21) 2001; 9 J Derrac (3343_CR46) 2011; 1 3343_CR40 MS Tavazoei (3343_CR42) 2007; 187 S García (3343_CR47) 2009; 15 3343_CR22 3343_CR44 S Arora (3343_CR28) 2017; 32 AR Yildiz (3343_CR4) 2009; 209 R Storn (3343_CR15) 1997; 11 3343_CR2 A Gandomi (3343_CR26) 2013; 18 F Wilcoxon (3343_CR48) 1970; 1 3343_CR3 AH Gandomi (3343_CR27) 2013; 18 3343_CR29 D Jia (3343_CR32) 2011; 181 3343_CR6 3343_CR7 3343_CR9 S Arora (3343_CR18) 2013; 69 L-Y Chuang (3343_CR36) 2011; 217 S Saremi (3343_CR37) 2014; 25 A Naanaa (3343_CR39) 2015; 269 X Han (3343_CR30) 2013; 218 AR Jordehi (3343_CR31) 2014; 25 X Yao (3343_CR45) 1999; 3 X-S Yang (3343_CR1) 2008 |
| References_xml | – reference: AroraSSinghSThe firefly optimization algorithm: convergence analysis and parameter selectionInt J Comput Appl20136934852 – reference: DerracJGarcíaSMolinaDHerreraFA practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithmsSwarm Evol Comput20111131810.1016/j.swevo.2011.02.002 – reference: NaanaaAFast chaotic optimization algorithm based on spatiotemporal maps for global optimizationAppl Math Comput201526940241133967871410.90275 – reference: Skalak DB (1994) Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: Proceedings of the eleventh international conference on machine learning, pp 293–301 – reference: Yang X-S (2010) Firefly algorithm, levy flights and global optimization. In: Research and development in intelligent systems XXVI, pp 209–218 – reference: TavazoeiMSHaeriMComparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithmsAppl Math Comput200718721076108523231141114.65335 – reference: Storn R (1996) Differential evolution design of an IIR filter. In: Proceedings of IEEE international conference on evolutionary computation, pp 268–273 – reference: GandomiAHYunGJYangX-STalatahariSChaos-enhanced accelerated particle swarm optimizationCommun Nonlinear Sci Numer Simul201318232734029718331323.9008210.1016/j.cnsns.2012.07.017 – reference: ZhengW-MKneading plane of the circle mapChaos Solitons Fractals199447122112330817.5803010.1016/0960-0779(94)90033-7 – reference: AroraSSinghSAn improved butterfly optimization algorithm with chaosJ Intell Fuzzy Syst2017321107910881366.9022210.3233/JIFS-16798 – reference: YangDLiGChengGOn the efficiency of chaos optimization algorithms for global optimizationChaos Solitons Fractals200734413661375228678010.1016/j.chaos.2006.04.057 – reference: JiaDZhengGKhanMKAn effective memetic differential evolution algorithm based on chaotic local searchInf Sci2011181153175318710.1016/j.ins.2011.03.018 – reference: GarcíaSMolinaDLozanoMHerreraFA study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the cec2005 special session on real parameter optimizationJ Heuristics20091566176441191.6882810.1007/s10732-008-9080-4 – reference: Kohli M, Arora S (2017) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Design Eng. https://doi.org/10.1016/j.jcde.2017.02.005 – reference: ChuangL-YTsaiS-WYangC-HChaotic catfish particle swarm optimization for solving global numerical optimization problemsAppl Math Comput2011217166900691627756811213.65087 – reference: RashediENezamabadi-PourHSaryazdiSGsa: a gravitational search algorithmInf Sci200917913223222481177.9037810.1016/j.ins.2009.03.004 – reference: dos CoelhoL SantosMarianiVCCombining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effectIEEE Trans Power Syst200621298910.1109/TPWRS.2006.873410 – reference: Arora S, Singh S (2015) Butterfly algorithm with levy flights for global optimization. In: International conference on signal processing, computing and control (ISPCC), pp 220–224 – reference: Lu H, Wang X, Fei Z, Qiu M (2014) The effects of using chaotic map on improving the performance of multiobjective evolutionary algorithms. Math Prob Eng 2014:16. https://doi.org/10.1155/2014/924652 – reference: YildizARAn effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industryJ Mater Process Technol200920962773278010.1016/j.jmatprotec.2008.06.028 – reference: Aarts E, Korst J (1988) Simulated annealing and Boltzmann machines. Wiley, New York – reference: GeemZWKimJHLoganathanGA new heuristic optimization algorithm: harmony searchSimulation2001762606810.1177/003754970107600201 – reference: SaremiSMirjaliliSLewisABiogeography-based optimisation with chaosNeural Comput Appl20142551077109710.1007/s00521-014-1597-x – reference: Eberhart RC, Kennedy J et al (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, New York, vol 1, pp 39–43 – reference: JordehiARA chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problemsNeural Comput Appl201526482783310.1007/s00521-014-1751-5 – reference: SaremiSMirjaliliSLewisAGrasshopper optimisation algorithm: theory and applicationAdv Eng Softw2017105304710.1016/j.advengsoft.2017.01.004 – reference: HanXChangXA chaotic digital secure communication based on a modified gravitational search algorithm filterInf Sci2012208142710.1016/j.ins.2012.04.039 – reference: WilcoxonFKattiSWilcoxRACritical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank testSel Tables Math Stat197011712590121.36201 – reference: GandomiAYangX-STalatahariSAlaviAFirefly algorithm with chaosCommun Nonlinear Sci Numer Simul2013181899829743651254.9208910.1016/j.cnsns.2012.06.009 – reference: JordehiARChaotic bat swarm optimisation (cbso)Appl Soft Comput20152652353010.1016/j.asoc.2014.10.010 – reference: IlonenJKamarainenJ-KLampinenJDifferential evolution training algorithm for feed-forward neural networksNeural Process Lett20031719310510.1023/A:1022995128597 – reference: GoldbergDEHollandJHGenetic algorithms and machine learningMach Learn198832959910.1023/A:1022602019183 – reference: JordehiARA chaotic-based big bang-big crunch algorithm for solving global optimisation problemsNeural Comput Appl20142561329133510.1007/s00521-014-1613-1 – reference: YangX-SIntroduction to mathematical optimization: from linear programming to metaheuristics2008CambridgeCambridge International Science Publishing1159.90004 – reference: MirjaliliSMirjaliliSMLewisAGrey wolf optimizerAdv Eng Softw201469466110.1016/j.advengsoft.2013.12.007 – reference: HeDHeCJiangL-GZhuH-WHuG-RChaotic characteristics of a one-dimensional iterative map with infinite collapsesIEEE Trans Circuits Syst I Fundam Theory Appl200148790090618410050993.3703310.1109/81.933333 – reference: AroraSSinghSNode localization in wireless sensor networks using butterfly optimization algorithmArab J Sci Eng2017423325333510.1007/s13369-017-2471-9 – reference: StornRPriceKDifferential evolution-a simple and efficient heuristic for global optimization over continuous spacesJ Global Optim199711434135914795530888.9013510.1023/A:1008202821328 – reference: SayedGIHassanienAEAzarATFeature selection via a novel chaotic crow search algorithmNeural Comput Appl201710.1007/s00521-017-2988-6 – reference: Yang X-S, Gandomi AH, Talatahari S, Alavi AH (eds) (2012) Metaheuristics in water, geotechnical and transport engineering. Elsevier, Newnes – reference: Brownlee J (2011) Clever algorithms: nature-inspired programming recipes. 1st edn. Lulu, North Carolina – reference: YaoXLiuYLinGEvolutionary programming made fasterIEEE Trans Evol Comput1999328210210.1109/4235.771163 – reference: HanXChangXAn intelligent noise reduction method for chaotic signals based on genetic algorithms and lifting wavelet transformsInf Sci201321810311810.1016/j.ins.2012.06.033 – reference: HeidariAAAbbaspourRAJordehiARAn efficient chaotic water cycle algorithm for optimization tasksNeural Comput Appl2017281578510.1007/s00521-015-2037-2 – reference: HansenNOstermeierACompletely derandomized self-adaptation in evolution strategiesEvol Comput20019215919510.1162/106365601750190398 – reference: Arora S, Singh S, Singh S, Sharma B (2014) Mutated firefly algorithm. In: International conference on parallel, distributed and grid computing (PDGC), pp 33–38 – reference: Wang G, Guo L (2013) A novel hybrid bat algorithm with harmony search for global numerical optimization. J Appl Math 2013:21. https://doi.org/10.1155/2013/696491 – reference: TalatahariSAzarBFSheikholeslamiRGandomiAImperialist competitive algorithm combined with chaos for global optimizationCommun Nonlinear Sci Numer Simul20121731312131928437971241.9019310.1016/j.cnsns.2011.08.021 – volume-title: Introduction to mathematical optimization: from linear programming to metaheuristics year: 2008 ident: 3343_CR1 – volume: 32 start-page: 1079 issue: 1 year: 2017 ident: 3343_CR28 publication-title: J Intell Fuzzy Syst doi: 10.3233/JIFS-16798 – volume: 3 start-page: 95 issue: 2 year: 1988 ident: 3343_CR14 publication-title: Mach Learn doi: 10.1023/A:1022602019183 – volume: 209 start-page: 2773 issue: 6 year: 2009 ident: 3343_CR4 publication-title: J Mater Process Technol doi: 10.1016/j.jmatprotec.2008.06.028 – ident: 3343_CR9 doi: 10.1109/ICEC.1996.542373 – volume: 208 start-page: 14 year: 2012 ident: 3343_CR38 publication-title: Inf Sci doi: 10.1016/j.ins.2012.04.039 – volume: 69 start-page: 48 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| Snippet | Grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The present study introduces... |
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| SubjectTerms | Algorithms Artificial Intelligence Chaos theory Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Global optimization Heuristic methods Image Processing and Computer Vision Optimization algorithms Original Article Probability and Statistics in Computer Science Swarming |
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