Skip to main content
Erschienen in: Soft Computing 12/2020

06.11.2019 | Methodologies and Application

A novel life choice-based optimizer

verfasst von: Abhishek Khatri, Akash Gaba, K. P. S. Rana, Vineet Kumar

Erschienen in: Soft Computing | Ausgabe 12/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This paper presents a novel metaheuristic algorithm named as life choice-based optimizer (LCBO) developed on the typical decision-making ability of humans to attain their goals while learning from fellow members. LCBO is investigated on 29 popular benchmark functions which included six CEC-2005 functions, and its performance has been benchmarked against seven optimization techniques including recent ones. Further, different abilities of LCBO optimization algorithm such as exploitation, exploration and local minima avoidance were also investigated and have been reported. In addition to this, scalability is tested for several benchmark functions where dimensions have been varied till 200. Furthermore, two engineering optimization benchmark problems, namely pressure vessel design and cantilever beam design, were also optimized using LCBO and the results have been compared with recently reported other algorithms. The obtained comparative results in all the above-mentioned experimentations revealed the clear superiority of LCBO over the other considered metaheuristic optimization algorithms. Therefore, based on the presented investigations, it is concluded that LCBO is a potential optimizer for engineering problems.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Anhänge
Nur mit Berechtigung zugänglich
Literatur
Zurück zum Zitat Abbass H (2002) MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach. Inst Electr Electron Eng 1:207–214 Abbass H (2002) MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach. Inst Electr Electron Eng 1:207–214
Zurück zum Zitat Abedinpourshotorban H, Mariyam Shamsuddin S, Beheshti Z, Jawawi D (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput 26:8–22 Abedinpourshotorban H, Mariyam Shamsuddin S, Beheshti Z, Jawawi D (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput 26:8–22
Zurück zum Zitat Ahrari A, Atai A (2010) Grenade explosion method—a novel tool for optimization of multimodal functions. Appl Soft Comput J 10(4):1132–1140 Ahrari A, Atai A (2010) Grenade explosion method—a novel tool for optimization of multimodal functions. Appl Soft Comput J 10(4):1132–1140
Zurück zum Zitat Alatas B (2011) ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst Appl 38(10):13170–13180 Alatas B (2011) ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst Appl 38(10):13170–13180
Zurück zum Zitat Askarzadeh A (2014) Bird mating optimizer: an optimization algorithm inspired by bird mating strategies. Commun Nonlinear Sci Numer Simul 19(4):1213–1228MathSciNetMATH Askarzadeh A (2014) Bird mating optimizer: an optimization algorithm inspired by bird mating strategies. Commun Nonlinear Sci Numer Simul 19(4):1213–1228MathSciNetMATH
Zurück zum Zitat Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation (CEC 2007), pp 4661–4667 Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation (CEC 2007), pp 4661–4667
Zurück zum Zitat Bouchekara H (2017) Most valuable player algorithm: a novel optimization algorithm inspired from sport. Oper Res 1–57 Bouchekara H (2017) Most valuable player algorithm: a novel optimization algorithm inspired from sport. Oper Res 1–57
Zurück zum Zitat Cheng M, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112 Cheng M, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112
Zurück zum Zitat Chickermane H, Gea H (1996) Structural optimization using a new local approximation method. Int J Numer Methods Eng 39(5):829–846MathSciNetMATH Chickermane H, Gea H (1996) Structural optimization using a new local approximation method. Int J Numer Methods Eng 39(5):829–846MathSciNetMATH
Zurück zum Zitat Chu S-C, Tsai P-W, Pan J-S (2006) Cat swarm optimization. PRICAI 2006: trends in artificial intelligence. PRICAI 2006. Lect Notes Comput Sci 4099:854–858 Chu S-C, Tsai P-W, Pan J-S (2006) Cat swarm optimization. PRICAI 2006: trends in artificial intelligence. PRICAI 2006. Lect Notes Comput Sci 4099:854–858
Zurück zum Zitat Coello C, Montes M (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16(3):193–203 Coello C, Montes M (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16(3):193–203
Zurück zum Zitat Cuevas E, Cienfuegos M, Zaldívar D, Pérez-Cisneros M (2013) A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl 40(16):6374–6384 Cuevas E, Cienfuegos M, Zaldívar D, Pérez-Cisneros M (2013) A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl 40(16):6374–6384
Zurück zum Zitat Cuevas E, Echavarría A, Ramírez-Ortegón M (2014) An optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation. Appl Intell 40(2):256–272 Cuevas E, Echavarría A, Ramírez-Ortegón M (2014) An optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation. Appl Intell 40(2):256–272
Zurück zum Zitat Dai C, Zhu Y, Cheng W (2006) Seeker optimization algorithm. Computational intelligence and security. CIS 2006. Lect Notes Comput Sci 4456:167–176 Dai C, Zhu Y, Cheng W (2006) Seeker optimization algorithm. Computational intelligence and security. CIS 2006. Lect Notes Comput Sci 4456:167–176
Zurück zum Zitat Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70 Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70
Zurück zum Zitat Dorigo M, Di Caro G (1999) Ant colony optimization: A new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99, pp 1470–1477 Dorigo M, Di Caro G (1999) Ant colony optimization: A new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99, pp 1470–1477
Zurück zum Zitat Duman E, Uysal M, Alkaya A (2012) Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Inf Sci 217:65–77MathSciNet Duman E, Uysal M, Alkaya A (2012) Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Inf Sci 217:65–77MathSciNet
Zurück zum Zitat Eita M, Fahmy M (2014) Group counseling optimization. Appl Soft Comput J 22:585–604 Eita M, Fahmy M (2014) Group counseling optimization. Appl Soft Comput J 22:585–604
Zurück zum Zitat Erol O, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37(2):106–111 Erol O, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37(2):106–111
Zurück zum Zitat Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166 Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166
Zurück zum Zitat Farasat A, Menhaj M, Mansouri T, Moghadam M (2010) ARO: a new model-free optimization algorithm inspired from asexual reproduction. Appl Soft Comput J 10(4):1284–1292 Farasat A, Menhaj M, Mansouri T, Moghadam M (2010) ARO: a new model-free optimization algorithm inspired from asexual reproduction. Appl Soft Comput J 10(4):1284–1292
Zurück zum Zitat Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Studies in Computational Intelligence, vol. 21, Springer, BerlinMATH Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Studies in Computational Intelligence, vol. 21, Springer, BerlinMATH
Zurück zum Zitat François O (1998) An evolutionary strategy for global minimization and its Markov chain analysis. IEEE Trans Evol Comput 2(3):77–90 François O (1998) An evolutionary strategy for global minimization and its Markov chain analysis. IEEE Trans Evol Comput 2(3):77–90
Zurück zum Zitat Gandomi A (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183 Gandomi A (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183
Zurück zum Zitat Gandomi A, Alavi A (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845MathSciNetMATH Gandomi A, Alavi A (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845MathSciNetMATH
Zurück zum Zitat Gandomi A, Yang X, Alavi A (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35 Gandomi A, Yang X, Alavi A (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35
Zurück zum Zitat Gao L, Hailu A (2010) Comprehensive learning particle swarm optimizer for constrained mixed-variable optimization problems. Int J Comput Intell Syst 3(6):832–842 Gao L, Hailu A (2010) Comprehensive learning particle swarm optimizer for constrained mixed-variable optimization problems. Int J Comput Intell Syst 3(6):832–842
Zurück zum Zitat Ghaemi M, Feizi-Derakhshi M (2014) Forest optimization algorithm. Expert Syst Appl 41(15):6676–6687 Ghaemi M, Feizi-Derakhshi M (2014) Forest optimization algorithm. Expert Syst Appl 41(15):6676–6687
Zurück zum Zitat Ghorbani N, Babaei E (2014) Exchange market algorithm. Appl Soft Comput J 19:177–187 Ghorbani N, Babaei E (2014) Exchange market algorithm. Appl Soft Comput J 19:177–187
Zurück zum Zitat Glover F (1989) Tabu search—part I. ORSA J Comput 1(3):190–206MATH Glover F (1989) Tabu search—part I. ORSA J Comput 1(3):190–206MATH
Zurück zum Zitat Hasançebi O, Azad S (2015) Adaptive dimensional search: a new metaheuristic algorithm for discrete truss sizing optimization. Comput Struct 154:1–16 Hasançebi O, Azad S (2015) Adaptive dimensional search: a new metaheuristic algorithm for discrete truss sizing optimization. Comput Struct 154:1–16
Zurück zum Zitat Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184MathSciNet Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184MathSciNet
Zurück zum Zitat Hatamlou A, Javidy B, Mirjalili S (2015) Ions motion algorithm for solving optimization problems. Appl Soft Comput 32:72–79 Hatamlou A, Javidy B, Mirjalili S (2015) Ions motion algorithm for solving optimization problems. Appl Soft Comput 32:72–79
Zurück zum Zitat He S, Wu Q, Saunders J (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13(5):973–990 He S, Wu Q, Saunders J (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13(5):973–990
Zurück zum Zitat Hedayatzadeh R, Salmassi F, Keshtgari M, Akbari R, Ziarati K (2010) Termite colony optimization: a novel approach for optimizing continuous problems. In: Proceedings—2010 18th Iranian conference on electrical engineering, ICEE, pp 553–558 Hedayatzadeh R, Salmassi F, Keshtgari M, Akbari R, Ziarati K (2010) Termite colony optimization: a novel approach for optimizing continuous problems. In: Proceedings—2010 18th Iranian conference on electrical engineering, ICEE, pp 553–558
Zurück zum Zitat Holland J (1992) Adaptation in natural and artificial systems. The MIT Press, Cambridge Holland J (1992) Adaptation in natural and artificial systems. The MIT Press, Cambridge
Zurück zum Zitat Hosseini H (2009) The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int J Bio Inspired Comput 1(1/2):71–79 Hosseini H (2009) The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int J Bio Inspired Comput 1(1/2):71–79
Zurück zum Zitat Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6(1/2):132–140 Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6(1/2):132–140
Zurück zum Zitat Huang F, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356MathSciNetMATH Huang F, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356MathSciNetMATH
Zurück zum Zitat Husseinzadeh Kashan A (2014) A new metaheuristic for optimization: optics inspired optimization (OIO). Comput Oper Res 55:99–125MathSciNetMATH Husseinzadeh Kashan A (2014) A new metaheuristic for optimization: optics inspired optimization (OIO). Comput Oper Res 55:99–125MathSciNetMATH
Zurück zum Zitat Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471MathSciNetMATH Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471MathSciNetMATH
Zurück zum Zitat Kaveh A (2014) Advances in metaheuristic algorithms for optimal design of structures. Springer, BerlinMATH Kaveh A (2014) Advances in metaheuristic algorithms for optimal design of structures. Springer, BerlinMATH
Zurück zum Zitat Kaveh A, Bakhshpoori T (2016) Water evaporation optimization: a novel physically inspired optimization algorithm. Comput Struct 167:69–85 Kaveh A, Bakhshpoori T (2016) Water evaporation optimization: a novel physically inspired optimization algorithm. Comput Struct 167:69–85
Zurück zum Zitat Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84 Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84
Zurück zum Zitat Kaveh A, Farhoudi N (2013) A new optimization method: dolphin echolocation. Adv Eng Softw 59:53–70 Kaveh A, Farhoudi N (2013) A new optimization method: dolphin echolocation. Adv Eng Softw 59:53–70
Zurück zum Zitat Kaveh A, Ilchi Ghazaan M (2017) Vibrating particles system algorithm for truss optimization with multiple natural frequency constraints. Acta Mech 228(1):307–322MathSciNet Kaveh A, Ilchi Ghazaan M (2017) Vibrating particles system algorithm for truss optimization with multiple natural frequency constraints. Acta Mech 228(1):307–322MathSciNet
Zurück zum Zitat Kaveh A, Mahdavi V (2014) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27 Kaveh A, Mahdavi V (2014) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27
Zurück zum Zitat Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3–4):267–289MATH Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3–4):267–289MATH
Zurück zum Zitat Kaveh A, Zolghadr A (2016) A novel meta-heuristic algorithm: tug of war optimization. Int Journal of Optim Civil Eng 6(4):469–492 Kaveh A, Zolghadr A (2016) A novel meta-heuristic algorithm: tug of war optimization. Int Journal of Optim Civil Eng 6(4):469–492
Zurück zum Zitat Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks, pp 1942–1948 Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks, pp 1942–1948
Zurück zum Zitat Kiran M (2015) TSA: tree-seed algorithm for continuous optimization. Expert Syst Appl 42(19):6686–6698 Kiran M (2015) TSA: tree-seed algorithm for continuous optimization. Expert Syst Appl 42(19):6686–6698
Zurück zum Zitat Koza JR (1994) Genetic programming II: automatic discovery of reusable subprograms. MIT Press, CambridgeMATH Koza JR (1994) Genetic programming II: automatic discovery of reusable subprograms. MIT Press, CambridgeMATH
Zurück zum Zitat Krishnanand KN, Ghose D (2006) Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and Grid Systems 2(3):209–222MATH Krishnanand KN, Ghose D (2006) Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and Grid Systems 2(3):209–222MATH
Zurück zum Zitat Krohling R, Dos Santos Coelho L (2006) Coevolutionary particle swarm optimization using gaussian distribution for solving constrained optimization problems. IEEE Trans Syst Man Cybern B Cybern 36(6):1407–1416 Krohling R, Dos Santos Coelho L (2006) Coevolutionary particle swarm optimization using gaussian distribution for solving constrained optimization problems. IEEE Trans Syst Man Cybern B Cybern 36(6):1407–1416
Zurück zum Zitat Lam A, Li V (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput 14(3):381–399 Lam A, Li V (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput 14(3):381–399
Zurück zum Zitat Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877 Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877
Zurück zum Zitat Liang J, Suganthan P, Deb K (2005) Novel composition test functions for numerical global optimization. Swarm Intell Symp 2005:68–75 Liang J, Suganthan P, Deb K (2005) Novel composition test functions for numerical global optimization. Swarm Intell Symp 2005:68–75
Zurück zum Zitat Lu X, Zhou Y (2008) A novel global convergence algorithm: bee collecting pollen algorithm. Advanced intelligent computing theories and applications. With aspects of artificial intelligence. ICIC 2008. Lect Notes Comput Sci 5227:518–525 Lu X, Zhou Y (2008) A novel global convergence algorithm: bee collecting pollen algorithm. Advanced intelligent computing theories and applications. With aspects of artificial intelligence. ICIC 2008. Lect Notes Comput Sci 5227:518–525
Zurück zum Zitat Mehrabian A, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1(4):355–366 Mehrabian A, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1(4):355–366
Zurück zum Zitat Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. Advances in swarm intelligence. ICSI 2014. Lect Notes Comput Sci 8794:86–94 Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. Advances in swarm intelligence. ICSI 2014. Lect Notes Comput Sci 8794:86–94
Zurück zum Zitat Merrikh-Bayat F (2015) The runner-root algorithm: a metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature. Appl Soft Comput J 33:292–303 Merrikh-Bayat F (2015) The runner-root algorithm: a metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature. Appl Soft Comput J 33:292–303
Zurück zum Zitat Mirjalili S (2015a) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249 Mirjalili S (2015a) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249
Zurück zum Zitat Mirjalili S (2015b) The ant lion optimizer. Adv Eng Softw 83:80–98 Mirjalili S (2015b) The ant lion optimizer. Adv Eng Softw 83:80–98
Zurück zum Zitat Mirjalili S (2016a) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073 Mirjalili S (2016a) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073
Zurück zum Zitat Mirjalili S (2016b) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133 Mirjalili S (2016b) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133
Zurück zum Zitat Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67 Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Zurück zum Zitat Mirjalili S, Mirjalili S, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46–61 Mirjalili S, Mirjalili S, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46–61
Zurück zum Zitat Mirjalili S, Mirjalili S, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513 Mirjalili S, Mirjalili S, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513
Zurück zum Zitat Mirjalili S, Gandomi A, Mirjalili S, Saremi S, Faris H, Mirjalili S (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191 Mirjalili S, Gandomi A, Mirjalili S, Saremi S, Faris H, Mirjalili S (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191
Zurück zum Zitat Moghaddam F, Moghaddam R, Cheriet M (2012) Curved space optimization: a random search based on general relativity theory Moghaddam F, Moghaddam R, Cheriet M (2012) Curved space optimization: a random search based on general relativity theory
Zurück zum Zitat Moghdani R, Salimifard K (2018) Volleyball premier league algorithm. Appl Soft Comput J 64:161–185 Moghdani R, Salimifard K (2018) Volleyball premier league algorithm. Appl Soft Comput J 64:161–185
Zurück zum Zitat Moosavian N, Kasaee Roodsari B (2014) Soccer league competition algorithm: a novel meta-heuristic algorithm for optimal design of water distribution networks. Swarm Evol Comput 17:14–24 Moosavian N, Kasaee Roodsari B (2014) Soccer league competition algorithm: a novel meta-heuristic algorithm for optimal design of water distribution networks. Swarm Evol Comput 17:14–24
Zurück zum Zitat Oftadeh R, Mahjoob M, Shariatpanahi M (2010) A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput Math Appl 60(7):2087–2098MATH Oftadeh R, Mahjoob M, Shariatpanahi M (2010) A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput Math Appl 60(7):2087–2098MATH
Zurück zum Zitat Pan W-T (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74 Pan W-T (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74
Zurück zum Zitat Pham D, Eldukhri E, Soroka A, Ghanbarzadeh A, Kog E, Otri S, Zaidi M (2006) The bees algorithm-a novel tool for complex optimisation problems. In: Intelligent production machines and systems, pp 454–459 Pham D, Eldukhri E, Soroka A, Ghanbarzadeh A, Kog E, Otri S, Zaidi M (2006) The bees algorithm-a novel tool for complex optimisation problems. In: Intelligent production machines and systems, pp 454–459
Zurück zum Zitat Pinto P, Runkler TA, Sousa JM (2005) Wasp swarm optimization of logistic systems. In: Ribeiro B, Albrecht RF, Dobnikar A, Pearson DW, Steele NC (eds) Adaptive and natural computing algorithms. Springer, Vienna, pp 264–267 Pinto P, Runkler TA, Sousa JM (2005) Wasp swarm optimization of logistic systems. In: Ribeiro B, Albrecht RF, Dobnikar A, Pearson DW, Steele NC (eds) Adaptive and natural computing algorithms. Springer, Vienna, pp 264–267
Zurück zum Zitat Rao RV, Vimal JS, Vakharia DP (2007) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315 Rao RV, Vimal JS, Vakharia DP (2007) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315
Zurück zum Zitat Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248MATH Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248MATH
Zurück zum Zitat Ryan C, Collins J (1998) Grammatical evolution: evolving programs for an arbitrary language. Genet Program Lect Notes Comput Sci 1391:83–96 Ryan C, Collins J (1998) Grammatical evolution: evolving programs for an arbitrary language. Genet Program Lect Notes Comput Sci 1391:83–96
Zurück zum Zitat Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput J 13(5):2592–2612 Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput J 13(5):2592–2612
Zurück zum Zitat Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75:1–18 Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75:1–18
Zurück zum Zitat Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Des 112(2):223–229 Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Des 112(2):223–229
Zurück zum Zitat Shareef H, Ibrahim A, Mutlag A (2015) Lightning search algorithm. Appl Soft Comput J 36:315–333 Shareef H, Ibrahim A, Mutlag A (2015) Lightning search algorithm. Appl Soft Comput J 36:315–333
Zurück zum Zitat Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713 Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713
Zurück zum Zitat Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–349MathSciNetMATH Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–349MathSciNetMATH
Zurück zum Zitat Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report, 2005005 Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report, 2005005
Zurück zum Zitat Svanberg K (1987) The method of moving asymptotes—a new method for structural optimization. Int J Numer Methods Eng 24:359–373MathSciNetMATH Svanberg K (1987) The method of moving asymptotes—a new method for structural optimization. Int J Numer Methods Eng 24:359–373MathSciNetMATH
Zurück zum Zitat Tan Y, Zhu Y (2015) Fireworks algorithm for optimization. Advances in swarm intelligence. ICSI 2010. Lect Notes Comput Sci 6145:355–364 Tan Y, Zhu Y (2015) Fireworks algorithm for optimization. Advances in swarm intelligence. ICSI 2010. Lect Notes Comput Sci 6145:355–364
Zurück zum Zitat Van Laarhoven PJM, Aarts EHL (1987) Simulated annealing. In: Simulated annealing: theory and applications. Springer, Dordrecht, pp 7–15MATH Van Laarhoven PJM, Aarts EHL (1987) Simulated annealing. In: Simulated annealing: theory and applications. Springer, Dordrecht, pp 7–15MATH
Zurück zum Zitat Venkata RR (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34 Venkata RR (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34
Zurück zum Zitat Wang G, Deb S, Coelho L (2016a) Elephant herding optimization. In: Proceedings—2015 3rd international symposium on computational and business intelligence, ISCBI 2015, pp 1–5 Wang G, Deb S, Coelho L (2016a) Elephant herding optimization. In: Proceedings—2015 3rd international symposium on computational and business intelligence, ISCBI 2015, pp 1–5
Zurück zum Zitat Wang G, Zhao X, Deb S (2016b) A novel monarch butterfly optimization with greedy strategy and self-adaptive. In: Proceedings—2015 2nd international conference on soft computing and machine intelligence, ISCMI 2015, pp 45–50 Wang G, Zhao X, Deb S (2016b) A novel monarch butterfly optimization with greedy strategy and self-adaptive. In: Proceedings—2015 2nd international conference on soft computing and machine intelligence, ISCMI 2015, pp 45–50
Zurück zum Zitat Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82 Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82
Zurück zum Zitat Woo Geem Z, Hoon Kim J, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simul Trans Soc Model Simul Int 76(2):60–68 Woo Geem Z, Hoon Kim J, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simul Trans Soc Model Simul Int 76(2):60–68
Zurück zum Zitat Yang X-S (2009) Firefly algorithms for multimodal optimization. Stochastic algorithms: foundations and applications. SAGA 2009. Lect Notes Comput Sci 5792:169–178 Yang X-S (2009) Firefly algorithms for multimodal optimization. Stochastic algorithms: foundations and applications. SAGA 2009. Lect Notes Comput Sci 5792:169–178
Zurück zum Zitat Yang X-S (2010) A new metaheuristic Bat-inspired Algorithm. Stud Comput Intell 284:65–74MATH Yang X-S (2010) A new metaheuristic Bat-inspired Algorithm. Stud Comput Intell 284:65–74MATH
Zurück zum Zitat Yang X-S (2012) Flower pollination algorithm for global optimization. Unconventional computation and natural computation. UCNC 2012. Lect Notes Comput Sci 7445:240–249 Yang X-S (2012) Flower pollination algorithm for global optimization. Unconventional computation and natural computation. UCNC 2012. Lect Notes Comput Sci 7445:240–249
Zurück zum Zitat Yang X-S, Deb S (2009) Cuckoo search via levy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC), pp 210–214 Yang X-S, Deb S (2009) Cuckoo search via levy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC), pp 210–214
Zurück zum Zitat Yang S, Jiang J, Yan G (2009) A dolphin partner optimization. In: Proceedings of the 2009 WRI global congress on intelligent systems, GCIS 2009, vol. 1, pp. 124–128 Yang S, Jiang J, Yan G (2009) A dolphin partner optimization. In: Proceedings of the 2009 WRI global congress on intelligent systems, GCIS 2009, vol. 1, pp. 124–128
Zurück zum Zitat Yao X, Liu Y (1996) Fast evolutionary programming. Computational intelligence and intelligent systems. ISICA 2010. Commun Comput Inf Sci 107:79–86 Yao X, Liu Y (1996) Fast evolutionary programming. Computational intelligence and intelligent systems. ISICA 2010. Commun Comput Inf Sci 107:79–86
Zurück zum Zitat Yazdani M, Jolai F (2016) Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Des Eng 3(1):24–36 Yazdani M, Jolai F (2016) Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Des Eng 3(1):24–36
Zurück zum Zitat Zhao R, Tang W (2008) Monkey algorithm for global numerical optimization. J Uncertain Syst 2(3):165–176 Zhao R, Tang W (2008) Monkey algorithm for global numerical optimization. J Uncertain Syst 2(3):165–176
Zurück zum Zitat Zheng Y (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11MathSciNetMATH Zheng Y (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11MathSciNetMATH
Zurück zum Zitat Zheng Y, Ling H, Xue J (2014) Ecogeography-based optimization: enhancing biogeography-based optimization with ecogeographic barriers and differentiations. Comput Oper Res 50:115–127MATH Zheng Y, Ling H, Xue J (2014) Ecogeography-based optimization: enhancing biogeography-based optimization with ecogeographic barriers and differentiations. Comput Oper Res 50:115–127MATH
Metadaten
Titel
A novel life choice-based optimizer
verfasst von
Abhishek Khatri
Akash Gaba
K. P. S. Rana
Vineet Kumar
Publikationsdatum
06.11.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 12/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-019-04443-z

Weitere Artikel der Ausgabe 12/2020

Soft Computing 12/2020 Zur Ausgabe

Premium Partner