Abstract
In the recent past year of 2020–2021, researchers proposed many swarm intelligence based algorithms. In the present work, an effort has been made to compare the performance of these algorithms for the real-life constraint optimization problem. Swarm intelligence-based algorithms developed during 2020–2021 such as GEO, WHO, MPA, JSO, ChoA, MA, BWO, AO, COOT, and TSA are considered in the present work. These algorithms are implemented for the performance optimization of the Sterling cycle operated refrigeration/liquefaction system. Four operating variables and two output constraints of the Sterling cycle based system are considered for optimization. Comparative results are presented with statistical data to judge the performance of the algorithm and subsequently identify the statistical significance and rank of the algorithm. The effect of various constraint handling methods on the performance of algorithms is evaluated and presented. The behaviour of constraint handling methods is analyzed and presented with statistical data. Statistical analysis is also performed to observe whether the constraint handling methods produce a significant difference on the output of the considered algorithm. The effect of output constraints on the performance of algorithms is also evaluated and presented. Finally, the convergence behaviour of the competitive algorithms is obtained and demonstrated.
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References
Abualigah L, Yousri D, Abd-Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250
Ahmadi MH, Ahmadi MA, Mohammadi AH, Feidt M, Pourkiaei SM (2014) Multi-objective optimization of an irreversible stirling cryogenic refrigerator cycle. Energy Convers Manag 82:351–360
Ahmadi MH, Ahmadi MA, Maleki A, Pourfayaz F, Bidi M, Açýkkalp E (2017a) Exergetic sustainability evaluation and multi-objective optimization of performance of an irreversible nanoscale Stirling refrigeration cycle operating with Maxwell Boltzmann gas. Renew Sustain Energy Rev 78:80–92
Ahmadi MH, Nabakhteh MA, Ahmadi MA, Pourfayaz F, Bidi M (2017b) Investigation and optimization of performance of nano-scale Stirling refrigerator using working fluid as Maxwell-Boltzmann gases. Physica A 483:337–350
Alsattar HA, Zaidan AA, Zaidan BB (2020) Novel meta-heuristic bald eagle search optimization algorithm. Artif Intell Rev 53:2237–2264
Ameca-Alducin MY, Hasani-Shoreh M, Blaikie W, Neumann F, Mezura-Montes E (2018) A comparison of constraint handling techniques for dynamic constrained optimization problems. In: IEEE congress on evolutionary computation (CEC), pp 1–8
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12
Batooei A, Keshavarz A (2018) A gamma type stirling refrigerator optimization: an experimental and analytical investigation. Int J Refrig 91:89–100
Chou JS, Truong DN (2020) Multi-objective optimization inspired by behaviour of jellyfish for solving structural design problems. Chaos Solitons Fractals 135:109738
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 (2019) Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowl-Based Syst 165:169–196
Dorigo M, Maniezzo V, Colorni A (1991) Positive feedback as a search strategy, Technical Report 91–016, Politecnico di Milano, Italy
Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Resour Plan Manag 129:210–225
Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine Predators Algorithm: A Nature-inspired Metaheuristic. Expert Syst Appl 152:113377
Hayyolalam V, Kazem AA (2020) Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng Appl Artif Intell 87:103249
Hayyolalam V, Pourhaji-Kazem AA (2017) QoS-aware optimization of cloud service composition using symbiotic organisms search algorithm. J Intell Proc Electr Technol 8:29–38
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872
Jahani E, Chizari M (2018) Tackling global optimization problems with a novel algorithm–Mouth Brooding Fish algorithm. Appl Soft Comput 62:987–1002
Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148–175
Joaquín D, Salvador G, Daniel M, Francisco H (2011) A practical tutorial on the use of non parametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, Technical report–TR06, Erciyes University, Engineering Faculty, Computer Engineering Department
Kaur S, Awasthi LK, Sangal AL, Dhiman G (2020) Tunicate Swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell 90:103541
Kennedy J, Eberhart RC (1995) Particle swarm optimization, In: Proceedings of the 1995 IEEE international conference on neural networks, Perth, Australia, pp 1942–1948
Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338
Mallipeddi R, Suganthan PN (2010) Ensemble of constraint handling techniques. IEEE Trans Evol Comput 14:561–579
Mirjalili S (2015a) The ant lion optimizer. Adv Eng Softw 83:80–98
Mirjalili S (2015b) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249
Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053–1073
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191
Mirjalili SZ, Mirjalili S, Saremi S, Faris H, Aljarah I (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48:805–820
Mohamed AA, Hassan SA, Hemeida AM, Alkhalaf S, Mahmoud MM, Eldin AM (2020) Parasitism-Predation algorithm (PPA): a novel approach for feature selection. Ain Shams Eng J 11:293–308
Mohammadi-Balani A, Nayeri MD, Azar A, Taghizadeh-Yazdi M (2021) Golden eagle optimizer: a nature-inspired metaheuristic algorithm. Comput Ind Eng 152:107050
Naruei I, Keynia F (2021a) Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems. Eng Comput. https://doi.org/10.1007/s00366-021-01438-z
Naruei I, Keynia F (2021b) A new optimization method based on coot bird natural life model. Expert Syst Appl 183:115352
Nguyen TT, Yao X (2012) Continuous dynamic constrained optimization: the challenges. IEEE Trans Evol Comput 16:769–786
Passino K (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22:52–67
Patel VK, Savsani VJ, Tawhid MA (2019) Thermal system optimization: a population-based metaheuristic approach. Springer, Switzerland
Pham D, Ghanbarzadeh A, Koc E, Rahim S, Zaidi M (2005) The bees algorithm: technical note, Technical report, Manufacturing engineering centre. Cardiff University, Cardiff, UK
Shadravan S, Naji HR, Bardsiri VK (2019) The Sailfish Optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20–34
Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng 8:22–34
Yang XS (2008) Firefly Algorithm, nature-inspired metaheuristic algorithms. Luniver Press, Beckington, pp 128–138
Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: González JR, Sancho-Royo A, Pelta DA, Cruz C (eds) Nature inspired cooperative strategies for optimization. Springer, Berlin/Heidelberg, pp 65–74
Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Modell Numer Optim 1:330–343
Zervoudakis K, Tsafarakis S (2020) A mayfly optimization algorithm. Comput Ind Eng 145:106559
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Raja, B.D., Patel, V.K., Savsani, V.J. et al. On the comparative performance of recent swarm intelligence based algorithms for optimization of real-life Sterling cycle operated refrigeration/liquefaction system. Artif Intell Rev 56, 1297–1317 (2023). https://doi.org/10.1007/s10462-022-10201-9
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DOI: https://doi.org/10.1007/s10462-022-10201-9