Abstract
Competitive swarm optimizer (CSO) has been concerned in recent years due to its achievements in solving global optimization problems. However, the CSO algorithm still suffers from issues such as low solution precision and premature convergence since it only relies on the winners to guide the population evolution. To address this issue, an improved competitive swarm optimizer with super-particle-leading is proposed in this paper. First, the super particle obtained by the cumulative learning strategy is used to provide a promising evolution direction for the population. Next, the weight-based dynamic omnidirectional strategy is employed to enhance the population exploration ability. Finally, CEC2017 benchmark problems are used to evaluate the efficiency of the proposed algorithm. The experimental results demonstrate that the proposed algorithm is competitive with the contender algorithms due to its better balance between exploration and exploitation.
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The experimental data used to support the findings of this study are included within the manuscript.
References
Thaher T, Chantar H, Too J, Mafarja M, Turabieh H, Houssein EH (2022) Boolean particle swarm optimization with various evolutionary population dynamics approaches for feature selection problems. Expert Syst Appl 195:116550
Liu J, Anavatti S, Garratt M, Abbass HA (2022) Modified continuous ant colony optimisation for multiple unmanned ground vehicle path planning. Expert Syst Appl 196:116605
Wang Z, Zhen H, Deng J, Zhang Q, Li X, Yuan M, Zeng J (2021) Multiobjective optimization-aided decision-making system for large-scale manufacturing planning. IEEE Trans Cybern 52(8):8326–8339
Sakai H, Iiduka H (2021) Riemannian adaptive optimization algorithm and its application to natural language processing. IEEE Trans Cybern 52(8):7328–7339
Li J, Zhan Z, Tan KC, Zhang J (2022) A meta-knowledge transfer-based differential evolution for multitask optimization. IEEE Trans Evol Comput 26(4):719–734
Guo Y, Zhang X, Gong D, Zhang Z, Yang J (2020) Novel interactive preference-based multiobjective evolutionary optimization for bolt supporting networks. IEEE Trans Evol Comput 24(4):750–764
Nadimi-Shahraki MH, Fatahi A, Zamani H, Mirjalili S, Abualigah L (2021) An improved moth-flame optimization algorithm with adaptation mechanism to solve numerical and mechanical engineering problems. Entropy 23:1637
Del Ser J, Osaba E, Molina D, Yang XS, Salcedo-Sanz S, Camacho D, Das S, Suganthan PN, Coello Coello CA, Herrera F (2019) Bio-inspired computation: where we stand and what’s next. Swarm Evol Comput 48:220–250
Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl Based Syst 191:105190
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Back T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Australia: Proceedings of ICNN’95-international conference on neural networks, pp 1942–1948
Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1:28–39
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes University
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315
Cheng R, ** Y (2014) A competitive swarm optimizer for large scale optimization. IEEE Trans Cybern 45(2):191–204
Wang X, Zhang K, Wang J, ** Y (2021) An enhanced competitive swarm optimizer with strongly convex sparse operator for large-scale multi-objective optimization. IEEE Trans Evol Comput 1–14
Musikawan P, Kongsorot Y, Muneesawang P, So-In C (2022) An enhanced obstacle-aware deployment scheme with an opposition-based competitive swarm optimizer for mobile WSNs. Expert Syst Appl 189:116035
Yang Z, Mourshed M, Liu K, Xu X, Feng S (2020) A novel competitive swarm optimized RBF neural network model for short-term solar power generation forecasting. Neurocomputing 397:415–421
**ong G, Shi D (2018) Orthogonal learning competitive swarm optimizer for economic dispatch problems. Appl Soft Comput 66:134–148
Mohapatra P, Das KN, Roy S (2017) A modified competitive swarm optimizer for large scale optimization problems. Appl Soft Comput 59:340–362
Wang X, Zhang B, Wang J, Zhang K, ** Y (2022) A cluster-based competitive particle swarm optimizer with a sparse truncation operator for multi-objective optimization. Swarm Evol Comput 71:101083
Ling T, Zhan Z, Wang Y, Wang Z, Yu W, Zhang J (2018) Competitive swarm optimizer with dynamic grou** for large scale optimization. In: 2018 IEEE congress on evolutionary computation (CEC), Rio de Janeiro, Brazil, pp 2655–2660
Huang W, Zhang W (2022) Multi-objective optimization based on an adaptive competitive swarm optimizer. Inf Sci 583:266–287
Nayak MR, Behura D, Nayak S (2021) Performance analysis of unbalanced radial feeder for integrating energy storage system with wind generator using inherited competitive swarm optimization algorithm. J Energy Storage 38:102574
**ong G, Zhang J, Shi D, Yuan X (2020) A simplified competitive swarm optimizer for parameter identification of solid oxide fuel cells. Energy Convers Manag 203:112204
Liu S, Lin Q, Li Q, Tan KC (2022) A comprehensive competitive swarm optimizer for large-scale multiobjective optimization. IEEE Trans Syst Man Cybern Syst 52(9):5829–5842
Chen X, Tang G (2022) Solving static and dynamic multi-area economic dispatch problems using an improved competitive swarm optimization algorithm. Energy 238:122035
Li W, Lei Z, Yuan J, Luo H, Xu Q (2021) Enhancing the competitive swarm optimizer with covariance matrix adaptation for large scale optimization. Appl Intell 51:4984–5006
Kumar A, Mehbodniya A, Webber JL, Haq MA, Gola KK, Singh P, Karupusamy S, Alazzam MB (2022) Optimal cluster head selection for energy efficient wireless sensor network using hybrid competitive swarm optimization and harmony search algorithm. Sustain Energy Technol Assess 52:102243
Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore
Crepinsek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3):1–33
Morales-Castaeda B, Zaldívar D, Cuevas E, Fausto F, Rodríguez A (2020) A better balance in metaheuristic algorithms: does it exist? Swarm Evol Comput 54:100671
Li DY, Guo W, Lerch A, Li YM, Wang L, Wu QD (2021) An adaptive particle swarm optimizer with decoupled exploration and exploitation for large scale optimization. Swarm Evol Comput 60:100789
Li JH, Gao YL, Wang KG, Sun Y (2021) A dual opposition-based learning for differential evolution with protective mechanism for engineering optimization problems. Appl Soft Comput 113:107942
Hansen N, Kern S (2004) Evaluating the CMA evolution strategy on multimodal test functions. Parallel problem solving from nature—PPSN VIII. PPSN 2004. Lecture notes in computer science. Springer, Berlin, pp 282–291
Zhang Y, Chi A, Mirjalili S (2021) Enhanced Jaya algorithm: a simple but efficient optimization method for constrained engineering design problems. Knowl Based Syst 233:107555
Kahraman HT, Aras S, Gedikli E (2020) Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms. Knowl Based Syst 190:105169
Abualigah L, Yousri D, Elaziz MA, Ewees AA, Al-qaness MAA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250
Yang Y, Chen H, Heidari AA, Gandomi AH (2021) Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864
Awad NH, Ali MZ, Liang JJ, Qu BY, Suganthan PN (2016) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-parameter numerical optimization. Technical Report, Nanyang Technological University, Singapore
Alcalá-Fdez J, Sánchez L, García S, del Jesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernández JC, Herrera F (2009) KEEL: a software tool to assess evolutionary algorithms to data mining problems. Soft Comput 13(3):307–318
Nadimi-Shahraki MH, Fatahi A, Zamani H, Mirjalili S, Abualigah L, Elaziz MA (2021) Migration-based moth-flame optimization algorithm. Processes 9:2276
Acknowledgements
This research is partly supported by the National Natural Science Foundation of China under Project Code (62176146, 61773314), and the Special project of Education Department of Shaanxi Provincial Government for Local Services (Program No. 21JC026).
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W.L. wrote the main manuscript text, Y.G. prepared figures 1-12, and L.W. prepared tables 1-8. All authors reviewed the manuscript.
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Li, W., Gao, Y. & Wang, L. An Improved Competitive Swarm Optimizer with Super-Particle-Leading. Neural Process Lett 55, 10501–10533 (2023). https://doi.org/10.1007/s11063-023-11336-8
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DOI: https://doi.org/10.1007/s11063-023-11336-8