Multipopulation cooperative particle swarm optimization with a mixed mutation strategy. (English) Zbl 1459.90228
Summary: The traditional particle swarm optimization algorithm learns from the two best experiences: the best position previously learned by the particle itself and the best position learned by the entire population to date. This learning strategy is simple and ordinary, but when addressing high-dimensional optimization problems, it is unable to quickly find the global optimal solution due to its low efficiency. This paper proposes a multipopulation cooperative particle swarm optimization (MPCPSO) algorithm with a dynamic segment-based mean learning strategy and a multidimensional comprehensive learning strategy. In MPCPSO, the dynamic segment-based mean learning strategy (DSMLS), which is employed to construct learning exemplars, achieves information sharing and coevolution between populations. The multidimensional comprehensive learning strategy (MDCLS) is employed to speed up convergence and improve the accuracy of MPCPSO solutions. Additionally, a differential mutation operator is introduced to increase the population diversity and enhance the global exploration ability of MPCPSO. Sixteen benchmark functions and seven well-known PSO variants are employed to verify the advantages of MPCPSO. The comparison results indicate that MPCPSO has a faster convergence speed, obtains more accurate solutions, and is more robust.
MSC:
| 90C59 | Approximation methods and heuristics in mathematical programming |
| 68W50 | Evolutionary algorithms, genetic algorithms (computational aspects) |
| 90C29 | Multi-objective and goal programming |
Keywords:
particle swarm optimization; dynamic segment-based mean learning strategy; multidimensional comprehensive learning; differential mutationSoftware:
CEC 05References:
| [1] | Hosseinabadi, A. A.R.; Vahidi, J.; Saemi, B.; Sangaiah, A. K.; Elhoseny, M., Extended genetic algorithm for solving open-shop scheduling problem, Soft Comput, 23, 13 (2019) |
| [2] | Huang, Y.; Li, W., A self-feedback strategy differential evolution with fitness landscape analysis, Soft Comput, 22, 23, 7773-7785 (2018) |
| [3] | Xue, Y.; Jiang, J.; Zhao, B.; Ma, T., A self-adaptive artificial bee colony algorithm based on global best for global optimization, Soft Comput, 22, 9, 2935-2952 (2018) |
| [4] | Li, W.; Chen, Z. X., Self-Feedback differential evolution adapting to fitness landscape characteristics, Soft Comput, 23, 4, 1151-1163 (2019) |
| [5] | Wei, L.; Zhang, Z.; Zhang, D.; Leung, S. C., A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints, Eur J Oper Res, 265, 3, 843-859 (2018) · Zbl 1374.90329 |
| [6] | Engin, O.; Güçlü, A., A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems, Appl Soft Comput, 72, 166-176 (2018) |
| [7] | Kennedy, J.; Eberhart, R. C., Particle swarm optimization, Proc. IEEE Int. Conf. Neural Netw., 4, 1942-1948 (1995) |
| [8] | Eberhart, R. C.; Kennedy, J., A new optimizer using particle swarm theory, Proc. 6th Int. Symp. Micromach. Human Sci., 39-43 (1995) |
| [9] | Alswaitti, M.; Albughdadi, M.; Isa, N. A.M., Density-based particle swarm optimization algorithm for data clustering, Expert Syst Appl, 91, 170-186 (2018) |
| [10] | Yadav, S.; Ekbal, A.; Saha, S., Feature selection for entity extraction from multiple biomedical corpora: a PSO-based approach, Soft Comput., 22, 6881-6904 (2018) |
| [11] | Jordehi, A. R., Particle swarm optimisation (PSO) for allocation of facts devices in electric transmission systems: a review, Renew. Sustain. Ener., 52, 1260-1267 (2015) |
| [12] | Pang, H.; Liu, F.; Xu, Z. R., Variable universe fuzzy control for vehicle semi-active suspension system with mr damper combining fuzzy neural network and particle swarm optimization, Neurocomputing, 306, 130-140 (2018) |
| [13] | Wang, F.; Zhu, H. Q.; Li, W.; Li, K. S., A hybrid convolution network for serial number recognition on banknotes, Inf Sci (Ny), 512, 952-963 (2020) |
| [14] | Zhang, H. W.; Xie, J. W.; Ge, J. A.; Lu, W. L.; Zong, B. F., An entropy-based pso for dar task schedulin problem, Appl. Soft Comput., 73, 862-873 (2018) |
| [15] | Wang, F.; Li, Y. X.; Zhou, A.; Tang, K., An estimation of distribution algorithm for mixed-variable newsvendor problems, IEEE Transactions on Evolutionary Computation (2019) |
| [16] | Belkadi, A.; Oulhadj, H.; Touati, Y.; Khan, S. A.; Daachi, B., On the robust pid adaptive controller for exoskeletons: a particle swarm optimization-based approach, Appl. Soft Comput., 60, 87-100 (2017) |
| [17] | Shi, Y.; Eberhart, R., A modified particle swarm optimizer, (1998 IEEE international conference on evolutionary computation proceedings (1998), IEEE world congress on computational intelligence), 69-73 |
| [18] | Ratnaweera, A.; Halgamuge, S. K.; Watson, H. C., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Trans. Evolut. Comput., 8, 3, 240-255 (2004) |
| [19] | Zhan, Z. H.; Zhang, J.; Li, Y.; Chung, H. S.H., Adaptive particle swarm optimization, IEEE Trans. Syst. Man Cybern. Part B Cybern., 39, 6, 1362-1381 (2009) |
| [20] | Chen, K.; Zhou, F. Y.; Liu, A. L., Chaotic dynamic weight particle swarm optimization for numerical function optimization, Knowl Based Syst, 139, 23-40 (2018) |
| [21] | Kennedy, J.; Mendes, R., Population structure and particle swarm performance, (Proceedings of IEEE Congress on Evolutionary Computation, 2 (2002), IEEE), 1671-1676 |
| [22] | Mendes, R.; Kennedy, J.; Neves, J., The fully informed particle swarm: simpler, maybe better, IEEE Trans. Evolut. Comput., 8, 3, 204-210 (2004) |
| [23] | Liang, J. J.; Qin, A.; Suganthan, P. N.; Baskar, S., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Trans. Evolut. Comput., 10, 3, 281-295 (2006) |
| [24] | Wang, F.; Zhang, H.; Li, K. S.; Lin, Z. Y.; Yang, J.; Shen, X. L., A hybrid particle swarm optimization algorithm using adaptive learning strategy, Inf Sci (Ny), 436-437, 162-177 (2018) |
| [25] | Liang, J.; Suganthan, P., Dynamic multi-swarm particle swarm optimizer, (Proceedings of IEEE Swarm Intelligence Symposium (2005)), 124-129 |
| [26] | Cao, L.; Xu, L.; Goodman, E. D., A neighbor-based learning particle swarm optimizer with short-term and long-term memory for dynamic optimization problems, Inf Sci (Ny), 453, 463-485 (2018) |
| [27] | Liu, H. R.; Cui, J. C.; Lu, Z. D.; Liu, D. Y.; Deng, Y. J., A hierarchical simple particle swarm optimization with mean dimensional information, Appl Soft Comput, 76, 712-725 (2019) |
| [28] | Deng, H.; Peng, L.; Zhang, H.; Yang, B.; Chen, Z. X., Ranking-based biased learning swarm optimizer for large-scale optimization, Inf Sci (Ny), 493, 120-137 (2019) · Zbl 1451.90172 |
| [29] | Yang, Q.; Chen, W. N.; Gu, T.; Zhang, H. X.; Deng, J. D.; Li, Y.; Zhang, J., Segment-based predominant learning swarm optimizer for large-scale optimization, IEEE Trans Cybern, 47, 9, 2896-2910 (2016) |
| [30] | Zhang, X.; Wang, X.; Kang, Q.; Cheng, J., Differential mutation and novel social learning particle swarm optimization algorithm, Inf Sci (Ny), 480, 109-129 (2019) |
| [31] | Meng, A.; Li, Z.; Yin, H.; Chen, S.; Guo, Z., Accelerating particle swarm optimization using crisscross search, Inf Sci (Ny), 329, 52-72 (2016) |
| [32] | Nasiraghdam, M.; Nafar, M., New approach based on hybrid ga and pso as hgapso in low-frequency oscillation damping using upfc controller, Journal of Basic and Applied Scientific Research, 1, 11, 2208-2218 (2011) |
| [33] | Gong, Y. J.; Li, J. J.; Zhou, Y. C.; Li, Y.; Chung, H. S.H.; Shi, Y. H., Genetic learning particle swarm optimization, IEEE Trans. Cybern., 46, 2277-2290 (2016) |
| [34] | Zhan, Z. H.; Zhang, J.; Li, Y.; Shi, Y. H., Orthogonal learning particle swarm optimization, IEEE Trans. Evol. Comput., 15, 6, 832-847 (2010) |
| [35] | Peram, T.; Veeramachaneni, K.; Mohan, C. K., Fitness-distance-ratio based particle swarm optimization, (Swarm Intelligence Symposium, 2003. SIS ’03. Proceedings of the 2003 IEEE (2003)), 174-181 |
| [36] | Shi, Y.; Eberhart, R., Parameter selection in particle swarm optimization, Evolutionary Programming VII, 591-600 (1998), Springer: Springer Berlin, Heidelberg |
| [37] | Janson, S.; Middendorf, M., A hierarchical particle swarm optimizer and its adaptive variant, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 35, 6, 1272-1282 (2005) |
| [38] | Cheng, R.; Jin, Y., A competitive swarm optimizer for large scale optimization, IEEE Trans. Cybern., 45, 2, 191-204 (2015) |
| [39] | Wang, F.; Li, Y. X.; Zhang, H.; Hu, T.; Shen, X. L., An adaptive weight vector guided evolutionary algorithm for preference-based multi-objective optimization, Swarm & Evolutionary Computation, 49, 220-233 (2019) |
| [40] | Yazdani, D.; Nasiri, B.; Sepas-Moghaddam, A.; Meybodi, M. R., A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization, Appl Soft Comput, 13, 4, 2144-2158 (2013) |
| [41] | Liu, R.; Li, J.; Mu, C.; Jiao, L., A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization, Eur J Oper Res, 261, 3, 1028-1051 (2017) · Zbl 1403.90611 |
| [42] | vandenBergh, F.; Engelbrecht, A. P., A cooperative approach to particle swarm optimization, IEEE Trans. Evol. Comput., 8, 3, 225-239 (2004) |
| [43] | Xu, G.; Cui, Q.; Shi, X.; Ge, H.; Zhan, Z. H.; Lee, H. P.; Wu, C., Particle swarm optimization based on dimensional learning strategy, Swarm Evol Comput, 45, 33-51 (2019) |
| [44] | Wang, F.; Zhang, H.; Li, Y. X.; Zhao, Y. Y.; Rao, Q., External archive matching strategy for moea/d, Soft Comput, 22, 23, 7833-7846 (2018) |
| [45] | Nagra, A. A.; Han, F.; Ling, Q. H.; Mehta, S., An improved hybrid method combining gravitational search algorithm with dynamic multi swarm particle swarm optimization, IEEE Access, 7, 50388-50399 (2019) |
| [46] | Cao, Y.; Zhang, H.; Li, W.; Zhou, M.; Zhang, Y.; Chaovalitwongse, W. A., Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions, IEEE Transactions on Evolutionary Computation, 23, 718-731 (2018) |
| [47] | Cheng, R.; Jin, Y., A social learning particle swarm optimization algorithm for scalable optimization, Inf Sci (Ny), 291, 43-60 (2015) · Zbl 1355.90108 |
| [48] | Chen, X.; Tianfield, H.; Mei, C.; Du, W.; Liu, G., Biogeography-based learning particle swarm optimization, Soft Comput, 21, 24, 7519-7541 (2017) |
| [49] | Suganthan, P. N.; Hansen, N.; Liang, J. J.; Deb, K., Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization, Proc. IEEE Congr. Evol. Comput, 1-50 (2005) |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
