Cooperative Coevolutionary Particle Swarms using Fuzzy Logic for Large Scale Optimization
Keywords:Adaptive inertia weight, Cooperative coevolutionary, Fuzzy logic, Particle Swarm Optimization
A cooperative coevolutionary framework can improve the performance of optimization algorithms on large-scale problems. In this paper, we propose a new Cooperative Coevolutionary Coevolutionary algorithm based on our preliminary work FuzzyPSO2. This new proposal, called CCFPSO, uses a variable decomposition method, adopting the random grouping technique and a dynamic subcomponent size at each generation. Unlike FuzzyPSO2, in CCFPSO the re-initialization of the variables suggested by the fuzzy system is performed on the particles that has the worst fitness value in each generation. Moreover, the particles are updated based on their best position and its neighborhoods, instead of the best particle in the population as its standard version. On high-dimensional problems that more closely resemble real-world problems (CEC2008, CEC2010) the performance of CCFPSO is favorable compared to other state-of-the-art PSO versions such as CCPSO2, SLPSO and CSO. The results indicate that using a Cooperative Coevolutionary PSO approach with a fuzzy logic system can improve results on high dimensionality problems (100 to 1000 variables).
K. Tang, X. Y´ao, P. N. Suganthan, C. MacNish, Y.-P. Chen, C.-M. Chen, and Z. Yang, “Benchmark functions for the cec’2008 special session and competition on large scale global optimization,” Nature inspired computation and applications laboratory, USTC, China, vol. 24, pp. 1–18, 2007.
K. Tang, X. Li, P. N. Suganthan, Z. Yang, and T. Weise, “Benchmark functions for the cec’2010 special session and competition on large-scale global optimization,” tech. rep., Nature Inspired Computation and Applications Laboratory, 2009.
L. Li, W. Fang, Y. Mei, and Q. Wang, “Cooperative coevolution for large-scale global optimization based on fuzzy decomposition,” Soft Computing, vol. 25, no. 5, pp. 3593–3608, 2021.
J. H. Holland, “Genetic algorithms,” Scientific american, vol. 267, no. 1, pp. 66–73, 1992.
R. Storn and K. Price, “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,” Journal of global optimization, vol. 11, no. 4, pp. 341–359, 1997.
R. Eberhart and J. Kennedy, “Particle swarm optimization,” in Proceedings of the IEEE international conference on neural networks, vol. 4, pp. 1942–1948, Citeseer, 1995.
J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95-international conference on neural networks, vol. 4, pp. 1942–1948, IEEE, 1995.
M. Dorigo, V.Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 26, no. 1, pp. 29–41, 1996.
L. Cui, G. Li, Y. Luo, F. Chen, Z. Ming, N. Lu, and J. Lu, “An enhanced artificial bee colony algorithm with dual-population framework,” Swarm and Evolutionary Computation, vol. 43, pp. 184–206, 2018.
M. N. Omidvar, M. Yang, Y. Mei, X. Li, and X. Yao, “Dg2: A faster and more accurate differential grouping for large-scale black-box optimization,” IEEE Transactions on Evolutionary Computation, vol. 21, no. 6, pp. 929–942, 2017.
J.-R. Jian, Z.-H. Zhan, and J. Zhang, “Large-scale evolutionary optimization: a survey and experimental comparative study,” International Journal of Machine Learning and Cybernetics, vol. 11, no. 3, pp. 729–745, 2020.
M. A. Potter and K. A. De Jong, “A cooperative coevolutionary approach to function optimization,” in International Conference on Parallel Problem Solving from Nature, pp. 249–257, Springer, 1994.
Z. Yang, K. Tang, and X. Yao, “Large scale evolutionary optimization using cooperative coevolution,” Information sciences, vol. 178, no. 15, pp. 2985–2999, 2008.
Z. Yang, K. Tang, and X. Yao, “Multilevel cooperative coevolution for large scale optimization,” in 2008 IEEE congress on evolutionary computation (IEEE World Congress on Computational Intelligence), pp. 1663–1670, IEEE, 2008.
X. Li and X. Yao, “Cooperatively coevolving particle swarms for large scale optimization,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 2, pp. 210–224, 2011.
M. N. Omidvar, X. Li, and X. Yao, “Cooperative co-evolution with delta grouping for large scale non-separable function optimization,” in IEEE congress on evolutionary computation, pp. 1–8, IEEE, 2010.
M. N. Omidvar, X. Li, Y. Mei, and X. Yao, “Cooperative co-evolution with differential grouping for large scale optimization,” IEEE Transactions on evolutionary computation, vol. 18, no. 3, pp. 378–393, 2013.
R. Cheng and Y. Jin, “A social learning particle swarm optimization algorithm for scalable optimization,” Information Sciences, vol. 291, pp. 43–60, 2015.
R. Cheng and Y. Jin, “A competitive swarm optimizer for large scale optimization,” IEEE transactions on cybernetics, vol. 45, no. 2, pp. 191–204, 2014.
J.-i. Kushida, A. Hara, and T. Takahama, “Rank-based differential evolution with multiple mutation strategies for large scale global optimization,” in 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 353–360, IEEE, 2015.
F. Valdez, P. Melin, and O. Castillo, “A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation,” Expert systems with applications, vol. 41, no. 14, pp. 6459–6466, 2014.
F. Olivas and O. Castillo, “Particle swarm optimization with dynamic parameter adaptation using fuzzy logic for benchmark mathematical functions,” in Recent Advances on Hybrid Intelligent Systems, pp. 247–258, Springer, 2013.
F. Olivas, F. Valdez, and O. Castillo, “Particle swarm optimization with dynamic parameter adaptation using interval type-2 fuzzy logic for benchmark mathematical functions,” in 2013 World Congress on Nature and Biologically Inspired Computing, pp. 36–40, IEEE, 2013.
F. Paz, G. Leguizam´on, and E. Mezura-Montes, “Particle swarm optimization with adaptive inertia weight using fuzzy logic for large-scale problems,” in XXVI Congreso Argentino de Ciencias de la Computación (CACIC)(Modalidad virtual, 5 al 9 de octubre de 2020), 2020.
F. Valdez, J. C. Vazquez, P. Melin, and O. Castillo, “Comparative study of the use of fuzzy logic in improving particle swarm optimization variants for mathematical functions using co-evolution,” Applied Soft Computing, vol. 52, pp. 1070–1083, 2017.
J. Kennedy and R. Mendes, “Population structure and particle swarm performance,” in Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No. 02TH8600), vol. 2, pp. 1671–1676, IEEE, 2002.
Y. Shi and R. C. Eberhart, “Parameter selection in particle swarm optimization,” in International conference on evolutionary programming, pp. 591–600, Springer, 1998.
M. R. Ullmann, K. F. Pimentel, L. A. de Melo, G. da Cruz, and C. Vinhal, “Comparison of pso variants applied to large scale optimization problems,” in 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp. 1–6, IEEE, 2017.
R. C. Eberhart, Y. Shi, and J. Kennedy, Swarm intelligence. Elsevier, 2001.
F. Valdez, P. Melin, and O. Castillo, “An improved evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms,” Applied Soft Computing, vol. 11, no. 2, pp. 2625–2632, 2011.
J. Perez, F. Valdez, O. Castillo, P. Melin, C. Gonzalez, and G. Martinez, “Interval type-2 fuzzy logic for dynamic parameter adaptation in the bat algorithm,” Soft Computing, vol. 21, no. 3, pp. 667–685, 2017.
A. Sombra, F. Valdez, P. Melin, and O. Castillo, “A new gravitational search algorithm using fuzzy logic to parameter adaptation,” in 2013 IEEE congress on evolutionary computation, pp. 1068–1074, IEEE, 2013.
M. S. Norouzzadeh, M. R. Ahmadzadeh, and M. Palhang, “Ladpso: using fuzzy logic to conduct pso algorithm,” Applied Intelligence, vol. 37, no. 2, pp. 290–304, 2012.
P. Ochoa, O. Castillo, and J. Soria, “Differential evolution using fuzzy logic and a comparative study with other metaheuristics,” in Nature-inspired design of hybrid intelligent systems, pp. 257–268, Springer, 2017.
S. Kumar and D. Chaturvedi, “Tuning of particle swarm optimization parameter using fuzzy logic,” in 2011 International Conference on Communication Systems and Network Technologies, pp. 174–179, IEEE, 2011.
F. Van den Bergh and A. P. Engelbrecht, “A cooperative approach to particle swarm optimization,” IEEE transactions on evolutionary computation, vol. 8, no. 3, pp. 225–239, 2004.
Y. Sun, M. Kirley, and S. K. Halgamuge, “Extended differential grouping for large scale global optimization with direct and indirect variable interactions,” in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 313–320, 2015.
Y. Mei, M. N. Omidvar, X. Li, and X. Yao, “A competitive divide-and-conquer algorithm for unconstrained large-scale black-box optimization,” ACM Transactions on Mathematical Software (TOMS), vol. 42, no. 2, pp. 1–24, 2016.
Y. Sun, X. Li, A. Ernst, and M. N. Omidvar, “Decomposition for large-scale optimization problems with overlapping components,” in 2019 IEEE congress on evolutionary computation (CEC), pp. 326–333, IEEE, 2019.
M. A. Meselhi, S. M. Elsayed, R. A. Sarker, and D. L. Essam, “Contribution based co-evolutionary algorithm for large-scale optimization problems,” IEEE Access, vol. 8, pp. 203369–203381, 2020.
M. Clerc, “The swarm and the queen: towards a deterministic and adaptive particle swarm optimization,” in Proceedings of the 1999 congress on evolutionary computation-CEC99, pp. 1951–1957, IEEE, 1999.
F. Olivas, F. Valdez, O. Castillo, C. I. Gonzalez, G. Martinez, and P. Melin, “Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems,” Applied Soft Computing, vol. 53, pp. 74–87, 2017.
S.-Z. Zhao, J. J. Liang, P. N. Suganthan, and M. F. Tasgetiren, “Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization,” in 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence), pp. 3845–3852, IEEE, 2008.
J. Derrac, S. Garc´ıa, D. Molina, and F. Herrera, “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms,” Swarm and Evolutionary Computation, vol. 1, no. 1, pp. 3–18, 2011.
M. L´opez-Ib´anez, J. Dubois-Lacoste, L. P. C´aceres, M. Birattari, and T. St ¨utzle, “The irace package: Iterated racing for automatic algorithm configuration,” Operations Research Perspectives, vol. 3, pp. 43–58, 2016.
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Copyright (c) 2021 Fabiola Patricia Paz, Guillermo Leguizamón, Efrén Mezura-Montes
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