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Competition-Based Distributed Differential Evolution

Published: 08 July 2018 Publication History

Abstract

Differential evolution (DE) is a simple and efficient evolutionary algorithm for global optimization. In distributed differential evolution (DDE), the population is divided into several sub-populations and each sub-population evolves independently for enhancing algorithmic performance. Through sharing elite individuals between sub-populations, effective information is spread. However, the information exchanged through individuals is still too limited. To address this issue, a competition-based strategy is proposed in this paper to achieve comprehensive interaction between sub-populations. Two operators named opposition-invasion and cross-invasion are designed to realize the invasion from good performing sub-populations to bad performing subpopulations. By utilizing opposite invading sub-population, the search efficiency at promising regions is improved by opposition-invasion. In cross-invasion, information from both invading and invaded sub-populations is combined and population diversity is maintained. Moreover, the proposed algorithm is implemented in a parallel master-slave manner. Extensive experiments are conducted on 15 widely used large-scale benchmark functions. Experimental results demonstrate that the proposed competition-based DDE (DDE-CB) could achieve competitive or even better performance compared with several state-of-the-art DDE algorithms. The effect of proposed competition-based strategy cooperation with well-known DDE variants is also verified.

References

[1]
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.
[2]
W.-J. Yu, M. Shen, W.-N. Chen, Z.-H. Zhan, Y.-J. Gong, Y. Lin, O. Liu, and J. Zhang, “Differential evolution with two-level parameter adaptation”, IEEE Transactions on Cybernetics, vol. 44, no. 7, pp. 1080–1099, 2014.
[3]
Yu, W.-J. Ji, J.-Y. Gong, Z.-J. Yang, Z. and J. Zhang, “A tri-objective differential evolution approach for multimodal optimization”, Information Sciences, vol. 423, pp. 1–23, 2018.
[4]
R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory”, in 1995 International Symposium on Micro Machine and Human Science, vol. 1, 1995, pp. 39–43.
[5]
J. Kennedy and R. C. Eberhart, “Particle swarm optimization”, in 1995 IEEE International Conference on Neural Networks. IEEE, 1995, pp. 1942–1948.
[6]
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.
[7]
Juang, C.-F. Jeng, T.-L. and Y.-C. Chang, “An interpretable fuzzy system learned through online rule generation and multiobjective aco with a mobile robot control application”, IEEE Transactions on Cybernetics, vol. 46, no. 12, pp. 2706–2718, 2015.
[8]
B. Xue, M. Zhang, and W. N. Browne, “Particle swarm optimization for feature selection in classification: a multi-objective approach”, IEEE Transactions on Cybernetics, vol. 43, no. 6, pp. 1656–1671, 2013.
[9]
N. García-Pedrajas, C. Hervás-Martínez, and J. Muñoz-Pérez, “Covnet: a cooperative coevolutionary model for evolving artificial neural networks”, IEEE Transactions on Neural Networks, vol. 14, no. 3, pp. 575–596, 2003.
[10]
S. Jiang and S. Yang, “An improved multiobjective optimization evolutionary algorithm based on decomposition for complex pareto fronts”, IEEE Transactions on Cybernetics, vol. 46, no. 2, pp. 421–437, 2016.
[11]
D. K. Tasoulis, N. G. Pavlidis, V. P. Plagianakos, and M. N. Vrahatis, “Parallel differential evolution”, in 2004 IEEE Congress on Evolutionary Computation, vol. 2. IEEE, 2004, pp. 2023–2029.
[12]
G. Wu, R. Mallipeddi, P. Suganthan, R. Wang, and H. Chen, “Differential evolution with multi-population based ensemble of mutation strategies”, Information Sciences, vol. 329, pp. 329–345, 2016.
[13]
I. De Falco, A. Della Cioppa, D. Maisto, U. Scafuri, and E. Tarantino, “Satellite image registration by distributed differential evolution”, in Applications of evolutionary computing. Springer, 2007, pp. 251–260.
[14]
Y.-F. Ge, W.-J. Yu, Y. Lin, Y.-J. Gong, Z.-H. Zhan, W.-N. Chen, and J. Zhang, “Distributed differential evolution based on adaptive mergence and split for large-scale optimization”, IEEE transactions on cybernetics, 2017. https://doi.org/10.1109/TCYB.2017.2728725.
[15]
J. Cheng, G. Zhang, and F. Neri, “Enhancing distributed differential evolution with multicultural migration for global numerical optimization”, Information Sciences, vol. 247, pp. 72–93, 2013.
[16]
K. Kozlov and A. Samsonov, “New migration scheme for parallel differential evolution”, in 2006 International Conference on Bioinformatics of Genome Regulation and Structure, 2006, pp. 141–144.
[17]
Ge, Y.-F. Yu, W.-J. Li, J.-J. Yu, Z.-W. and J. Zhang, “Enhancing distributed differential evolution with a space-driven topology”, in 2016 IEEE Congress on Evolutionary Computation. IEEE, 2016, pp. 4090–4095.
[18]
A. Zamuda, J. Brest, and E. Mczura-Montes, “Structured population size reduction differential evolution with multiple mutation strategies on cec 2013 real parameter optimization”, in 2013 IEEE Congress on Evolutionary Computation. IEEE, 2013, pp. 1925–1931.
[19]
M. Weber, F. Neri, and V. Tirronen, “Shuffle or update parallel differential evolution for large-scale optimization”, Soft Computing, vol. 15, no. 11, pp. 2089–2107, 2011.
[20]
I. De Falco, A. Della Cioppa, D. Maisto, U. Scafuri, and E. Tarantino, “Biological invasion-inspired migration in distributed evolutionary algorithms”, Information Sciences, vol. 207, pp. 50–65, 2012.
[21]
J. Apolloni, J. Garda-Nieto, G. Leguizamn, and E. Alba, “Island based distributed differential evolution: an experimental study on hybrid testbeds”, in 2008 International Conference on Hybrid Intelligent Systems. IEEE, 2008, pp. 696–701.
[22]
D. Izzo, M. Ruciñski, and C. Ampatzis, “Parallel global optimisation meta-heuristics using an asynchronous island-model”, in 2009 IEEE Congress on Evolutionary Computation. IEEE, 2009, pp. 2301–2308.
[23]
M. Weber, F. Neri, and V. Tirronen, “Distributed differential evolution with explorative-exploitative population families”, Genetic Programming and Evolvable Machines, vol. 10, no. 4, pp. 343–371, 2009.
[24]
S. Rahnamayan, H. R. Tizhoosh, and M. M. Salama, “Opposition-based differential evolution”, IEEE Transactions on Evolutionary computation, vol. 12, no. 1, pp. 64–79, 2008.
[25]
X. Li, K. Tang, M. N. Omidvar, Z. Yang, and K. Qin, “Benchmark functions for the cec 2013 special session and competition on large-scale global optimization”, Gene, vol. 7, no. 33, p. 8, 2013.

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cover image Guide Proceedings
2018 IEEE Congress on Evolutionary Computation (CEC)
Jul 2018
2623 pages

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IEEE Press

Publication History

Published: 08 July 2018

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