Experimental Study of a Structured Differential Evolution with Mixed Strategies

Takashi Ishimizu; Kiyoharu Tagawa

doi:10.20965/jaciii.2011.p1310

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JACIII Vol.15 No.9 pp. 1310-1319

doi: 10.20965/jaciii.2011.p1310

(2011)

Paper:

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Experimental Study of a Structured Differential Evolution with Mixed Strategies

Takashi Ishimizu and Kiyoharu Tagawa

School of Science and Engineering, Kinki University, 3-4-1 Kowakae, Higashi-Osaka 577-8502, Japan

Received:

May 29, 2011

Accepted:

September 19, 2011

Published:

November 20, 2011

Keywords:

evolutionary algorithm, differential evolution, structured differential evolution

Abstract

In this paper, a new Differential Evolution (DE) that has multiple populations, or islands, is proposed. The proposed DE is called Structured Differential Evolution (StDE). In order to generate a new individual from the current population, various characteristic strategies have been proposed for DE. However, the performances of these strategies depend on the kind of the optimization problem. The proposed StDE uses different strategies in respective islands. Therefore, it can be expected that the proposed StDE is effective for a wide range of optimization problems. Although various networks topologies among islands are reported for island-based evolutionary algorithms, the most popular ones, namely the ring network and the torus network, are employed by StDE. Furthermore, in order to enhance the performance of proposed StDE, various migration policies are examined in two kinds of networks though a variety of benchmark problems.

Cite this article as:

T. Ishimizu and K. Tagawa, “Experimental Study of a Structured Differential Evolution with Mixed Strategies,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.9, pp. 1310-1319, 2011.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] R. Storn and K. Price, “Differential evolution – a simple and efficient heuristic for global optimization over steady-state space,” J. of Global Optimization, Vol.11, No.4, pp. 341-359, 1997,

[2] [2] S. Das and P. N. Suganthan, “Differential evolution: a survey of the state-of-the art,” IEEE Trans. on Evolutionary Computation, Vol.15, No.1, pp. 4-31. 2011.

[3] [3] J. Vesterstrom and R. Thomson, “A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems,” Proc. of IEEE Congress on Evolutionary Computation, pp. 1980-1987, 2004.

[4] [4] K. V. Price, R. M. Storn, and J. A. Lampinen, “Differential Evolution – A Practical Approach to Global Optimization,” Springer, 2005.

[5] [5] K. Tagawa, “Multi-objective optimum design of balanced SAW filters using generalized differential evolution,” WSEAS Trans. on System, Issue 8, Vol.8, pp. 923-932, 2009.

[6] [6] G. Syswerda, “A study of reproduction in generational and steadystate genetic algorithms,” Foundations of Genetic Algorithms 2, Morgan Kaufmann Publ., pp. 94-101, 1991.

[7] [7] K. Tagawa, “A statistical study of the differential evolution based on continuous generation model,” Proc. of IEEE Congress on Evolutionary Computation, pp. 2614-2621, 2009.

[8] [8] R. Thomsen, “Multimodal optimization using crowding-based differential evolution,” Proc. of IEEE Congress on Evolutionary Computation, pp. 1382-1389, 2004.

[9] [9] V. Feoktistov, “Differential Evolution in Search Solutions,” Chapter 6, Springer, 2006.

[10] [10] E. Alba and M. Tomassini, “Parallelism and evolutionary algorithms,” IEEE Trans. on Evolutionary Computation, Vol.6, No.5, pp. 443-462, 2002.

[11] [11] D. Zaharie and D. Petcu, “Parallel implementation of multipopulation differential evolution,” Concurrent Information Processing and Computing, ISO Press, pp. 223-232. 2005.

[12] [12] D. K. Tasoulis, N .G. Pavlidis, V. P. Plagianakos, and M. N. Vrahatis, “Parallel differential evolution,” Proc. of the IEEE Congress on Evolutionary Computation, pp. 2023-2029, 2004.

[13] [13] L. J. Eshelman and J. D. Schaffer, “Real-coded genetic algorithms and interval-schemata,” Foundations of Genetic Algorithms 2, Morgan Kaufmann Publ., pp. 187-202, 1993.

[14] [14] L. G. Valiant, “A bridging model for parallel computation,” Communication of the ACM, Vol.33, No.8, pp. 103-111, 1990.

[15] [15] F. Dehne, A. Fabri, and A. Rau-Chapman, “Scalable parallel computational geometry for coarse grained multicomputers,” Proc. of ACM Symposium on Computational Geometry, pp. 298-307, 1993.