Abstract
In recent years many efficient nature-inspired techniques (based on evolutionary strategies, particle swarm optimization, differential evolution and others) have been proposed for real-valued multimodal optimization (MMO) problems. Unfortunately, there is a lack of efficient approaches for problems with binary representation. Existing techniques are usually based on general ideas of niching. Moreover, there exists the problem of choosing a suitable algorithm and fine tuning it for a certain problem. In this study, an approach based on a metaheuristic for designing multi-strategy genetic algorithm is proposed. The approach controls the interactions of many MMO techniques (different genetic algorithms) and leads to the self-configuring solving of problems with a priori unknown structure. The results of numerical experiments for benchmark problems from the CEC competition on MMO are presented. The proposed approach has demonstrated efficiency better than standard niching techniques and comparable to advanced algorithms. The main feature of the approach is that it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.
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References
Das, S., Maity, S., Qub, B.-Y., Suganthan, P.N.: Real-parameter evolutionary multimodal optimization: a survey of the state-of-the art. Swarm Evol. Comput. 1, 71–88 (2011)
Preuss, M.: Tutorial on multimodal optimization. In: The 13th International Conference on Parallel Problem Solving from Nature, PPSN 2014, Ljubljana, Slovenia (2014)
Liu, Y., Ling, X., Shi, Zh., Lv, M., Fang. J., Zhang, L.: A survey on particle swarm optimization algorithms for multimodal function optimization. J. Softw. 6(12), 2449–2455 (2011)
Deb, K., Saha, A.: Finding multiple solutions for multimodal optimization problems using a multi-objective evolutionary approach. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO 2010, pp. 447–454 (2010)
Li, X., Engelbrecht, A., Epitropakis, M.: Results of the 2013 IEEE CEC competition on niching methods for multimodal optimization. In: Report presented at 2013 IEEE Congress on Evolutionary Computation Competition on: Niching Methods for Multimodal Optimization (2013)
Bessaou, M., Petrowski, A., Siarry, P.: Island model cooperating with speciation for multimodal optimization. Parallel Problem Solving from Nature PPSN VI, Lecture Notes in Computer Science, vol. 1917. pp. 437–446 (2000)
Yu, E.L., Suganthan, P.N.: Ensemble of niching algorithms. Inf. Sci. 180(15), 2815–2833 (2010)
Qu, B., Liang, J., Suganthan P.N., Chen, T.: Ensemble of Clearing Differential Evolution for Multi-modal Optimization. Advances in Swarm Intelligence Lecture Notes in Computer Science, vol. 7331. pp. 350–357 (2012)
Sopov, E.: A Self-configuring Metaheuristic for Control of Multi-Strategy Evolutionary Search. ICSI-CCI 2015, Part III, LNCS 9142. pp. 29–37 (2015)
Singh, G., Deb, K.: Comparison of multi-modal optimization algorithms based on evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, Seattle, pp. 1305–1312 (2006)
Preuss, M., Wessing, S.: Measuring multimodal optimization solution sets with a view to multiobjective techniques. In: EVOLVE—A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV. AISC, vol. 227, pp. 123–137. Springer, Heidelberg (2013)
Preuss, M., Stoean, C., Stoean, R.: Niching foundations: basin identification on fixed-property generated landscapes. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011. pp. 837–844 (2011)
Li, X., Engelbrecht, A., Epitropakis, M.G.: Benchmark functions for CEC’2013 special session and competition on niching methods for multimodal function optimization. Evolutionary Computation, Machine Learning Group, RMIT University, Melbourne, Australia. Technical Report (2013)
Semenkin, E.S., Semenkina, M.E.: Self-configuring Genetic Algorithm with Modified Uniform Crossover Operator. Advances in Swarm Intelligence. Lecture Notes in Computer Science, vol. 7331. Springer, Berlin Heidelberg. pp. 414–421 (2012)
Molina, D., Puris, A., Bello, R., Herrera, F.: Variable mesh optimization for the 2013 CEC special session niching methods for multimodal optimization. In: Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC’13), pp. 87–94 (2013)
Epitropakis, M.G., Li, X., Burke, E.K.: A dynamic archive niching differential evolution algorithm for multimodal optimization. In: Proceeding of 2013 IEEE Congress on Evolutionary Computation (CEC’13), pp. 79–86 (2013)
Bandaru, S., Deb, K.: A parameterless-niching-assisted bi-objective approach to multimodal optimization. In: Proceedings of 2013 IEEE Congress on Evolutionary Computation (CEC’13), pp. 95–102 (2013)
Acknowledgements
The research was supported by President of the Russian Federation grant (MK-3285.2015.9). The author expresses his gratitude to Mr. Ashley Whitfield for his efforts to improve the text of this article.
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Sopov, E. (2016). A Self-configuring Multi-strategy Multimodal Genetic Algorithm. In: Pillay, N., Engelbrecht, A., Abraham, A., du Plessis, M., Snášel, V., Muda, A. (eds) Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-319-27400-3_2
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DOI: https://doi.org/10.1007/978-3-319-27400-3_2
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