Abstract
In this paper we introduce the multi-agent heuristic procedure to solve multi-objective optimization problems. To diminish the drawbacks of the evolutionary search, an island model is used to involve various genetic algorithms which are based on different concepts (NSGA-II, SPEA2, and PICEA-g). The main benefit of our proposal is that it does not require additional experiments to expose the most appropriate algorithm for the problem considered. For most of the test problems the effectiveness of the developed algorithmic scheme is comparable with (or even better than) the performance of its component which provides the best results separately. Owing to the parallel work of island model components we have managed to decrease computational time significantly (approximately by a factor of 2.7).
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Brester, C., Semenkin, E. (2015). Cooperative Multi-objective Genetic Algorithm with Parallel Implementation. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9140. Springer, Cham. https://doi.org/10.1007/978-3-319-20466-6_49
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DOI: https://doi.org/10.1007/978-3-319-20466-6_49
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