Abstract
This chapter proposes a novel adaptive memetic approach for solving multi-objective optimization problems. The proposed approach introduces the novel concept of crossdominance and employs this concept within a novel probabilistic scheme which makes use of the Wigner distribution for performing coordination of the local search. Thus, two local searchers are integrated within an evolutionary framework which resorts to an evolutionary algorithm previously proposed in literature for solving multi-objective problems. These two local searchers are a multi-objective version of simulated annealing and a novel multi-objective implementation of the Rosenbrock algorithm.
Numerical results show that the proposed algorithm is rather promising and, for several test problems, outperforms two popular meta-heuristics present in literature. A realworld application in the field of electrical engineering, the design of a control system of an electric motor, is also shown. The application of the proposed algorithm leads to a solution which allows successful control of a direct current motor by simultaneously handling the conflicting objectives of the dynamic response.
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Caponio, A., Neri, F. (2009). Integrating Cross-Dominance Adaptation in Multi-Objective Memetic Algorithms. In: Goh, CK., Ong, YS., Tan, K.C. (eds) Multi-Objective Memetic Algorithms. Studies in Computational Intelligence, vol 171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88051-6_15
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