Abstract
Differential evolution has the characteristics of fast convergence, less parameters, and ease of implementation. This paper proposes an enhanced DE using the local search for multi-objective optimization, which is called DEMOLS. In DEMOLS, two candidate mutation variants are randomly chosen to enhance the search ability by taking their advantages and strengths and two local search mechanisms are designed to improve the ability of local adjustment. Numerical experiments are performed on a set of multi-objective optimization problems, and the experimental results show that DEMOLS has the ability to solve multi-objective optimization problems.
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Ao, Y. (2012). Differential Evolution Using Local Search for Multi-objective Optimization. In: Jin, D., Lin, S. (eds) Advances in Electronic Engineering, Communication and Management Vol.2. Lecture Notes in Electrical Engineering, vol 140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27296-7_16
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DOI: https://doi.org/10.1007/978-3-642-27296-7_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27295-0
Online ISBN: 978-3-642-27296-7
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