Abstract
This paper presents a new adaptive strategy for combining global (exploration) and local (exploitation) search capabilities of a multi-objective particle swarm optimizer (MOPSO).The goal of hybridization of search strategies is to enhance an optimizer’s overall performance. In contrast to previous attempts at hybridization, the proposed methodology efficiently balances exploration and exploitation of the search space using the two novel methods of intersection test and objective function normalization. Experimental results obtained from several well-known test cases demonstrate the efficiency of the proposed MOPSO algorithm. The results are compared with those obtained from NSGA-II, which is a well-established evolutionary algorithm.
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Ma, L., Forouraghi, B. (2011). A Hybrid Search Strategy to Enhance Multiple Objective Optimization. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21827-9_31
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DOI: https://doi.org/10.1007/978-3-642-21827-9_31
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