Combination of Global and Local Search for Real Function Optimization

Authors:
Xinsheng Lai
Affiliations:
Department of mathematics and computer, Shangrao Normal university, Shangrao, China
Venue:
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Year:
2008

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Abstract

This article introduces a global optimizer based on combination of global and local search (OBCGL). OBCGL contains two populations, one for global search and the other for local search. OBCGL utilizes hill climbing method to select new individuals to form new generation instead of selection methods used in Genetic Algorithms (GAs). The algorithm's performance was studied using a test bed of real-valued functions with different degree of multi-modality. In all cases studied, we found that with two populations OBCGL works well.