Abstract
In recent decades, solving multi-modal optimization problem has attracted many researchers attention in evolutionary computation community. Multi-modal optimization refers to locating not only one optimum but also the entire set of optima in the search space. To locate multiple optima in parallel, many niching techniques are proposed and incorporated into evolutionary algorithms in literature. In this paper, a local search technique is proposed and integrated with the existing Fitness Euclidean-distance Ratio PSO (FER-PSO) to enhance its fine search ability or the ability to identify multiple optima. The algorithm is tested on 8 commonly used benchmark functions and compared with the original FER-PSO as well as a number of multi-modal optimization algorithms in literature. The experimental results suggest that the proposed technique not only increases the probability of finding both global and local optima but also speeds up the searching process to reduce the average number of function evaluations.
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Liang, J.J., Qu, B.Y., Ma, S.T., Suganthan, P.N. (2012). Memetic Fitness Euclidean-Distance Particle Swarm Optimization for Multi-modal Optimization. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_50
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DOI: https://doi.org/10.1007/978-3-642-24553-4_50
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