Niching evolutionary algorithms for multimodal and dynamic optimization

Abstract

Many optimization functions have complex landscapes with multiple global or local optima. In order to solve such problems, niching evolutionary algorithms were introduced. The “niching” concept in evolutionary algorithms was brought from the ecological “niches”. It describes the roles that different individuals take when there are several optima to pursue. Niching gives growth to diverse promising species in the population, making it possible to locate multiple optima in a multimodal landscape. In this thesis, a literature review on evolutionary algorithms and several classes of niching methods is presented. After that, a simulation-based comparative study is carried out using hybrid composition test functions with multiple global optima. Three popular niching techniques with binary genetic algorithms are examined for their searching ability, accuracy and computation speed in solving the hybrid composition problems. The number of functions evaluations is employed as the main performance measure. It has been observed that the performance of the niching methods varies with problems, while methods that belong to the same class have shared characteristics.