Niching

The interest in multimodal optimization methods is increasing in the last years. The objective is to find multiple solutions that allow the expert to choose the solution that better adapts to the actual conditions. Niching methods extend genetic algorithms to domains that require the identification of multiple solutions. There are different niching genetic algorithms: sharing, clearing, crowding and sequential, etc.

Abstract. In some problems of mathematics there are many functions which have several optimum points which all, should be computed. The method NichePSO is previously designed to accomplish this. This paper presents a new memetic-based scheme to enhance the NichePSO. This work utilizes a powerful soft computing tool namely Learning Automata as the local search algorithm in our proposed memetic approach. Numerical results demonstrate the superiority of the proposed method over the original NichePSO in terms of convergence and diversity.

Abstract. Multimodal optimization is used to find multiple global & local optima which is very useful in many real world optimization problems. But often evolutionary algorithms fail to locate multiple optima as required by the system. Also they fail to store those optima by themselves. So we have to use other selection scheme that can detect & store multiple optima along with evolutionary algorithms. Hence we use niching which is a very powerful tool in detecting & storing multiple optima. Niching methods were introduced to EAs to allow maintenance of a population of diverse individuals so that multiple optima within a single population can be located.Crowding which is a very primitive branch of niching is used here as the selection scheme with Invasive Weed Optimization (IWO) which is a ecologically inspired algorithms depicting behaviors of plants. For multimodal optimization the total search space is divided into several niches in which separately IWO is applied to find the opti...