Variable mesh optimization for the 2013 CEC Special Session Niching Methods for Multimodal Optimization

Abstract

Many real-world problems have several optima, and the aim of niching optimisation algorithms is to obtain the different global optima, and not only the best solution. One common technique to create niches is the clearing method that removes solutions too close to better ones. Unfortunately, clearing is very sensitive to the niche radius, and its right value depends on the problem (in real-world problems the minimum distance between optima is unknown). In this work we propose a niching algorithm that uses clearing with an adaptive niche radius, that decreases during the run. The proposal uses an external memory that stores current global optima to avoid losing found optima during the clearing process, allowing a non-elitist search. This algorithm applies this clearing method to a mesh of solutions, expanded by the generation of nodes using combination methods between the nodes, their best neighbour, and their nearest current global optima in the population (current global optima are nodes with fitness very similar to current best fitness). The proposal is tested on the competition benchmark proposed in the Special Session Niching Methods for Multimodal Optimization, and compared with other algorithms. The proposal obtains very good results detecting global optima. In comparisons with other algorithm, this proposal obtains the best results, proving to be a very competitive niching algorithm.