Using headless chicken crossover for local guide selection when solving dynamic multi-objective optimization

Abstract

One of the major issues that should be addressed when solving dynamic problems, is a loss of diversity. In addition, when solving multi-objective optimisation problems, one of the goals is to find a diverse set of solutions. Therefore, a key component of a dynamic multi-objective optimisation algorithm (DMOA) is an approach to increase diversity of either the dynamic multi-objective optimisation algorithm DMOA’s individuals or the guides that guide the search of the DMOA. This study investigates whether using the headless chicken macromutation operator for local guide selection improves the performance of the dynamic vector evaluated particle swarm optimisation (DVEPSO) algorithm. Results indicate that the operator does improve the accuracy of the set of solutions that is found by DVEPSO. However, fewer solutions are found.