Evolutionary algorithm for multiobjective optimization: Cooperative coevolution and new features

Abstract

The thesis seeks to explore and improve the evolutionary techniques for multiobjective optimization. A cooperative coevolution mechanism is introduced into multiobjective optimization, which evolves multiple solutions in the form of cooperative subpopulations. The cooperation among subpopulations is achieved through the sharing of archive and representatives of subpopulations. Such a loosely coupled paradigm can be easily formulated into a distributed computing structure to reduce the runtime by sharing the computational workload among various networked computers. To improve the ability of multiobjective evolutionary algorithms to discover and distribute non-dominated solutions along the Pareto front, an adaptive mutation operator and an enhanced exploration strategy are proposed. The adaptive mutation operator adapts the mutation rate to maintain a balance between the introduction of diversity and local fine-tuning. The enhanced exploration strategy encourages the search towards less populated areas to distribute the generated solutions evenly.