Balancing exploration and exploitation in multiobjective evolutionary optimization

Balancing exploration and exploitation is fundamental to the performance of an evolutionary algorithm. In this paper, we propose a survival analysis method to address this issue. Results of the analysis is used to adaptively choose appropriate new solution creation operators which prefer either exploration or exploitation. In the developed algorithm, a differential evolution recombination operator is used for the exploration purpose, while a new clustering-based operator is proposed for exploitation. Empirical comparison with four well-known multi-objective evolutionary algorithms on test instances with complex Pareto sets and Pareto fronts indicates the effectiveness and outperformance of the developed algorithms on these test instances in terms of commonly-used metrics.