PIBEA: Prospect Indicator Based Evolutionary Algorithm for Multiobjective Optimization Problems

This paper proposes and evaluates an evolutionary multiobjective optimization algorithm (EMOA) that uses a new quality indicator, called the prospect indicator, for parent selection and environmental selection operators. The prospect indicator measures the potential of each individual to reproduce offspring that dominate itself and spread out in the objective space. The prospect indicator allows the proposed EMOA, PIBEA (Prospect Indicator Based Evolutionary Algorithm), to (1) maintain sufficient selection pressure, even in high dimensional MOPs, thereby improving convergence velocity toward the Pareto front, and (2) diversify individuals, even in high dimensional MOPs, thereby distributing individuals uniformly in the objective space. Experimental results show that PIBEA effectively performs its operators in high dimensional problems and outperforms three existing well-known EMOAs, NSGA-II, SPEA2 and AbYSS, in terms of convergence velocity, diversity of individuals, coverage of the Pareto front and performance stability.