Improved Interval Multi-objective Evolutionary Optimization Algorithm Based on Directed Graph

Multi-objective evolutionary algorithm for optimizing objectives with interval parameters is becoming more and more important in practice. The efficient comparison metrics on interval values and the associated offspring generations are critical. We first present a neighboring dominance metric for interval numbers comparisons. Then, the potential dominant solutions are predicted by constructing a directed graph with the neighboring dominance. We design a directed graph using those competitive solutions sorted with NSGA-II, and predict the possible evolutionary paths of next generation. A PSO mechanic is applied to generate the potential outstanding solutions in the paths, and these solutions are further used to improve the crossover efficiency. The experimental results demonstrate the performance of the proposed algorithm in improving the convergence of interval multi-objective evolutionary optimization.