Adaptive Multi-objective Differential Evolution with Stochastic Coding Strategy

Abstract

Many real-world applications can be modeled as multi-objective optimization problems (MOPs). Applying differential evolution (DE) to MOPs is a promising research topic and has drawn a lot of attention in recent years. To search high-quality solutions for MOPs, this paper presents a robust adaptive DE (termed AS-MODE) with following two features. First, a stochastic coding strategy is used to improve the solution quality. This coding strategy represents each individual by a stochastic region, which enables the algorithm to fine-tune solutions efficiently. Second, a probability-based adaptive control strategy is utilized to reduce the influence of parameter settings. The adaptive control strategy associates each parameter with a candidate value set. Better candidate values would have higher selection probabilities to generate new individuals. The performance of the proposed AS-MODE is compared with several highly regarded multi-objective evolutionary algorithms. Simulation results on ten benchmark test functions with different characteristics reveal that AS-MODE yields very promising performance.