Learning to Evolve With Guiding Solutions Generated by Generative Adversarial Network

Many search strategies have been designed to generate a promising offspring population for efficiently solving large-scale multiobjective optimization problems (LSMOPs). The effectiveness of existing search strategies relies on the quality of good parent solutions. However, especially in early generations, the current population does not always include high-quality solutions. This article proposes a generative adversarial network (GAN)-guided search (G2S) strategy for learning to evolve with guiding solutions. Its main idea is to employ GAN for mapping a set of guiding points in the objective space with good convergence and diversity back to the decision space to guide evolution. Specifically, the current population is used as real data, and the guiding points consisting of nondominated solutions and reference vectors are used as virtual data. The trained GAN generates guiding solutions in the decision space to guide the population to evolve efficiently. A large-scale multiobjective evolutionary framework using G2S is also proposed which can be embedded into multiobjective evolutionary algorithms (MOEAs) to improve their ability to handle LSMOPs. Experimental studies on several benchmark problems with the highest-5000-D decision space show that the proposed G2S is competitive compared with the state-of-the-art algorithms and has impressive efficiency as the component to improve the performance of MOEAs for solving LSMOPs.