A large-scale multi-objective algorithm integrating prisoner’s dilemma model and prospect theory with adaptive learning

Abstract

Numerous multi-objective problems contain many decision variables in the real world, which are referred to as large-scale multi-objective problems (LSMOPs). The challenge of achieving a balance between convergence and diversity in large-scale multi-objective optimization is addressed in this paper, which introduces a hybrid strategy for solving LSMOPs. Initially, a two-stage adaptive learning strategy based on history is employed to update the population. Subsequently, the prisoner’s dilemma model in game theory is introduced during the offspring generation stage to balance convergence and diversity. A fuzzy evolutionary mechanism is then employed for optimization to enhance the diversity and searchability of the population. Finally, a reference vector-guided selection and a risk preference mechanism based on prospect theory are employed to perform selection during the environmental selection phase. Experimental results on benchmark problems with 100–1000 decision variables reveal that the algorithm has the best overall performance compared with state-of-the-art large-scale multi-objective evolutionary algorithms (MOEAs).