Shift Based Penalty Competitive Constrained Multi-Objective Particle Swarm Optimization Algorithm

In real life, there are many problems with conditional constraints, which can be regarded as constrained optimization problems. The difficulty in solving this type of problem lies in how to handle the constraint conditions. Constraint conditions make solving constrained multi-objective optimization problems challenging. Based on the multi-objective particle swarm optimization algorithm based on the competition mechanism, this paper introduces the displacement-based penalty function constraint processing technology, proposes the Shift Penalty Competitive Constrained Multi-Objective Particle Swarm Optimization Algorithm (SPCCMOPSO) with mixed displacement penalty functions, and is used to solve constrained multi-objective optimization problems. Through conducting simulation experiments on the MW series test functions and the Constrained DTLZ (C-DTLZ) series test functions, and comparing and analyzing with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Push and Pull Search (PPS) algorithms, based on the experimental results of the Inverted Generational Distance (IGD) index, the proposed algorithm is effective.