A New High-Dimensional Particle Swarm Evolution Algorithm Based on New Fitness Allocation and Multi-criteria Strategy

A large number of increasingly complex multi-objective optimization problems have emerged in scientific research and engineering practice, especially high-dimensional multi-objective problems, which has become a problem in the field of intelligent optimization. In order to solve the shortcomings of multi-objective particle swarm optimization in high-dimensional optimization, a new fitness allocation and multi-criteria mutation strategy for high-dimensional particle swarm evolution (FAMCHPSO) is proposed by combining fuzzy information theory and new mutation methods. The algorithm combines fuzzy information theory to abandon the disadvantages of the traditional fitness allocation method of multi-objective optimization algorithm, and proposes a new fitness allocation method, which increases the pressure of population selection, eliminates the influence of external uncertain factors on the algorithm and simplifies the algorithm process, making it suitable for solving high-dimensional multi-objective optimization problems. A new multi-criteria mutation strategy is introduced to effectively perturb the multi-objective particle algorithm, effectively avoiding the algorithm to fall into a local optimum. The FAMCHPSO algorithm is compared with three other representative multi-objective evolution algorithms on the DTLZ series test function set. The simulation results show that the FAMCHPSO algorithm has a significant performance advantage in terms of convergence, diversity, and robustness.