Multi-objective Particle Swarm Optimization Algorithm for Task Allocation and Archived Guided Mutation Strategies

In this paper, we propose a novel multi-objective particle swarm optimization algorithm with a task allocation and archive-guided mutation strategy (TAMOPSO), which effectively solves the problem of inefficient search in traditional algorithms by assigning different evolutionary tasks to particles with different characteristics. First, TAMOPSO divides multiple subpopulations according to the particle distribution status of each iteration of the population and designs a new task allocation mechanism to improve the evolutionary search efficiency. Second, TAMOPSO adopts an adaptive Lévy flight strategy according to the population growth rate, automatically increasing the global variation probability to expand the search range when the population converges and enhancing the local variation to conduct fine search when the population disperses to realize the dynamics of global and local variations. Finally, TAMOPSO measures the contribution of particles to the population optimization through the particle evolution contribution rate index and filters out valuable historical solutions for subsequent reuse to accelerate the convergence speed; in addition, TAMOPSO improves the individual optimal particle selection mechanism, changes the bias of the traditional algorithm, ensures that each particle has an equal opportunity, and enhances the fairness of the selection process. The fairness of the selection process is enhanced at the same time. The performance of TAMOPSO is compared with ten existing algorithms on 22 standard test problems, and the experimental results show that TAMOPSO outperforms the other algorithms in several standard test problems and has better performance in solving multi-objective problems.