A Multi-objective Particle Swarm Optimizer Based on Simulated Annealing and Decomposition

Multi-objective particle swarm optimization algorithms have been widely used to solve multi-objective optimization problem. This paper presents a novel multi-objective particle swarm optimizer based on simulated annealing and decomposition for solving multi-objective optimization problems. First, the decomposition mechanism is adopted that simplifies a multi-objective optimization problem into a number of scalar single optimization sub-problems and optimizes them simultaneously. And then the simulated annealing strategy is incorporated into the algorithm as a local search operator to improve the search performance of PSO. At last simulation results compared with three classical multi-objective methods using thirteen test functions and one metric taken from the standard literature on multi-objective optimization indicates that the proposed algorithm is effective and competitive.