To deal with the problems of premature convergence and tending to jump into\nthe local optimum in the traditional particle swarm optimization, a novel\nimproved particle swarm optimization algorithm was proposed. The\nself-adaptive inertia weight factor was used to accelerate the converging speed,\nand chaotic sequences were used to tune the acceleration coefficients for the\nbalance between exploration and exploitation. The performance of the proposed\nalgorithm was tested on four classical multi-objective optimization\nfunctions by comparing with the non-dominated sorting genetic algorithm\nand multi-objective particle swarm optimization algorithm. The results verified\nthe effectiveness of the algorithm, which improved the premature convergence\nproblem with faster convergence rate and strong ability to jump out\nof local optimum.
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