Tan, K.C., Lee, T.H. and Khor, E.F. (2002) Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons. Artificial Intelligence Review, 17, 253-290.
https://doi.org/10.1023/A:1015516501242

has been cited by the following article:

• TITLE: Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

  AUTHORS: Mengshan Li, Liang Liu, Genqin Sun, Keming Su, Huaijin Zhang, Bingsheng Chen, Yan Wu

  KEYWORDS: Particle Swarm, Algorithm, Chaotic Sequences, Self-Adaptive Strategy, Multi-Objective Optimization

  JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.12, September 30, 2017

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