Computer Science ›› 2017, Vol. 44 ›› Issue (1): 253-258.doi: 10.11896/j.issn.1002-137X.2017.01.047

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Hybrid Multi-objective Particle Swarm Optimization Based on Intuitionistic Fuzzy Dominance

MEI Hai-tao, HUA Ji-xue, WANG Yi and WEN Tong   

  • Online:2018-11-13 Published:2018-11-13

Abstract: To improve the precision and distribute uniformity of multi-objective optimization problem,a hybrid multi-objective particle swarm optimization based on intuitionistic fuzzy dominance (IFDHPSO) was proposed.By introducing the scalar factor,this paper utilizeed intuitionistic fuzzy membership and rank method to define a new dominance strategy.Then,we proposed a meta-lamarckian local learning strategy to reduce the probability of being trapped into the local optima and premature convergence,which is based on simulated annealing algorithm.Then,the PSO identical factor was defined to adjust inertia weight and acceleration operator adaptively.Furthermore,a decline disturbance strategy was proposed to disturb particle’s velocity.Finally,the simulation results comparing with other typical optimization algorithm shows that the proposed algorithm performs better in solution precision,uniformity and convergence.

Key words: Intuitionistic fuzzy dominance,Hybrid particle swarm optimization,Simulated annealing,Meta-lamarckian learning,Identical factor

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