Computer Science ›› 2018, Vol. 45 ›› Issue (5): 208-214.doi: 10.11896/j.issn.1002-137X.2018.05.035

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Differential Hybrid Particle Swarm Optimization Algorithm Based on Different Dimensional Variation

LI Jun, LUO Yang-kun, LI Bo and LI Qiao-mu   

  • Online:2018-05-15 Published:2018-07-25

Abstract: Considering the limitations that particle swarm optimization(PSO) algorithm and differential evolution(DE) algorithm are difficult to control the initial distribution of the particles and easily fall into local optimum to reduce the convergence accuracy at later process,a differential hybrid particle swarm optimization algorithm based on the different dimensional variation was proposed. Firstly,in order to improve the diversity of particle swarm,the entropy measure me-thod was introduced to initialize particles.Secondly,during the process of particle iteration, learning strategy of different dimensional variation and dimension factors were adopted to guide the immersed particles to jump out of the local optimum to reach the best solution in a timely manner according to the particle distribution.Finally,this algorithm was simulated on ten typical test functions.The convergence precision and standard deviation show the superior performance of the proposed method on nine testing functions comparing with PSO,DEPSO and CDEPSO.These experiments prove that the algorithm has a strong advantage in convergence accuracy and optimization efficiency.

Key words: Entropy,Different dimensional variation,Dimensionality factor,Differential evolution-particle swarm optimization algorithm

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