Multi-objective random drift particle swarm optimization algorithm with adaptive grids

In this paper, we propose a multi-objective random drift particle swarm optimization algorithm with adaptive grids (MORDPSO-AG) to solve the multi-objective optimization problem. Due to the good search performance of the RDPSO, the proposed algorithm can find more accurate Pareto optimal solutions q...

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Veröffentlicht in:2016 IEEE Congress on Evolutionary Computation (CEC) S. 2064 - 2070
Hauptverfasser: Yiqiong Yuan, Sun, Jun, Dongmei Zhou
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2016
Schlagworte:
Algorithm design and analysis
Convergence
Linear programming
Mathematical model
Pareto optimization
Particle swarm optimization
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Zusammenfassung:In this paper, we propose a multi-objective random drift particle swarm optimization algorithm with adaptive grids (MORDPSO-AG) to solve the multi-objective optimization problem. Due to the good search performance of the RDPSO, the proposed algorithm can find more accurate Pareto optimal solutions quickly. However, like PSO and other population-based search techniques, the loss of diversity and premature convergence are inevitable. Therefore, we introduce the method of adaptive grids into RDPSO to maintain the swarm diversity. We adopt an external archive to reserve the found Pareto optimal solutions, and update the solutions based on adaptive grids. Besides, in order to make the lead particle guide the particle swarm to find the true Pareto optimal solutions, we select the leader particle by using roulette wheel method. Fianlly, we use four benchmark test functions to evaluate the performance of the algorithm, and the experimental results show that the proposed algorithm has better convergence and solution distribution than the other tested methods.
DOI:10.1109/CEC.2016.7744042