Particle swarm optimization based on dimensional learning strategy

In traditional particle swarm optimization (PSO) algorithm, each particle updates its velocity and position with a learning mechanism based on its personal best experience and the population best experience. The learning mechanism in traditional PSO is simple and easy to implement, but it suffers some potential problems, such as the phenomena of “oscillation” and “two steps forward, one step back”. Therefore, designing an effective learning strategy to avoid these two phenomena and to improve the search efficiency is an urgent issue for PSO research. This paper proposes a dimensional learning strategy (DLS) for discovering and integrating the promising information of the population best solution according to the personal best experience of each particle. Thereafter, a two-swarm learning PSO (TSLPSO) algorithm based on different learning strategies is proposed. One of the subpopulations constructs the learning exemplars by DLS to guide the local search of the particles, and the other subpopulation constructs the learning exemplars by the comprehensive learning strategy to guide the global search. 16 classic benchmark functions, 30 CEC2014 test functions, and 1 real-world optimization problem are used to test the proposed algorithm against with 5 typical PSO algorithms and 1 state-of-the-art differential evolution (DE) algorithm. The experimental results show that TSLPSO is statistically and significantly better than the compared algorithms for most of the test problems. Moreover, the convergence speed and convergence accuracy of TSLPSO are also significantly improved.