Comparison of PSO variants applied to large scale optimization problems
Matheus R. D. Ullmann, Kayo F. Pimentel, Leonardo A. de Melo, Gelson da Cruz, Cássio Dener Noronha Vinhal · 2017
In order to address an issue concerning the increasing number of algorithms based on particle swarm optimization (PSO) applied to solve large-scale optimization problems (up to 2000 variables), this article presents analysis and comparisons among five state-of-the-art PSO algorithms (CCPSO2, LSS-PSO, OBL-PSO, SPSO and VCPSO). Tests were performed to illustrate the efficiency and feasibility of using the algorithms for this type of problem. Six benchmark functions most commonly used in the literature (Ackley 1, Griewank, Rastrigin, Rosenbrock, Schwefel 1.2 and Sphere) were tested. The experiments were performed using a high-dimensional problem (500 variables), varying the number of particles (50, 100 and 200 particles) in each algorithm, thus increasing the computational complexity. The analysis showed that the CCPSO2 and OBL-PSO algorithms found significantly better solutions than the other algorithms for more complex multimodal problems (which most resemble real-world problems). However, considering unimodal functions, the CCPSO2 algorithm stood out before the others. Our results and experimental analysis suggest that CCPSO2 and OBL-PSO seem to be highly competitive optimization algorithms to solve large-scale complex and multimodal optimization problems.