Comparação de algoritmos de enxame de partículas para otimização de problemas em larga escala
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2018-10-26
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Universidade Federal de Goiás
Resumo
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 e
ciency 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 realworld
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 complex and multimodal optimization problems.
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MELO, L. A. M. Comparação de algoritmos de enxame de partículas para otimização de problemas em larga escala. 2018. 64 f. Dissertação (Mestrado em Engenharia Elétrica e da Computação) - Universidade Federal de Goiás, Goiânia, 2018.