Investigation of the robustness of pso algorithms in reliability optimal design problems

Abstract eng:
In this work, the robustness of two variants of the Binary Particle Swarm Optimization algorithm (ΒPSO) are examined, in the Reliability Based Optimal Design (RBOD) of determinate truss structures. Particle swarm optimization (PSO) [1] is a population based stochastic optimization method inspired from the social behavior of birds or insects. In the first variant, the Binary Repulsive PSO (BRPSO), the velocity vector uses information obtained from an optimal solution found by the neighbors of the individual, the individual’s memory, its current velocity vector and some “noise”. Repulsive PSO is considered robust in very complex search space with numerous local optima [2]. The second variant is the Binary PSO incorporating the “craziness” operator. When craziness is implemented a subset of the particles is selected, and their position and/or velocity vectors are re-initialized [3]. The robustness of these variants is examined in the RBOD of a 25-bar truss and a 30-bar arch. The results of the investigated optimization schemes, for these two problems, are compared with the results obtained from the BPSO and the Standard Genetic Algorithm (SGA) and the relative performance of all variants is discussed.

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