Two-phase ESO and comprehensive learning PSO method for structural optimization with discrete steel sections

Abstract

This paper proposes the novel two-phase metaheuristic method that combines the distinctive features provided by evolutionary structural optimization (ESO) and comprehensive learning particle swarm optimization (CLPSO) for the design of steel structures with standard sections. The approach overcomes the challenges associated with local optima pitfalls in processing the mixed integer nonlinear programming problem, and hence determines the accurate solutions at modest computing efforts. In essence, the first-phase ESO design incorporates the constraint relaxations to fast eliminate the ineffective (infeasible) sections from initial discrete variable domains. This advantageously provides a significant reduction in the search spaces performed in the second-phase CLPSO scheme. The comprehensive learning technique enhances a standard particle swarm optimization algorithm by enabling the cooperative responses among swarm populations. The learning probability function defines the comprehensive cross-positions between the sets of best particles constructed from the reducing sets of steel sections specified in the preliminary ESO phase leading to the computation of optimal solutions. Various benchmarks (subjected to infinite design combinations) illustrate robustness and accuracy of the proposed design method (viz., generating the smaller number of particles) as compared to conventional metaheuristic algorithms.