HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION FOR THE FORCE METHOD-BASED SIMULTANEOUS ANALYSIS AND DESIGN

Abstract– The computational drawbacks of existing numerical methods have forced researchers to rely on heuristic algorithms. Heuristic methods are powerful in obtaining the solution of optimization problems. Although these methods are approximate methods (i.e. their solutions are good, but probably not optimal), they do not require the derivatives of the objective function and constraints. Also, the heuristics use probabilistic transition rules instead of deterministic rules. Here, an evolutionary algorithm based on the hybrid genetic algorithm (GA) and particle swarm optimization (PSO), denoted by HGAPSO, is developed in order to solve force method-based simultaneous analysis and design problems for frame structures. Suitability of the HGAPSO algorithm is compared to both GA and PSO for all the design examples, demonstrating its efficiency and superiority, especially for frames with a larger number of redundant forces.

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