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

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