FLEXURE SECTION OPTIMIZATION USING ADVANCED PARTICLE SWARM-ASSISTED GENETIC ALGORITHM

摘要

This paper is focused on the development of an efficient hybrid optimizer using particle swarmrnoptimization and an improved genetic algorithm with an effective constraint handling scheme forrnconstrained nonlinear optimization. The basic operators of genetic algorithm, i.e., crossover and mutationrnare revisited and a new rank-based multi-parent crossover operator is proposed. The rank-based crossoverrnoperator enhances both the local as well as the global exploration simultaneously. The performance of thernalgorithm is investigated using a mixed discrete constrained engineering optimization problem. The resultsrnindicate remarkable improvements in terms of efficiency and robustness as compared to other state-of-theartrnalgorithms. The proposed algorithm is then employed for the design of flexbeam cross-section of a fullscalernbearingless helicopter.