A hybrid genetic algorithm for multiobjective structural optimization

The genetic algorithm (GA) is a potent multiobjective optimization method, and the effectiveness of hybridizing it with local search (LS) has recently been reported in the literature. In this work, the proposed hybrid algorithm integrates a simple local search strategy with an effective constrained multi-objective evolutionary algorithm. A novel constrained tournament selection is used as a single objective function in the local search strategy. The selection is utilized to determine whether a new solution generated in local search process will survive. Hooke and Jeeves method is applied to decide search path. Good initial solutions, the solutions to be mutated, are chosen for local search. This paper also examines the following strategies in the implementation of local search: applying local search only to final solutions, applying local search to solutions only in the early generations, and initializing local search when mutation gives rise to improvement in the solution. Simulation results from a target matching test problem indicate that the hybrid algorithm outperforms the multi-objective method without genetic local search when the implementation of local search is appropriate. It is also shown that the hybridization can improve the convergence speed.