IMPLEMENTATION OF LOCAL SEARCH IN HYBRID MULTI-OBJECTIVE GENETIC ALGORITHMS : A CASE STUDY ON FLOWSHOP SCHEDULING

This paper examines the following issues related to the implementation of local search in hybrid multi-objective genetic algorithms: specification of an objective function to be optimized by local search, early termination of local search before finding a locally optimum solution, choice of individuals to which local search is applied, and timing of the application of local search. These issues are examined through computer simulations on a flowshop scheduling problem using a hybrid version of a wellknown multi-objective genetic algorithm: the strength Pareto evolutionary algorithm (SPEA). Simulation results show that the hybridization with local search degrades the search ability of the SPEA when the implementation of local search is not appropriate. It is also shown that the hybridization has the possibility to improve the convergence speed of the SPEA to the Pareto front.

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