Hybridization of SBX based NSGA-II and sequential quadratic programming for solving multi-objective optimization problems

Most real-world search and optimization problems involve multiple conflicting objectives and results in a Pareto-optimal set. Various multi-objective optimization algorithms have been proposed for solving such problems with the goals of finding as many trade-off solutions as possible and maintaining diversity among them. Since last decade, evolutionary multi-objective optimization (EMO) algorithms have been applied successfully to various test and real-world optimization problems. These population based algorithms provide a diverse set of non-dominated solutions. The obtained non-dominated set is close to the true Pareto-optimal front but it's convergence to the true Pareto-optimal front is not guaranteed. Hence to ensure the same, a local search method using classical algorithm can be applied. In the present work, SBX based NSGA-II is used as a population based approach and the sequential quadratic programming (SQP) method is used as a local search procedure. This hybridization of evolutionary and classical algorithms approach provides a confidence of converging near to the true Pareto-optimal set with a good diversity. The proposed procedure is successfully applied to 13 test problems consisting two, three and five objectives. The obtained results validate our motivation of hybridizing evolutionary and classical methods.