a hybrid moea/d-ts for solving multi-objective problems

In many real-world applications, various optimization problems with conflicting objectives are very common. in this work, we employ multi-objective evolutionary algorithm based on decomposition (moea/d), a newly developed method beside tabu search (ts) accompaniment to achieve a new manner for solving multi-objective optimization problems (mops) with two or three conflicting objectives. this improved hybrid algorithm, namely moea/d-ts, uses the parallel computing capacity of moea/d along with the neighborhood search authority of ts for discovering pareto optimal solutions. our goal is to exploit the advantages of evolutionary algorithms and ts to achieve an integrated method to cover the totality of the pareto front by uniformly distributed solutions. in order to evaluate the capabilities of the proposed method, its performance based on various metrics is compared with spea, comoeats, and spea2ts on the wellknown zitzler-deb-thiele’s zdt test suite and dtlz test functions with separable objective functions. according to the experimental results obtained, the proposed method could significantly outperform the previous algorithms and produce fully satisfactory results.