Solving multi-objective problems using SPEA2 and Tabu search

Evolutionary Algorithms (EA´s) are often well-suited for optimization problems involving several, often conflicting objectives. In this paper an improved hybrid method based on Strength Pareto Evolutionary Algorithm2 (SPEA2) and Tabu Search (TS) is proposed to handle the multi-objective optimization problems (MOPs). This method uses the exploration capacity of SPEA2 beside the power of TS in neighborhood research to find Pareto optimal solutions in different multi objective problems. To have a good distribution of solutions, proposed approach also uses an Improved Diversificator Tabu Search (IDTS) to find unexplored zones of Pareto front and achieve a comprehensive coverage. To test and compare this model with previous works, functions (ZDT1, ZDT2, and ZDT3) are selected. Experimental results show not only clear improvement of the proposed method, in contrast to the previous approaches, but good coverage and distribution of the Pareto front points have been achieved.