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 a 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 makes cooperation between the strength of research of neighborhood methods presented by the TS and the important exploration capacity of evolutionary algorithm. This approach also uses a two-level Diversificator Tabu Search (DTS) to find unexplored zones of Pareto front and achieve a comprehensive coverage. This model was implemented and tested in benchmark functions (ZDT1, ZDT2, and ZDT3). Experimental results show not only clear improvement of the proposed method in contrast to the previous approaches, but good co