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 StrengthPareto 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 theimportant exploration capacity of evolutionary algorithm. Thisapproach also uses a two-level Diversificator Tabu Search (DTS) to find unexplored zones of Pareto front and achieve acomprehensive coverage. This model was implemented and tested in benchmark functions (ZDT1, ZDT2, and ZDT3). Experimentalresults 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.