A Hybrid MOEA/D-TS for Solving Multi-Objective Problems

In many real world applications we are often faced with different optimization problems involving several, often conflicting objectives. In this paper we employ a recently developed algorithm, the so-called Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) beside Tabu Search (TS) to handle multi-objective optimization problems (MOPs). This improved hybrid method, namely MOEA/D-TS, uses the parallel computing capacity of MOEA/D along with the power of TS in neighborhood search to find Pareto optimal solutions in different multi-objective problems. The performance of the proposed approach, in terms of different metrics is compared with SPEA, COMOEATS and SPEA2TS on three well-known test functions (ZDT1, ZDT2, and ZDT3). Experimental results reveal that the proposed method could significantly outperform previous algorithms with a good coverage and distribution of the Pareto front points.