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The bat algorithm was integrated into decomposition mechanism on the basis of its evaluation and a multi-objective bat algorithm based on decomposition (MOBA/D) was proposed. In order to improve the algorithm diversity, the differential evolutionary strategy was introduced into MOBA/D. The performances of MOBA/D on 14 multi-objective optimization problems were tested, which included family benchmark functions of LZ—09 and ZDT with different neighborhood scales effect on the performance of the algorithm. The result indicated that MOBA/D had the best performance with neighborhood size of 20. Compared with MOEA/D—DE and NSGA—II, the simulation results showed that MOBA/D can obtain a more uniform distribution of Pareto solution set and better convergence as well as diversity than those of state-of-the-art multi-objective metaheuristics. For further performance analysis of MOBA/D on constraint problem, the optimization design of sliding bearing was solved to demonstrate the feasibility and effectiveness. The good performance on convergence and diversity of the obtained Pareto set demonstrated that MOBA/D was suitable for engineering practice, which was an effective way for solving complex and high dimensional multi-objective optimization problems.