Abstract:
In order to improve the diversity and distribution of multi-objective algorithms, this paper proposes an adaptive neighborhood multi-objective evolutionary algorithm based on event triggering (MOEA/D-ET). By using the event triggering strategy to coordinate the global search and the local search, the proposed algorithm can converge to the Pareto frontier and attain better diversity. The grid method is adopted to achieve global optimization such that the grid coordinate system can quickly constrain the solution to the vicinity of the frontier surface and attain a good diversity, which can provide an effective guidance for local search. The adaptive neighborhood MOEA/D is used for local optimization and the search direction is optimized by the adaptive neighborhood. The number of weight vectors is enlarged for the small coverage area for increasing the search intensity such that it can jump out of the local minimum, improve the diversity of the solution, and make the solution more widely distributed in space. Three different levels of fixed neighborhood method and adaptive neighborhood method are compared via the ZDT1 problem, by which it is shown that the proposed adaptive neighborhood method can effectively improve the density of solution in the central region and make the distribution of the solution more uniform. Finally, the simulation via ZDT1—ZDT4, WFG1—WFG4, DTLZ1—DTLZ4, and DTLZ7 test functions is undergone, whose results are compared with four classic multi-objective algorithms and two new multi-objective algorithms including NSGA-III. It is shown from these simulation results that the proposed algorithm can attain better distribution in most tests than other test algorithms and has greater advantages in convergence and diversity.