An improvement decomposition-based multi-objective evolutionary algorithm using multi-search strategy

摘要

The main goal of multi-objective optimization evolutionary algorithms (MOEAs) is to obtain a set of solutions with good diversity and convergence. However, how to concurrently improve the diversity and convergence is a difficult work. To address this problem, an updated strategy based on decomposition is used to maintain the diversity, and the convergence is enhanced by improving the search efficiency. In this paper, an improvement decomposition-based multi-objective evolutionary algorithm using multi-search strategy is aimed at improving the search efficiency. In this work, three search strategies are used to help crossover operators to carry out the local search and global search. This multi-search strategy selects sparse non-dominated solutions to carry out the exploration, and selects convergent solutions to implement the exploitation. In the experiments, the proposed algorithm is compared with seven efficient state-of-the-art algorithms, e.g., NSGAII, MOEA/D, MOEA-DVA, MOEA-IGD-NS, MOEA/D-PaS, RVEA and MPSOD, on twenty-two benchmark functions. Empirical results show that the proposed algorithm can find a set of solutions with better diversity and convergence than six compared algorithms.