An enhanced reference vectors-based multi-objective evolutionary algorithm with neighborhood-based adaptive adjustment - Fan, Rui; Wei, Lixin; Sun, Hao; Hu, Ziyu (Neural computing & applications, 2020)
The decomposition-based evolutionary algorithms have shown great potential in multi-objective optimization and many-objective optimization. However, their performance strongly depends on the Pareto front shapes. This may result from the fixed reference vectors, which will waste computing resources when handling irregular Pareto fronts. Inspired by this issue, an enhanced reference vectors-based multi-objective evolutionary algorithm with neighborhood-based adaptive adjustment (MOEA-NAA) is proposed. Firstly, a few individuals of the population are used to search the solution space to accelerate the convergence speed until enough non-dominated solutions are found. Then, a multi-criteria environment selection mechanism is implemented to achieve the balance between convergence and diversity, which makes a fusion between dominance-based method and reference vector-based method. Finally, according to the neighborhood information, a small-scale reference vectors adaptive fine-tuning strategy is introduced to enhance the adaptability of different Pareto fronts. To validate the efficiency of MOEA-NAA, experiments are conducted to compare it with four state-of-the-art evolutionary algorithms. The simulation results have shown that the proposed algorithm outperforms the compared algorithms for overall performance.