Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement
针对多目标优化问题中算法性能下降的挑战，提出一种两阶段方法：先让种群快速逼近真实帕累托前沿附近的少数目标点，再通过多样性提升策略使个体均匀分布。实验表明该方法在收敛性和多样性上均优于五种现有算法。
Evolutionary algorithms have been successfully applied for exploring both converged and diversified approximate Pareto-optimal fronts in multiobjective optimization problems, two- or three-objective in general. However, when solving problems with many objectives, nearly all algorithms perform poorly due to the loss of selection pressure in fitness evaluation. An extremely large objective space could inadvertently deteriorate the effect of an evolutionary operator. In this paper, we propose a new approach to directly handle the challenges to solve many-objective optimization problems (MaOPs). This novel design includes two stages: first, the whole population quickly approaches a small number of “target” points near the true Pareto front; then, the proposed diversity improvement strategy is applied to facilitate these individuals to spread and well distribute. As a case study, the proposed algorithm based on this design is compared with five state-of-the-art algorithms. Experimental results show that the proposed method exhibits improved performance in both convergence and diversity for solving MaOPs.