论文部分内容阅读
为了提高多目标优化算法解集的分布性和收敛性,提出一种基于分解和差分进化的多目标粒子群优化算法(d MOPSO-DE).该算法通过提出方向角产生一组均匀的方向向量,确保粒子分布的均匀性;引入隐式精英保持策略和差分进化修正机制选择全局最优粒子,避免种群陷入局部最优Pareto前沿;采用粒子重置策略保证群体的多样性.与非支配排序(NSGA-II)算法、多目标粒子群优化(MOPSO)算法、分解多目标粒子群优化(d MOPSO)算法和分解多目标进化-差分进化(MOEA/D-DE)算法进行比较,实验结果表明,所提出算法在求解多目标优化问题时具有良好的收敛性和多样性.
In order to improve the distribution and convergence of the solution set of the multi-objective optimization algorithm, a multi-objective particle swarm optimization algorithm (d MOPSO-DE) based on decomposition and differential evolution is proposed. This algorithm generates a set of uniform directional vectors , To ensure the uniformity of particle distribution; the introduction of implicit elite retention strategy and differential evolutionary correction mechanism to select the global optimal particle, to avoid the population into the local optimal Pareto front; particle replacement strategy to ensure the diversity of the population and non-dominated sorting NSGA-II algorithm, MOPSO algorithm, decomposed multi-objective particle swarm optimization (d MOPSO) algorithm and decomposed multi-objective evolutionary-differential evolution (MOEA / D-DE) The proposed algorithm has good convergence and diversity in solving multi-objective optimization problems.