【摘要】 本文针对复杂多目标优化问题Pareto前沿搜索难度大的特点,设计了一种结合多种群间捕获竞争、强化学习机制的多种群Memetic学习策略与进化计算模型.受种群进化、捕食种群与被捕食群体间的竞争等生态学原理的启发,提出了一种基于生态种群捕获竞争模型的多目标Memetic优化算法(Multi-Objective Memetic Algorithm based on Ecological Population Preying-competition Model,ECPM-MOMA).ECPM-MOMA算法设计并运用了捕获竞争、强化学习算子进行全局搜索,在种群进化过程中结合了Memetic搜索算子进行局部搜索.理论分析与实验结果表明,本文所提出的算法具有良好的收敛性能和分布特征,生态种群捕获竞争策略与进化计算模型对于解决复杂多目标优化问题是有效的.更多还原

【Abstract】 Aiming at the difficulty of searching Pareto front for complex multi-objective optimization,a Memetic learning strategy which combines many of population preying-competition mechanism with reinforcement learning mechanism and evolutionary computation model was designed.Inspired by ecological principle,such as the population evolution and the competition between predator populations and prey populations,a multi-objective Memtic optimization algorithm(multi-objective Memetic algorithm based on ecological population preying-competition model,ECPM-MOMA) was proposed.In ECPM-MOMA,Preying-competition and Reinforcement Learning operator was designed and applied for global search.Memetic search operator was also applied for local search in the population evolution process.Experimental results show that the proposed algorithm has better convergence performance and distribution characteristics.The ecological preying-competition strategy and evolutionary computation model is effective for solving complex multi-objective optimization problems.更多还原