摘要
将粒子群算法与局部优化方法相结合,提出了一种混合粒子群多目标优化算法(HMOPSO)。该算法针对粒子群局部优化性能较差的缺点,引入多目标线搜索与粒子群算法相结合的策略,以增强粒子群算法的局部搜索能力。HMOPSO首先运行PSO算法,得到近似的Pareto最优解;然后启动多目标线搜索,发挥传统数值优化算法的优势,对其进行进一步的优化。数值实验表明,HMOPSO具有良好的全局优化性能和较强的局部搜索能力,同时HMOPSO所得的非劣解集在分散性、错误率和逼近程度等量化指标上优于MOPSO。
Combining particle swarm search with local search,a hybrid multi-objective particle swarm optimization（HMOPSO） algorithm for multi-objective optimization is proposed.Aiming at the defect of local optimization for PSO,HMOPSO introduces multi-objective linearity search as a means of acceleration and refinement of the solutions of particle swarm search to improve search performance.It first runs the PSO in order to obtain approximative Pareto optimal solutions.Once the MOPSO is over,multi-objective linearity search is then run with each previously obtained solution to find a better solution.Simulation results show that HMOPSO,compared with MOPSO,can improve efficiency of optimization and ensure a better convergence,spacing and error ration to the true Pareto optimal front.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第33期18-21,共4页
Computer Engineering and Applications
基金
国家自然科学基金No.10472034
No.10590351~~
