多蚁群分级优化的多目标求解方法
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摘要
为提高多目标优化方法的求解性能,在给出了蚁群算法优化函数类问题求解方法的基础上,提出了基于多蚁群分级优化多目标问题的求解方法。构建了子蚁群以自身启发式信息及以其他子群的启发式信息获得准Pareto解以及采用各子群的每一只蚂蚁获得的准Pareto解作支配判断,从而提高Pareto解的多样性;构建了父蚁群以准Pareto解作为空间节点构成TSP类似的组合优化问题,其求解结果以获得多目标优化问题的Pareto解的前沿,从而提高Pareto解的均匀分布性。通过优化实例验证,结果表明,多蚁群分级优化的多目标求解方法所获得的Pareto解具有解的多样性以及解的均匀分布性。
In order to improve the solving performance of multi-objective optimization problem,this paper proposed a new method based on multi-ant-colony algorithms. Aiming to enhance the diversity of Pareto solutions,quasi-Pareto solutions were constructed by sub-ant-colony algorithm which adopted its own and other sub-ant-colony heuristic information and quasi-Pareto solutions obtained by every ant were used for control judgment. The constructed farther-group ants with the quasi-Pareto solutions which act as space nodes constitute TSP( traveling salesman problem) ,and then the solutions of the TSP act as the front of solutions for multi-objective optimization problem,hence lead to the enhancement of the uniform distribution of Pareto solutions. Experiment results show that the obtained Pareto solutions by multi-ant-colony optimization based on multi-classification methods have many advantages,such as the diversity and uniform distribution of solutions.
In order to improve the solving performance of multi-objective optimization problem,this paper proposed a new method based on multi-ant-colony algorithms. Aiming to enhance the diversity of Pareto solutions,quasi-Pareto solutions were constructed by sub-ant-colony algorithm which adopted its own and other sub-ant-colony heuristic information and quasi-Pareto solutions obtained by every ant were used for control judgment. The constructed farther-group ants with the quasi-Pareto solutions which act as space nodes constitute TSP( traveling salesman problem) ,and then the solutions of the TSP act as the front of solutions for multi-objective optimization problem,hence lead to the enhancement of the uniform distribution of Pareto solutions. Experiment results show that the obtained Pareto solutions by multi-ant-colony optimization based on multi-classification methods have many advantages,such as the diversity and uniform distribution of solutions.
引文
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[6]原思聪,刘道华,江祥奎,等.基于蚁群算法的多维有约束函数优化研究[J].计算机应用研究,2008,25(6):1682-1684.
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[11]芦金婵,李乃成,王伟东.基于最佳进化方向的多目标遗传算法[J].计算机工程与应用,2005,41(19):73-76.
[2]ZITZLER E,LAUMANNS M,THIELE L.Improving the strength Pare-to evolutionary algorithm[J].Evolutionary Computation,2001,5(2):121-129.
[3]DEB K,PRATA P A,AGARWAL S,et al.A fast and elitist multi-ob-jective genetic algorithm:NSGA-II[J].Evolutionary Computation,2002,6(6):182-197.
[4]李英民,杨琼,赖明.基于进化策略算法拟合多阻尼比反应谱的地震动仿真[J].世界地震工程,2003,19(2):33-38.
[5]张利彪,周春光,刘小华,等.粒子群算法在求解优化问题中的应用[J].吉林大学学报,2005,23(4):385-389.
[6]原思聪,刘道华,江祥奎,等.基于蚁群算法的多维有约束函数优化研究[J].计算机应用研究,2008,25(6):1682-1684.
[7]朱颖,武穆清.最优化参数逼近的自适应退避算法[J].西安电子科技大学学报,2007,34(6):995-999.
[8]HWANG S F,HE R S.Improving real-parameter genetic algorithm with simulated annealing for engineering problems[J].Advances in Engineering Software,2006,37(6):406-418.
[9]罗彪,郑金华.多目标进化算法中基于动态聚集距离的分布性保持策略[J].计算机应用研究,2008,25(10):2934-2938.
[10]邹振宇,江全元,张鹏翔,等.PSS与SVC多目标协调设计[J].电力系统及其自动化学报,2005,17(4):66-70.
[11]芦金婵,李乃成,王伟东.基于最佳进化方向的多目标遗传算法[J].计算机工程与应用,2005,41(19):73-76.
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