为了准确地求解组合权重的组合系数,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)思想引入评估领域,提出一种基于MOEA/D的组合权重方法.通常,利用加权和法将组合权重模型转化为...为了准确地求解组合权重的组合系数,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)思想引入评估领域,提出一种基于MOEA/D的组合权重方法.通常,利用加权和法将组合权重模型转化为单目标模型时,模型加权系数难以准确确定.对此,引入MOEA/D算法的分解思想,将组合权重模型转化为多个单目标子模型.MOEA/D算法仅适用于无约束优化问题,而较为常用的惩罚函数法难以表达进化初期无可行解的情况,因而提出改进自适应惩罚函数(improved adaptive penalty function,IAPF),将组合权重模型转化为无约束优化模型.应用所提出方法与其他方法进行仿真实验,实验结果表明,所提出算法具有有效性.展开更多
建立含风电系统的环境经济调度(Environmental Economic Dispatch,EED)模型,提出采用基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)来求解,并在算法中加入约束处理,归一化及变异操...建立含风电系统的环境经济调度(Environmental Economic Dispatch,EED)模型,提出采用基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)来求解,并在算法中加入约束处理,归一化及变异操作来改善算法的性能并保持解的多样性,以获得理想的Pareto最优前沿.通过仿真计算,并与其他优化算法进行对比分析,验证了MOEA/D算法解决含风电EED问题的可行性和有效性.展开更多
基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)是多目标优化算法的一个重要分支,多目标优化的关键问题是如何在算法的收敛性和散布性之间达到良好的平衡.目前主流算法的聚合函数存...基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)是多目标优化算法的一个重要分支,多目标优化的关键问题是如何在算法的收敛性和散布性之间达到良好的平衡.目前主流算法的聚合函数存在着不同的优缺点,尤其是当使用切比雪夫方法选择个体时,经常出现个体偏离权重现象,个体和权重间得不到很好的粘合.本文基于此提出了一种新的聚合函数方法,提高了MOEA/D的性能.该聚合函数的函数形式为二次函数,种群个体在该函数下的等高线是一条二次曲线(本文称双曲线函数方法,Hyperbola Function Method,HYB),是对目前存在的聚合函数的一种泛化形式.该HYB方法相比PBI(Penalty-based Boundary Intersection)方法更强调收敛性,能更容易地在收敛性散布性之间达到平衡.本文测试了MOKP问题及DTLZ系列等测试函数,并与其他算法进行了实验对比,结果显示HYB方法更稳定有效且种群在收敛速度上有一定的提高.展开更多
Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model base...Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.展开更多
Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review exp...Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential.展开更多
为了准确、快速地求解电力系统环境经济调度(environmental economic dispatching,EED)问题,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)应用于电力调度领域,提出了基于MOEA/D...为了准确、快速地求解电力系统环境经济调度(environmental economic dispatching,EED)问题,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)应用于电力调度领域,提出了基于MOEA/D的多目标环境经济调度算法。该算法首先采用Tchebycheff法将整个EED Pareto最优前沿的逼近问题分解为一定数量的单目标优化子问题,然后利用差分进化同时求解这些子问题,并在算法中加入约束处理及归一化操作,以获得最优的带约束EED问题的调度方案。最后,应用模糊集理论为决策者提供最优折中解。对IEEE 30节点测试系统进行仿真计算,并与其它智能优化算法的调度方案对比。结果表明,该算法有效可行,且具有很好的收敛速度和求解精度。展开更多
目前,大多数多目标进化算法采用为单目标优化所设计的重组算子.通过证明或实验分析了几个典型的单目标优化重组算子并不适合某些多目标优化问题.提出了基于分解技术和混合高斯模型的多目标优化算法(multiobjective evolutionary algorit...目前,大多数多目标进化算法采用为单目标优化所设计的重组算子.通过证明或实验分析了几个典型的单目标优化重组算子并不适合某些多目标优化问题.提出了基于分解技术和混合高斯模型的多目标优化算法(multiobjective evolutionary algorithm based on decomposition and mixture Gaussian models,简称MOEA/D-MG).该算法首先采用一个改进的混合高斯模型对群体建模并采样产生新个体,然后利用一个贪婪策略来更新群体.针对具有复杂Pareto前沿的多目标优化问题的测试结果表明,对给定的大多数测试题,该算法具有良好的效果.展开更多
将基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)应用于工程优化问题时,由于各目标函数在数量级及量纲上的不同,需要对目标函数进行归一化处理.首先,采用一种自适应ε约束差分进化...将基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)应用于工程优化问题时,由于各目标函数在数量级及量纲上的不同,需要对目标函数进行归一化处理.首先,采用一种自适应ε约束差分进化算法(εConstrained Differential Evolution,εDE)寻找各个目标在Pareto前沿上的最大值和最小值,利用这些值对各目标进行归一化处理;然后,用MOEA/D进行求解,并在算法中加入了自适应ε约束处理技术;最后,采用一个标准测试问题和一个焊接梁设计优化问题对该算法进行测试,并与其他两种归一化方法进行了比较.根据提出的方法,MOEA/D能对Pareto前沿的一端进行集中优化,因而能处理一些Pareto前沿两端难以优化的问题.展开更多
文摘为了准确地求解组合权重的组合系数,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)思想引入评估领域,提出一种基于MOEA/D的组合权重方法.通常,利用加权和法将组合权重模型转化为单目标模型时,模型加权系数难以准确确定.对此,引入MOEA/D算法的分解思想,将组合权重模型转化为多个单目标子模型.MOEA/D算法仅适用于无约束优化问题,而较为常用的惩罚函数法难以表达进化初期无可行解的情况,因而提出改进自适应惩罚函数(improved adaptive penalty function,IAPF),将组合权重模型转化为无约束优化模型.应用所提出方法与其他方法进行仿真实验,实验结果表明,所提出算法具有有效性.
文摘建立含风电系统的环境经济调度(Environmental Economic Dispatch,EED)模型,提出采用基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)来求解,并在算法中加入约束处理,归一化及变异操作来改善算法的性能并保持解的多样性,以获得理想的Pareto最优前沿.通过仿真计算,并与其他优化算法进行对比分析,验证了MOEA/D算法解决含风电EED问题的可行性和有效性.
文摘基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)是多目标优化算法的一个重要分支,多目标优化的关键问题是如何在算法的收敛性和散布性之间达到良好的平衡.目前主流算法的聚合函数存在着不同的优缺点,尤其是当使用切比雪夫方法选择个体时,经常出现个体偏离权重现象,个体和权重间得不到很好的粘合.本文基于此提出了一种新的聚合函数方法,提高了MOEA/D的性能.该聚合函数的函数形式为二次函数,种群个体在该函数下的等高线是一条二次曲线(本文称双曲线函数方法,Hyperbola Function Method,HYB),是对目前存在的聚合函数的一种泛化形式.该HYB方法相比PBI(Penalty-based Boundary Intersection)方法更强调收敛性,能更容易地在收敛性散布性之间达到平衡.本文测试了MOKP问题及DTLZ系列等测试函数,并与其他算法进行了实验对比,结果显示HYB方法更稳定有效且种群在收敛速度上有一定的提高.
文摘Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.
文摘Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential.
文摘为了准确、快速地求解电力系统环境经济调度(environmental economic dispatching,EED)问题,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)应用于电力调度领域,提出了基于MOEA/D的多目标环境经济调度算法。该算法首先采用Tchebycheff法将整个EED Pareto最优前沿的逼近问题分解为一定数量的单目标优化子问题,然后利用差分进化同时求解这些子问题,并在算法中加入约束处理及归一化操作,以获得最优的带约束EED问题的调度方案。最后,应用模糊集理论为决策者提供最优折中解。对IEEE 30节点测试系统进行仿真计算,并与其它智能优化算法的调度方案对比。结果表明,该算法有效可行,且具有很好的收敛速度和求解精度。
文摘目前,大多数多目标进化算法采用为单目标优化所设计的重组算子.通过证明或实验分析了几个典型的单目标优化重组算子并不适合某些多目标优化问题.提出了基于分解技术和混合高斯模型的多目标优化算法(multiobjective evolutionary algorithm based on decomposition and mixture Gaussian models,简称MOEA/D-MG).该算法首先采用一个改进的混合高斯模型对群体建模并采样产生新个体,然后利用一个贪婪策略来更新群体.针对具有复杂Pareto前沿的多目标优化问题的测试结果表明,对给定的大多数测试题,该算法具有良好的效果.
文摘将基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)应用于工程优化问题时,由于各目标函数在数量级及量纲上的不同,需要对目标函数进行归一化处理.首先,采用一种自适应ε约束差分进化算法(εConstrained Differential Evolution,εDE)寻找各个目标在Pareto前沿上的最大值和最小值,利用这些值对各目标进行归一化处理;然后,用MOEA/D进行求解,并在算法中加入了自适应ε约束处理技术;最后,采用一个标准测试问题和一个焊接梁设计优化问题对该算法进行测试,并与其他两种归一化方法进行了比较.根据提出的方法,MOEA/D能对Pareto前沿的一端进行集中优化,因而能处理一些Pareto前沿两端难以优化的问题.
