The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly...The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.展开更多
Over the years, a number of methods have been proposed for the generation of uniform and globally optimal Pareto frontiers in multi-objective optimization problems. This has been the case irrespective of the problem d...Over the years, a number of methods have been proposed for the generation of uniform and globally optimal Pareto frontiers in multi-objective optimization problems. This has been the case irrespective of the problem definition. The most commonly applied methods are the normal constraint method and the normal boundary intersection method. The former suffers from the deficiency of an uneven Pareto set distribution in the case of vertical （or horizontal） sections in the Pareto frontier, whereas the latter suffers from a sparsely populated Pareto frontier when the optimization problem is numerically demanding （ill-conditioned）. The method proposed in this paper, coupled with a simple Pareto filter, addresses these two deficiencies to generate a uniform, globally optimal, well-populated Pareto frontier for any feasible bi-objective optimization problem. A number of examples are provided to demonstrate the performance of the algorithm.展开更多
针对高比例清洁能源接入配电网的背景,对柔性开关(soft open point,SOP)与需求响应(demand response,DR)接入主动配电网优化重构问题展开研究,提出了考虑SOP和DR的配电网重构策略。首先,构建配电网的SOP与DR模型,以综合考虑损耗费用、...针对高比例清洁能源接入配电网的背景,对柔性开关(soft open point,SOP)与需求响应(demand response,DR)接入主动配电网优化重构问题展开研究,提出了考虑SOP和DR的配电网重构策略。首先,构建配电网的SOP与DR模型,以综合考虑损耗费用、运营成本与负载不均衡度为优化目标,建立计及SOP与DR的多目标主动配电网优化重构模型。然后,采用一种改进的规格化法平面约束法(normalized normal constraint,NNC)求解该三目标优化问题的完整Pareto前沿以获得折中最优解。最后以IEEE 33节点配电系统为例进行验证。结果表明综合考虑SOP与DR的配电网重构策略可以有效提升配电网的经济性,提高清洁能源的消纳能力,以及降低系统线路的负载不均衡度。展开更多
本文以三跨六层平面钢框架的结构总质量和总动应变能最小作为优化目标,结合Pareto最优解理论与拥挤距离机制提出了一种新的适用于钢结构抗震优化设计的多目标算法:多目标快速群搜索算法MQGSO(Multi-objective Quick Group Search Optimi...本文以三跨六层平面钢框架的结构总质量和总动应变能最小作为优化目标,结合Pareto最优解理论与拥挤距离机制提出了一种新的适用于钢结构抗震优化设计的多目标算法:多目标快速群搜索算法MQGSO(Multi-objective Quick Group Search Optimization)。通过振型分解反应谱法进行结构分析,优化的计算结果表明:该算法在处理带约束的平面钢框架的不同抗震性能多目标优化时,具有良好的收敛效果和较快的收敛速度,且Pareto前沿分布均匀宽泛,可为钢框架结构抗震优化设计提供可行的设计方案。展开更多
基金the Liaoning Province Nature Fundation Project(2022-MS-291)the National Programme for Foreign Expert Projects(G2022006008L)+2 种基金the Basic Research Projects of Liaoning Provincial Department of Education(LJKMZ20220781,LJKMZ20220783,LJKQZ20222457)King Saud University funded this study through theResearcher Support Program Number(RSPD2023R704)King Saud University,Riyadh,Saudi Arabia.
文摘The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.
文摘Over the years, a number of methods have been proposed for the generation of uniform and globally optimal Pareto frontiers in multi-objective optimization problems. This has been the case irrespective of the problem definition. The most commonly applied methods are the normal constraint method and the normal boundary intersection method. The former suffers from the deficiency of an uneven Pareto set distribution in the case of vertical （or horizontal） sections in the Pareto frontier, whereas the latter suffers from a sparsely populated Pareto frontier when the optimization problem is numerically demanding （ill-conditioned）. The method proposed in this paper, coupled with a simple Pareto filter, addresses these two deficiencies to generate a uniform, globally optimal, well-populated Pareto frontier for any feasible bi-objective optimization problem. A number of examples are provided to demonstrate the performance of the algorithm.
文摘针对高比例清洁能源接入配电网的背景,对柔性开关(soft open point,SOP)与需求响应(demand response,DR)接入主动配电网优化重构问题展开研究,提出了考虑SOP和DR的配电网重构策略。首先,构建配电网的SOP与DR模型,以综合考虑损耗费用、运营成本与负载不均衡度为优化目标,建立计及SOP与DR的多目标主动配电网优化重构模型。然后,采用一种改进的规格化法平面约束法(normalized normal constraint,NNC)求解该三目标优化问题的完整Pareto前沿以获得折中最优解。最后以IEEE 33节点配电系统为例进行验证。结果表明综合考虑SOP与DR的配电网重构策略可以有效提升配电网的经济性,提高清洁能源的消纳能力,以及降低系统线路的负载不均衡度。
文摘本文以三跨六层平面钢框架的结构总质量和总动应变能最小作为优化目标,结合Pareto最优解理论与拥挤距离机制提出了一种新的适用于钢结构抗震优化设计的多目标算法:多目标快速群搜索算法MQGSO(Multi-objective Quick Group Search Optimization)。通过振型分解反应谱法进行结构分析,优化的计算结果表明:该算法在处理带约束的平面钢框架的不同抗震性能多目标优化时,具有良好的收敛效果和较快的收敛速度,且Pareto前沿分布均匀宽泛,可为钢框架结构抗震优化设计提供可行的设计方案。
