A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems....A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.展开更多
An integer linear bilevel programming problem is firstly transformed into a binary linear bilevel programming problem, and then converted into a single-level binary implicit programming. An orthogonal genetic algorith...An integer linear bilevel programming problem is firstly transformed into a binary linear bilevel programming problem, and then converted into a single-level binary implicit programming. An orthogonal genetic algorithm is developed for solving the binary linear implicit programming problem based on the orthogonal design. The orthogonal design with the factor analysis, an experimental design method is applied to the genetic algorithm to make the algorithm more robust, statistical y sound and quickly convergent. A crossover operator formed by the orthogonal array and the factor analysis is presented. First, this crossover operator can generate a smal but representative sample of points as offspring. After al of the better genes of these offspring are selected, a best combination among these offspring is then generated. The simulation results show the effectiveness of the proposed algorithm.展开更多
对已有梯级水电站进行融合改造,增建抽水蓄能机组形成梯级混合式抽水蓄能电站,是加快抽水蓄能发展的有效途径之一。梯级混合式抽水蓄能电站相较于常规梯级电站,新增具备抽水运行工况,相较于传统抽水蓄能电站,又具有更加复杂梯级水力联系...对已有梯级水电站进行融合改造,增建抽水蓄能机组形成梯级混合式抽水蓄能电站,是加快抽水蓄能发展的有效途径之一。梯级混合式抽水蓄能电站相较于常规梯级电站,新增具备抽水运行工况,相较于传统抽水蓄能电站,又具有更加复杂梯级水力联系,灵活的运行模式需要更为精细化的调度建模方法。为探索其典型调峰运行模式,提出了梯级混合式抽水蓄能电站短期调峰优化模型。该模型以电网剩余负荷峰谷差最小为目标,以机组为最小调度单元,针对不同类型机组的抽-发运行工况采用差异化建模。在模型求解方面,通过线性转换方法将原有非线性模型转化为混合整数线性规划(mixed integer linear programming,MILP)模型,然后在JAVA环境中运用CPLEX数学工具进行求解。以西南某梯级水电站为实例的分析结果表明,梯级混合式抽水蓄能电站相较于常规梯级电站电网剩余负荷峰谷差减少4.6%。展开更多
基金Projects(50275150,61173052) supported by the National Natural Science Foundation of ChinaProject(14FJ3112) supported by the Planned Science and Technology of Hunan Province,ChinaProject(14B033) supported by Scientific Research Fund Education Department of Hunan Province,China
文摘A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.
基金supported by the Fundamental Research Funds for the Central Universities(K50511700004)the Natural Science Basic Research Plan in Shaanxi Province of China(2013JM1022)
文摘An integer linear bilevel programming problem is firstly transformed into a binary linear bilevel programming problem, and then converted into a single-level binary implicit programming. An orthogonal genetic algorithm is developed for solving the binary linear implicit programming problem based on the orthogonal design. The orthogonal design with the factor analysis, an experimental design method is applied to the genetic algorithm to make the algorithm more robust, statistical y sound and quickly convergent. A crossover operator formed by the orthogonal array and the factor analysis is presented. First, this crossover operator can generate a smal but representative sample of points as offspring. After al of the better genes of these offspring are selected, a best combination among these offspring is then generated. The simulation results show the effectiveness of the proposed algorithm.
文摘对已有梯级水电站进行融合改造,增建抽水蓄能机组形成梯级混合式抽水蓄能电站,是加快抽水蓄能发展的有效途径之一。梯级混合式抽水蓄能电站相较于常规梯级电站,新增具备抽水运行工况,相较于传统抽水蓄能电站,又具有更加复杂梯级水力联系,灵活的运行模式需要更为精细化的调度建模方法。为探索其典型调峰运行模式,提出了梯级混合式抽水蓄能电站短期调峰优化模型。该模型以电网剩余负荷峰谷差最小为目标,以机组为最小调度单元,针对不同类型机组的抽-发运行工况采用差异化建模。在模型求解方面,通过线性转换方法将原有非线性模型转化为混合整数线性规划(mixed integer linear programming,MILP)模型,然后在JAVA环境中运用CPLEX数学工具进行求解。以西南某梯级水电站为实例的分析结果表明,梯级混合式抽水蓄能电站相较于常规梯级电站电网剩余负荷峰谷差减少4.6%。
