在许多经济模型中,决策者需要通过比较集值优化问题的目标函数来衡量支出以达到自身收益的最大。在电子商务视角下,本文讨论了有限理性下基于改进集的集合优化问题E-u-最小解集的Levitin-Polyak良定性和广义Levitin-Polyak良定性,并通...在许多经济模型中,决策者需要通过比较集值优化问题的目标函数来衡量支出以达到自身收益的最大。在电子商务视角下,本文讨论了有限理性下基于改进集的集合优化问题E-u-最小解集的Levitin-Polyak良定性和广义Levitin-Polyak良定性,并通过有限理性模型证明了该良定性的充分条件。此外,借助非线性分析的方法给出了集合优化问题(广义) Levitin-Polyak良定性的特征刻画。这些结果为电子商务在实际生活中的应用打下了夯实的理论基础。In many economic models, decision-makers need to measure expenditures by comparing the objective functions of set-valued optimization problems in order to achieve maximum benefit. Under the perspective of E-commerce, this paper studies the Levitin-Polyak well-posedness and generalized Levitin-Polyak well-posedness of E-u-minimal solution of set optimization problems under bounded rationality via improvement set. Furthermore, the sufficient condition of well-posedness is given by using a bounded rationality model. Besides, we obtain the characterization of (generalized) Levitin-Polyak well-posedness for the problem by utilizing nonlinear analysis method. These results have laid a solid theoretical foundation for the application of E-commerce in practical life.展开更多
The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically inv...The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the per- formance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and im- proper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.展开更多
A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrai...A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Com- parison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.展开更多
Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal...Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This paper presents a hybrid swarm intelligence ap-proach (HSIA) for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. HSIA provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Comparison testing of several examples of mixed-variable optimization problems in the literature showed that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.展开更多
文摘在许多经济模型中,决策者需要通过比较集值优化问题的目标函数来衡量支出以达到自身收益的最大。在电子商务视角下,本文讨论了有限理性下基于改进集的集合优化问题E-u-最小解集的Levitin-Polyak良定性和广义Levitin-Polyak良定性,并通过有限理性模型证明了该良定性的充分条件。此外,借助非线性分析的方法给出了集合优化问题(广义) Levitin-Polyak良定性的特征刻画。这些结果为电子商务在实际生活中的应用打下了夯实的理论基础。In many economic models, decision-makers need to measure expenditures by comparing the objective functions of set-valued optimization problems in order to achieve maximum benefit. Under the perspective of E-commerce, this paper studies the Levitin-Polyak well-posedness and generalized Levitin-Polyak well-posedness of E-u-minimal solution of set optimization problems under bounded rationality via improvement set. Furthermore, the sufficient condition of well-posedness is given by using a bounded rationality model. Besides, we obtain the characterization of (generalized) Levitin-Polyak well-posedness for the problem by utilizing nonlinear analysis method. These results have laid a solid theoretical foundation for the application of E-commerce in practical life.
基金Project (No. 20276063) supported by the National Natural Sci-ence Foundation of China
文摘The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the per- formance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and im- proper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.
基金Project (Nos. 60074040 and 6022506) supported by the NationalNatural Science Foundation of China
文摘A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Com- parison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.
基金Project supported by the National Natural Science Foundation ofChina (Nos. 60074040 6022506) and the Teaching and ResearchAward Program for Outstanding Young Teachers in Higher Edu-cation Institutions of China
文摘Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This paper presents a hybrid swarm intelligence ap-proach (HSIA) for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. HSIA provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Comparison testing of several examples of mixed-variable optimization problems in the literature showed that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.
