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2nd International Conference on Engineering Design and optimization (ICEDO 2011)

作者： Yong, Longquan Shaanxi Univ Technol Hanzhong 723001 Shaanxi Provinc Peoples R China

ISBN: (纸本)9783037852590

A new method is proposed for the linear complementarity problem (LCP). Firstly we formulate the LCP into a multiobjective optimization problem (MOP), and study the relations between the efficient solution of MOP and optimal solution of LCP. Based on the efficient solution of MOP, we define zero-efficient solution. Then we indicate that zero-efficient solution of the MOP is also the solution to the LCP. Finally some standard LCP examples are respectively transformed into MOP and solved by minimax method. Numerical results indicate that the proposed method is effective.

关键词： Linear complementarity problem multiobjective optimization problem Zero-efficient solution Minimax method

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Hybrid multiobjective Evolutionary Algorithm for Assembly Line Balancing problem with Stochastic Processing Time

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Procedia Computer Science 2014年 36卷 587-592页

作者： Wenqiang Zhang Weitao Xu Mitsuo Gen College of Information Science and Engineering Henan University of Technology China Fuzzy Logic Systems Institute Tokyo University of Science Japan

An assembly line (AL) is a typical manufacturing process consisting of various tasks in which interchangeable parts are added to a product in a sequential manner at a station to produce a final product. Most of the work related to the ALs concentrate on the assembly line balancing (ALB) which deals with the allocation of the tasks among stations so that the precedence relations among them are not violated and a given objective function is optimized. From the view point of the real ALB systems, multiobjective ALB with stochastic processing time (S-moALB) is an important and practical topic from traditional ALB problem involving conflicting criteria such as the cycle time, variation of workload, and/or the processing cost under uncertain manufacturing environment. This paper proposes a hybrid multiobjective evolutionary algorithm (hMOEA) to deal with such S-moALB problem with stochastic processing time considering minimization of the cycle time and the processing cost, given a fixed number of stations available. The special fitness function strategy is adopted and a hybrid selection mechanism is designed to improve the convergence and distribution performance. Experimental results with various instances show that hMOEA could get the better convergence distribution performance than existing MOEAs.

关键词： Assembly line balancing (ALB) Stochastic processing time multiobjective optimization problem multiobjective evolutionary algorithm (MOEA)

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multiobjective Virtual Machine Placement in Cloud Environment

Multiobjective Virtual Machine Placement in Cloud Environmen...

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International Conference on Cloud and Ubiquitous Computing and Emerging Technologies (CUBE)

作者： Adamuthe, Amol C. Pandharpatte, Rupali M. Thampi, Gopakumaran T. RIT Dept CSE Islampur MS India TSEC Dept IT Bombay Maharashtra India

ISBN: (纸本)9781479922345;9781479922352

In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of Genetic algorithms, Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.

关键词： Nondominated Sorting Genetic Algorithm Resource allocation multiobjective optimization problem

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Density estimation for selecting leaders and mantaining archive in MOPSO

Density estimation for selecting leaders and mantaining arch...

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IEEE Congress on Evolutionary Computation

作者： Hu, Wang Yen, Gary G. Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Sichuan Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

ISBN: (纸本)9781479904549;9781479904532

Leader selection and archive maintenance are the two key issues, which have an important impact on the performance of the obtained approximate Pareto front, to be tackled when extending Single-Objective Particle Swarm optimization to Multi-Objective Particle Swarm optimization (MOPSO). In this paper, a new method of density estimation is proposed for selecting leaders and maintaining archive in MOPSO. The density of a nondominated solution in archive is calculated according to the Parallel Cell Distance after the archive is mapped from Cartesian Coordinate System into Parallel Cell Coordinate System. A new MOPSO is proposed based on this method of density estimation for selecting leaders and maintaining archive to improve the performance of convergence and diversity. The experimental results show that the proposed algorithm is significantly superior to the five chosen state-of-the-art MOPSOs on 12 test problems in term of hypervolume performance indicator.

关键词： particle swarm optimization multiobjective optimization problem evolutionary computation multiobjective particle swarm optimization

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multiobjective PARTICLE SWARM optimization BASED ON DIMENSIONAL UPDATE

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2013年第3期22卷 1350015-1350015页

作者： Xu, Heming Wang, Yinglin Xu, Xin Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

For multiobjective particle swarm optimization (MOPSO), two particles may be incomparable, i.e., not dominated by each other. The personal best and the global best for the particle become less optimal, thus the convergence becomes slow. Even worse, an archive of a limited size can not cover the entire region dominated by the Pareto front, the uncovered region can contain unidentifiable nondominated solutions that are not optimal, and thus the precision the algorithm achieves encounters a plateau. Therefore we propose dimensional update, i.e., evaluating the particle's fitness after updating each variable of its position. Separate consideration of the impact of each variable decreases the occurrence of incomparable relations, thus improves the performance. Experimental results validate the efficiency of our algorithm.

关键词： multiobjective optimization problem particle swarm optimization dimensional update personal best archive learning

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multiobjective optimization Method for Solving Nonlinear Complementarity problem

Multiobjective Optimization Method for Solving Nonlinear Com...

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23rd Chinese Control and Decision Conference

作者： Yong, Longquan Shaanxi Univ Technol Dept Math Hanzhong 723001 Shaanxi Peoples R China

ISBN: (纸本)9781424487363

In last decades, there has been much effort on the solution and the analysis of the nonlinear complementarity problem (NCP) by reformulating NCP as an unconstrained minimization. In this paper, we propose a new method for the NCP under the condition that the solution exists. We formulate the NCP into a multiobjective optimization problem (MOP), and define zero-efficient solution of MOP. Then we indicate that zero-efficient solution of the MOP is also the solution to the NCP. Finally we use minimax method for the MOP. Numerical results indicate that the proposed method is promising.

关键词： Nonlinear complementarity problem multiobjective optimization problem Zero-efficient solution Minimax method

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multiobjective optimization problems with modified objective functions and cone constraints and applications

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JOURNAL OF GLOBAL optimization 2011年第1期49卷 137-147页

作者： Chen, Jia Wei Cho, Yeol Je Kim, Jong Kyu Li, Jun Gyeongsang Natl Univ Dept Math Educ Chinju 660701 South Korea Gyeongsang Natl Univ RINS Coll Educ Chinju 660701 South Korea China W Normal Univ Sch Math & Informat Nanchong 637002 Sichuan Peoples R China Kyungnam Univ Dept Math Masan 631701 South Korea

In this paper, we consider a differentiable multiobjective optimization problem with generalized cone constraints (for short, MOP). We investigate the relationship between weakly efficient solutions for (MOP) and for the multiobjective optimization problem with the modified objective function and cone constraints [for short, (MOP) (eta) (x)] and saddle points for the Lagrange function of (MOP) (eta) (x) involving cone invex functions under some suitable assumptions. We also prove the existence of weakly efficient solutions for (MOP) and saddle points for Lagrange function of (MOP) (eta) (x) by using the Karush-Kuhn-Tucker type optimality conditions under generalized convexity functions. As an application, we investigate a multiobjective fractional programming problem by using the modified objective function method.

关键词： multiobjective optimization problem Q-(pseudo)invex Q-convexlike Weakly efficient solution Saddlepoint KKT condition Weak (strong, converse) duality Lagrange function

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The Exact Solution of the Environmental/Economic Dispatch problem

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IEEE TRANSACTIONS ON POWER SYSTEMS 2012年第2期27卷 723-731页

作者： Bayon, Luis Grau, Jose M. Ruiz, Maria M. Suarez, Pedro M. Univ Oviedo Dept Math Gijon Spain

In this paper, the exact analytical solution for the environmental/economic dispatch (EED) optimization problem is presented for the first time. The EED, which simultaneously satisfies multiple contradictory criteria, is stated as a multiobjective optimization problem (MOP). Our paper has improved several aspects of a previous analytical approach. First, we take into account the unit capacity constraints in the exact formulae. Second, we obtain the set of compromise solutions known as Pareto optimal solutions and, third, our treatment of transmission losses satisfies the power balance constraint. In contrast with the known heuristic methods used in the literature, which only provide a reasonable solution (suboptimal, nearly global optimal), our method provides the global solution. Moreover, our method can obtain the Pareto optimal set under different loading conditions. The performance of the proposed technique is validated using a standard test system.

关键词： Environmental/economic dispatch multiobjective optimization problem Pareto optimal set transmission losses

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Variational principles and optimal solutions of the inverse problems of creep bending of plates

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JOURNAL OF APPLIED MECHANICS AND TECHNICAL PHYSICS 2012年第5期53卷 751-760页

作者： Bormotin, K. S. Oleinikov, A. I. Komsomolsk On Amur State Tech Univ Komsomolsk On Amur 681013 Russia

It is shown that inverse problems of steady-state creep bending of plates in both the geometrically linear and nonlinear formulations can be represented in a variational formulation. Steady-state values of the obtained functionals corresponding to the solutions of the problems of inelastic deformation and elastic unloading are determined by applying a finite element procedure to the functionals. Optimal laws of creep deformation are formulated using the criterion of minimizing damage in the functionals of the inverse problems. The formulated problems are reduced to the problems solved by the finite element method using *** software.

关键词： inverse creep problem damage variational principles multiobjective optimization problem optimal control

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A Modified Marriage in Honey-Bee optimization for multiobjective optimization problems

A Modified Marriage in Honey-Bee Optimization for Multiobjec...

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IEEE International Conference Intelligent Systems

作者： Patcharawadee Poolsamran Arit Thammano Computational Intelligence Laboratory Faculty of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9781467322768

This paper proposes a modified marriage in honey-bee optimization for solving multiobjective optimization problems. Unlike the original marriage in honey-bee optimization, the proposed algorithm divides the objective space into several colonies, each of which has its own queen. The fitness of each solution is based on 3 parameters: the size of the colony, the number of dominating solutions, and the number of dominated solutions. The nondominated solutions with highest fitness values are preferentially assigned to be the queens while the rest are assigned to be the drones. Next, all drones are assigned to the colony according to their distances from the queens of the colonies. In order to maximize a genetic variance in the population, the multiple mating is used. The multiple mating requires the queen to mate with drones from the other colonies. The proposed algorithm has been evaluated and compared to two state-of-the-art metaheuristic algorithms: the Pareto archived evolution strategy and the nondominated sorting genetic algorithm. The experimental results on 5 different ZDT benchmark functions illustrate that the proposed algorithm is able to converge to the true Pareto fronts and has better spread of solutions, as compared with the published results of the two state-of-the-art algorithms.

关键词： swarm intelligence marriage in honey-bee optimization multiobjective optimization problem queens drone honey bees colonies Marital Relationship Marriage Solutions evolution strategy Mating BISTABILITY Queens

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