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Nonconvex composite multiobjective nonsmooth fractional programming

JOURNAL OF INEQUALITIES AND APPLICATIONS 2013年第1期2013卷 1-10页

作者： Kim, Ho Jung Kim, Do Sang Pukyong Natl Univ Dept Appl Math Pusan 608737 South Korea

We consider nonsmooth multiobjective programs where the objective function is a fractional composition of invex functions and locally Lipschitz and G teaux differentiable functions. Kuhn-Tucker necessary and sufficient optimality conditions for weakly efficient solutions are presented. We formulate dual problems and establish weak, strong and converse duality theorems for a weakly efficient solution.

关键词： composite functions fractional programming nonsmooth programming multiobjective problems necessary and sufficient conditions duality

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multiobjective spanning tree based optimization model to political redistricting

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SPATIAL INFORMATION RESEARCH 2018年第3期26卷 317-325页

作者： Kim, Myung Jin GBSA Policy Res Div Suwon Gyeonggi South Korea

A successful political redistricting plan should be neutral by satisfying a number of requirements, such as equal population, and contiguity as well as compactness. Modeling approaches to solving this problem becomes either computationally intensive when the contiguity requirement is explicitly addressed, and requirements are used as more than one objective In this paper, a multiobjective mixed integer optimization model was developed based on developments in solving land acquisition problems. Using the approach, geographical criteria such as contiguity and compactness can be explicitly addressed. Tradeoff between compactness and equal population can be evaluated. The use of the approach based on synthetic data was demonstrated using a set of experiments.

关键词： Political redistricting Optimization multiobjective problems Mixed integer programming Spanning tree model

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GDE3: The third evolution step of generalized differential evolution

GDE3: The third evolution step of generalized differential e...

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IEEE Congress on Evolutionary Computation

作者： Kukkonen, S Lampinen, J Lappeenranta Univ Technol Dept Informat Technol FIN-53851 Laapeenranta Finland

ISBN: (纸本)0780393635

A developed version of Generalized Differential Evolution, GDE3, is proposed. GDE3 is an extension of Differential Evolution (DE) for global optimization with an arbitrary number of objectives and constraints. In the case of a problem with a single objective and without constraints GDE3 falls back to the original DE. GDE3 improves earlier GDE versions in the case of multiobjective problems by giving a better distributed solution. Performance of GDE3 is demonstrated with a set of test problems and the results are compared with other methods.

关键词： constraint theory evolutionary computation optimisation GDE3 generalized differential evolution global optimization multiobjective problems

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Solving Biobjective Set Covering Problem Using Binary Cat Swarm Optimization Algorithm 16th

Solving Biobjective Set Covering Problem Using Binary Cat Sw...

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16th International Conference on Computational Science and Its Applications (ICCSA)

作者： Crawford, Broderick Soto, Ricardo Caballero, Hugo Olguin, Eduardo Misra, Sanjay Pontificia Univ Catolica Valparaiso Valparaiso 2362807 Chile Univ Cent Chile Santiago 8370178 Chile Univ San Sebastian Fac Ingn & Tecnol Bellavista 7 Santiago 8420524 Chile Univ Autonoma Chile Santiago 7500138 Chile Univ Cient Sur Lima 18 Peru Covenant Univ Ogun 110001 Nigeria

ISBN: (纸本)9783319420851

The set cover problem is a classical question in combinatorics, computer science and complexity theory. It is one of Karp's 21 NP-complete problems shown to be NP-complete in 1972. Several algorithms have been proposed to solve this problem, based on genetic algorithms (GA), Particle Swarm Optimizer (PSO) and in recent years algorithms based in behavior algorithms based groups or herds of animals, such as frogs, bats, bees and domestic cats. This work presents the basic features of the algorithm based on the behavior of domestic cats and results to solve the SCP bi-objective, experimental results and opportunities to improve results using adaptive techniques applied to Cat Swarm Optimization. For this purpose we will use instances of SCP OR-Library of Beasley by adding an extra function fitness to transform the Beasly instance to Bi-Objective problem.

关键词： multiobjective problems Evolutionary algorithm Swarm optimization Cat swarm optimization multiobjective cat swarm optimization Pareto dominance

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Generalized intuitionistic fuzzy programming for non-linear multiobjective optimization using t-norms and t-conorms

Generalized intuitionistic fuzzy programming for non-linear ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Chauhan, Abhishek Mahajan, Sumati Deemed Be Univ Dept Math Punjab Engn Coll Chandigarh India

ISBN: (纸本)9798350319552;9798350319545

In real-world scenarios, decision-makers are often faced with the problem of optimizing several conflicting objectives governed by non-linear functions. In such cases, non-linear multi-objective programming problems are an important tool. However, as such, these cannot incorporate the underlying imprecision and vagueness of data. Therefore, to better accommodate the associated ambiguity of realistic circumstances, uncertain non-linear multiobjective programming problems are used. To optimize multiple objectives together, a compromise or Pareto-optimal solution is obtained using intuitionistic fuzzy programming (IFP). Through this article, we attempt to extend this widely used technique by utilizing t-norms and t-conorms of fuzzy sets. The proposed method generalizes the prevalent IFP and provides a decision-maker-friendly approach. Afterwards, a numerical illustration is presented using the proposed methodology with various t-norms and t-conorms and then the results are compared amongst themselves as well as with several predominant studies.

关键词： Intuitionistic fuzzy programming multiobjective problems t-norms and t-conorms Non-linear optimization

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Design of Switching Circuits based on Particle Swarm Optimizer and Hybrid Fitness Function

Design of Switching Circuits based on Particle Swarm Optimiz...

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36th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)/4th IEEE International Conference on E-Learning in Industrial Electronics/IES Industry Forum

作者： Kawamura, Kengo Saito, Toshimichi Hosei Univ EE Dept Tokyo 1848584 Japan

ISBN: (纸本)9781424452262

This paper studies application of the particle swarm optimizer to finding suitable parameters for multi-objective problems in design of switching circuits. The problem is described by the hybrid fitness function consisting of analog objective functions of the parameters, criterion and digital logic. The hybrid fitness function permits increase of some fitness component(s) below the criterion and this flexibility can realize good performance. We then consider an example: optimization of switching angles of the inverters. In this application, each particle corresponds to switching angles of the inverters and moves to improve the total harmonic distortion and average power. Performing basic numerical experiments, the algorithm efficiency is confirmed.

关键词： analog objective functions digital logic harmonic distortion harmonic distortion hybrid fitness function logic circuits multiobjective problems particle swarm optimisation particle swarm optimizer switching angles switching circuits

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A Bi-Objetive Cat Swarm Optimization Algorithm for Set Covering Problem 5th

A Bi-Objetive Cat Swarm Optimization Algorithm for Set Cover...

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5th Computer Science On-line Conference (CSOC)

作者： Crawford, Broderick Soto, Ricardo Caballero, Hugo Olguin, Eduardo Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ San Sebastian Santiago Metropolitan Re Chile Univ Cent Chile Santiago Metropolitan Re Chile Univ Autonoma Chile Temuco Chile Univ Cientif Sur Lima Peru

ISBN: (纸本)9783319336251;9783319336237

In this paper, we study a classical problem in combinatorics and computer science, Set Covering Problem. It is one of Karp's 21 NP-complete problems, using a new and original metaheuristic, Cat Swarm Optimization. This algorithm imitates the domestic cat through two states: seeking and tracing mode. The OR-Library of Beasley instances were used for the benchmark with additional fitness function, thus the problem was transformed from Mono-objective to Bi-objective. The Cat Swarm Optimization finds a set solution non-dominated based on Pareto concepts, and an external file for storing them. The results are promising for further continue in future work optimizing this problem.

关键词： multiobjective problems Evolutionary algorithm Swarm optimization Cat swarm optimization multiobjective cat swarm optimization Pareto dominance

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Taguchi-based Disturbance with Tournament Selection to Improve on MOPSO

Taguchi-based Disturbance with Tournament Selection to Impro...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Lin, Chi-nien Huo, Chih-Li Lin, Shu-Yan Yu, Yu-Hsiang Sun, Tsung-Ying Natl Dong Hwa Univ Dept Elect Engn Hualien Taiwan

ISBN: (纸本)9781424478354

In this paper, a Taguchi-based disturbance mechanism is deployed in our preliminary study on MOPSO. The proposed scenario includes tournament selection for global best solutions, jump-improved operation to expand the searching space, cluster operation to improve the diversity, and Taguchi-based disturbance can enhance the searching ability and reduce the possibility of falling into local optima of particles. Experiments are conducted on seven two-objective benchmarks. The results show that the proposed method operates better than other algorithms in three performance metrics.

关键词： multiobjective problems particle swarm optimization (PSO) Taguchi-based Disturbance mechanism Tournament Selection

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Evaluating Components of Artificial Immune Algorithms: A Performance-aware Method based on Evolutionary Calibrator

Evaluating Components of Artificial Immune Algorithms: A Per...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Montero, Elizabeth Riff, Maria-Cristina Univ Tecn Federico Santa Maria Casilla 110-V Valparaiso Chile

ISBN: (纸本)9781479938407

We are interested in methods and strategies that allow us to simplify bio-inspired algorithms without reducing their accuracy. These algorithms are usually designed and implemented adding new components incrementally which makes inherently difficult to understand the relation between them and their individual contribution to the algorithm performance. In this paper, the information obtained when using a tuner to identify a set of good parameter values is analyzed and a method to use this tuner in order to help us to take design decisions is proposed. Our results are shown and our approach is validated using an artificial immune algorithm which has been proposed to solve multi-objective problems. The results show that these decisions lead to a code that is shorter than that of the initial algorithm while maintaining its performance.

关键词： artificial immune systems evolutionary computation artificial immune algorithms bio-inspired algorithms design decisions evolutionary calibrator multiobjective problems performance-aware method Algorithm design and analysis Cloning Immune system Sociology Statistics Tuners

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Fission-and-Recombination Particle Swarm Optimizers for Search of Multiple Solutions 21

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21st International Conference on Neural Information Processing (ICONIP)

作者： Sato, Takumi Saito, Toshimichi Hosei Univ Koganei Tokyo 1848584 Japan

ISBN: (纸本)9783319126401

This paper presents the fission-and-recombination particle swarm optimizer (FRPSO) and its application to search of periodic points of a nonlinear dynamical system. The search problem is translated into a multi-solution problem evaluated in a multi-objective problem. The FRPSO is based on the ring topology. The FRPSO is effective to escape from trap of partial/local solutions and to find all the solutions. Performing basic numerical experiments, the algorithm efficiency is investigated.

关键词： particle swarm optimizers multi-solution problems multiobjective problems Henon map

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