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International Conference on Materials Science and Information Technology (MSIT 2011)

作者： Sun Ying Chen Ting-gui Zhejiang Gongshang Univ Coll Comp Sci & Informat Engn Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9783037853191

Based on the best producting plan for multi-objective question, making the simulation model by increasing anti-disruption factors,or increasing the cost and the budget through undertaking the research on the inner system and the outer system's disruption At the same time drawing the most close scheme by using the classical genetic algorithm for multiple-objective problem. The experiment shows that the system of internal and external factors causing the very different results to the allocation, but increasing the allocation costs and the opportunity costs is necessary in the actual operation.

关键词： genetic algorithm multi-objective problem disruption simulation

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Matrix Method for Non-Dominated Sorting and Population Selection for Next Generation in multi-objective problem Solution 8

Matrix Method for Non-Dominated Sorting and Population Selec...

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8th IEEE International Conference on Cloud Computing, Data Science and Engineering (Confluence) / Global Technology, Innovation and Enterpreneurship Summit

作者： Rajpoot, Prince Dwivedi, Pragya Motilal Nehru Natl Inst Technol Allahabad Comp Sci & Engn Allahabad Uttar Pradesh India

ISBN: (纸本)9781538617199

The multi-objective problem solution methods consider two or more conflicting objectives simultaneously. The method selects the initial population and rectifies it for generating better solution using different operations like: crossover, mutation etc. Finally, it generates pareto-optimal solutions for the given problem after satisfying sonic conditions. In order to rectify the current population, after performing operations on the population, it generates fronts and selects the best populations from the higher to lower fronts according to the needs. Crowding distance concept comes into existence and plays an important role when the number of solutions presents in the particular front has been exceeded then the remaining needed solutions. Due to the limitation of time, we need to stop the rectifying process after a limited period of time. It shows that due to its higher convergence power, the algorithm with lower computational complexity can generate efficient solution within that period of time as compared to the algorithm with higher computational complexity. In recent years, several algorithms provide solutions for multi-objective optimization problem based on non-dominated sorting but the computational complexity of such algorithms is high and implementation in complex ill nature. In this paper, we propose an alternative method based on matrix approach for front generation and selection of next-generation population so that it can magnify the convergence power by decreasing the time consumption in these phases of algorithms. The computational complexity of the proposed method is calculated. The proposed algorithm has been applied to various cases of a case study and it selects the same population for next generation as NSGA algorithm, but with a lower computation effort. This algorithm is also very easy to learn and implement.

关键词： multi-objective problem fronts generation non-dominant solution population selection

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Weighted Linear Fractional Programming for Possibilistic multi-objective problem 4

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4th INNS Symposia on Computational Intelligence in Information Systems (INNS-CIIS)

作者： Arbaiy, Nureize Univ Tun Hussein Onn Malaysia Batu Pahat 86400 Malaysia

ISBN: (纸本)9783319131535;9783319131528

The assessment of the weights of objective function plays an important role in a multi-objective process. This paper discusses a weighting method for linear fractional programming to solve possibilistic programming of the multi-objective decision-making problem. The minimal and maximal values of the objective function are utilized in the determination the weight value. This analysis concludes that it is worthwhile to pursue proposed solution approach to the multi-objective evaluation scheme, which addresses some limitation to determine the weight values.

关键词： Weighting method multi-objective problem Possibilistic Programming Linear fractional

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Fuzzy Goal Programming for multi-level multi-objective problem: An Additive Model

Fuzzy Goal Programming for Multi-level Multi-objective Probl...

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2nd International Conference on Software Engineering and Computing Systems (ICSECS 2011)

作者： Arbaiy, Nureize Watada, Junzo Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9783642221903

The coordination of decision authority is noteworthy especially in a complex multi-level structured organization, which faces multi-objective problems to achieve overall organization targets. However, the standard formulation of mathematical programming problems assumes that a single decision maker made the decisions. Nevertheless it should be appropriate to establish the formulations of mathematical models based on multi-level programming method embracing multiple objectives. Yet, it is realized that sometimes estimating the coefficients of objective functions in the multi-objective model are difficult when the statistical data contain random and fuzzy information. Hence, this paper proposes a fuzzy goal programming additive model, to solve a multi-level multi-objective problem in a fuzzy environment, which can attain a satisfaction solution. A numerical example of production planning problem illustrates the proposed solution approach and highlights its advantages that consider the inherent uncertainties in developing the multi-level multi-objective model.

关键词： multi-level multi-objective problem fuzzy goal programming additive model

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Research of solving method against multi-objective problem based on adaptive genetic algorithm and grey relation degree

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25th Chinese Control Conference

作者： Li Junfeng Dai Wenzhan Zhejiang Sci Tech Univ Dept Automat Control Hangzhou 310018 Peoples R China Zhejiang Gongshang Univ Coll Elect & Informat Engn Hangzhou Zhejiang 310033 Peoples R China

ISBN: (纸本)9787810778022

In this paper, based on adaptive genetic algorithm and grey relation degree, a new method for solving multi-objective problem is put forward. First, the best solution of every objective in the middle of the multi-objective is solved and they are looked on as referenced vector. Second, the grey relation degree between every individual and the referenced vector is solved and the grey relation degree is acted as fitness of the individual. At last, the pareto optimal sets are solved by means of adaptive genetic algorithm. The variety of population is kept by means of adaptive probability of crossover and mutation. Simulation examples show the effectiveness of the proposed approach.

关键词： adaptive genetic algorithm multi-objective problem grey relation degree

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Robust optimization algorithms for multi-objective knapsack problem 10

Robust optimization algorithms for multi-objective knapsack ...

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10th International Symposium on Computing and Networking (CANDAR)

作者： Miyamoto, Takuya Fujiwara, Akihiro Kyushu Inst Technol Grad Sch Comp Sci & Syst Engn Iizuka Fukuoka 8208502 Japan

ISBN: (纸本)9781665475327

The solution obtained using a simple optimization technique is affected by changes in variables in the real world due to errors and other factors, and thus the predicted optimality of the solution may not be guaranteed. Therefore, a robust optimal solution, which is less affected by changes in variables, has attracted considerable attention in recent years. In the present paper, we propose an optimization algorithm for robust solutions of the multi-objective knapsack problem. Experimental results show that our proposed algorithm obtains a wider range of solutions than the existing algorithm.

关键词： robust optimization multi-objective problem Pareto-optimal solution knapsack problem

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A New Approach to Solve Fully Fuzzy multi-objective Transportation problem

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FUZZY INFORMATION AND ENGINEERING 2022年第4期14卷 456-467页

作者： Niksirat, Malihe Birjand Univ Technol Comp Sci Birjand Iran

The transportation problem is the problem of transferring goods from several sources or producers to multiple destinations or consumers in a cost-effective way, which is one of the most important problems in the supply chain management problems. The application of this problem in addition to the distribution of goods in the location and production planning problems is also important. Many real-life transportation problems encounter multiple, conflicting, and incommensurable objective functions. In addition, in real applications, due to lack of information, it is not possible to accurately estimate the parameters of this problem. Therefore, the main goal of this paper is to find the Pareto optimal solutions of fully fuzzy multi-objective transportation problem under the conditions of uncertainty. In accordingly, a new approach based on nearest interval approximation is proposed to solve the problem. Numerical examples are provided to illustrate the proposed approach and results.

关键词： Fully fuzzy transportation problem multi-objective problem nearest interval approximation Pareto optimal solutions

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Design and Analysis of multi-objective Optimization problem Using Evolutionary Algorithms.

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Procedia Computer Science 2020年 172卷 896-899页

作者： S. Jose C. Vijayalakshmi Mathematics Division School of Advanced Sciences Vellore Insistitute of Technology(Chennai) Department of Statistics & Applied Mathematics Central University of TamilNadu Thiruvarur

In this paper a major amount of work has been listed regarding multi-objective optimization problem using evolutionary algorithms. From time immemorial optimization have been required in every industry for improvement of the business or other scientific results. Evolutionary algorithms is a direct search algorithm and has been applied for various mathematical models to attain optimal results because of its adaptability with the system. The aim of the study is to find a multi-objective transportation problem by using evolutionary algorithm in bipartite graph. In multi-travelling salesman problem the objective to derive the optimal sequencing schedule of a salesman which is formed as a multiple objective problem.

关键词： multi-objective problem Evolutionary Algorithm MOTSP MOmSP

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Development of Genetic Algorithm of multi-objective Logistics problem Combined with Inventory Control in multi-periods

Development of Genetic Algorithm of Multi-Objective Logistic...

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The Twelfth Asia Pacific Industrial Engineering ＆ Management Systems Conference(APIEMS 2011)

作者： Masahiro Arakawa Faculty of Environmental and Urban Engineering Kansai University 3-3-35 Yamate-choSuitaOsaka 564-8680JAPAN

multi-objective logistics problem combined of inventory control and vehicle routing in multi-periods is treated in this *** this problem,the model is constructed of plural locations and demand is predetermined on each *** of a vehicle traveled to locations are sought to prevent from stock-out and to minimize total traveled *** problem includes two types of assignment controls;assignment of locations traveled by a vehicle at each period and the order of locations traveled by a vehicle started from a *** the condition that different demands are predetermined on different locations on different *** this study,a method to resolve multi-objective problem is developed aiming at minimizing total traveled distance and total inventory simultaneously. The developed method is based on combination of multi-objective Genetic Algorithm and algorithm to generate optimal route using typical template of routs *** numerical experiments are executed to evaluate the Pareto solutions obtained by the proposed algorithm by using a simple ***,the numerical results show that the computational time required to the approximate optimal solutions is sufficient for practical use.

关键词： Vehicle routing problem Inventory control multi-objective problem Genetic algorithm Space filter

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A modified teaching-learning based optimization for multi-objective optimal power flow problem

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ENERGY CONVERSION AND MANAGEMENT 2014年 77卷 597-607页

作者： Shabanpour-Haghighi, Amin Seifi, Ali Reza Niknam, Taher Shiraz Univ Sch Elect & Comp Engn Shiraz Iran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

In this paper, a modified teaching-learning based optimization algorithm is analyzed to solve the multi-objective optimal power flow problem considering the total fuel cost and total emission of the units. The modified phase of the optimization algorithm utilizes a self-adapting wavelet mutation strategy. Moreover, a fuzzy clustering technique is proposed to avoid extremely large repository size besides a smart population selection for the next iteration. These techniques make the algorithm searching a larger space to find the optimal solutions while speed of the convergence remains good. The IEEE 30-Bus and 57-Bus systems are used to illustrate performance of the proposed algorithm and results are compared with those in literatures. It is verified that the proposed approach has better performance over other techniques. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Optimal power flow multi-objective problem Modified teaching-learning based optimization Pareto-optimal set

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