检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Integrated approach for dynamic economic/emission dispatch problem: multi-objective moth flame optimizer with transmission loss prediction using cascaded forward neural network

引用

ELECTRICAL ENGINEERING 2024年第3期106卷 3495-3528页

作者： Nalini, Nagulsamy Kumar, Chandrasekaran Vijayarajan, Periyasamy Chidambararaj, Natarajan M Kumarasamy Coll Engn Dept Elect & Elect Engn Karur Tamil Nadu India Karpagam Coll Engn Dept Elect & Elect Engn Coimbatore Tamil Nadu India Univ Coll Engn Dept Elect & Elect Engn BIT Campus Tiruchirappalli Tamil Nadu India St Josephs Coll Engn Dept Elect & Elect Engn Chennai Tamil Nadu India

This paper presents improved single- and multi-objective algorithms based on the original moth flame optimizer (MFO) to tackle the dynamic economic emission dispatch (DEED) problem that affects power systems operations. The DEED problem is a multi-objective optimization problem that is strongly constrained, multi-dimensional, nonlinear, and non-convex. It comprises several optimization criteria, many of which are in direct opposition to one another;therefore, no one solution is optimal with regard to all of those criteria. Firstly, an enhanced flame generation strategy is incorporated into the MFO algorithm to improve performance. Then, the improved MFO is combined with the crowding distance mechanism and non-dominated sorting framework to enhance the convergence rate and the quality of the results. This helps improve the convergence pace. Firstly, the proposed multi-objective moth flame optimizer (MOMFO) algorithm is validated using 15 ZDT and UF benchmark multi-objective test functions. Then, the nonlinear DEED problem is also solved by determining the feasible optimal solution using the MOMFO algorithm. The implementation of the MOMFO on 10-unit systems and the IEEE 30-bus test system is being done to display the ability to solve a nonlinear, non-convex, and constrained DEED optimization problem. The DEED problem is solved using the MOMFO algorithm and other state-of-the-art algorithms, such as the non-dominated sorting genetic algorithm-II (NSGA-II), the multi-objective teaching-learning-based optimization (MOTLBO) algorithm, and multi-objective reptile search algorithm (MORSA). The selection of the control parameters of the MOMFO can be decided from the algorithm's findings on different IEEE bus systems. This study also introduces a new technique for incorporating loss predictions using artificial neural networks into the DEED model. During each phase of the dispatch time, the trained neural network can make only a single forecast of the transmission loss. The

关键词： Crowding distance Economic load dispatch Emission dispatch Moth flame optimizer multi-objective problem Non-dominated sorting

来源：评论

学校读者我要写书评

暂无评论

Weighted Linear Fractional Programming for Possibilistic multi-objective problem 4

引用

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

来源：评论

学校读者我要写书评

暂无评论

A two-phase multi-objective metaheuristic for a green UAV grid routing problem

引用

OPTIMIZATION LETTERS 2023年第9期17卷 2233-2256页

作者： Marques Jr, Elias L. Coelho, Vitor N. Coelho, Igor M. Ochi, Luiz Satoru Maculan, Nelson Mladenovic, Nenad Coelho, Bruno N. Univ Fed Fluminense Inst Comp Sci Niteroi Brazil OptBlocks Av Joao Pinheiro274 Room 201 BR-30130186 Belo Horizonte MG Brazil Univ Fed Rio De Janeiro Rio De Janeiro Brazil Khalifa Univ Abu Dhabi U Arab Emirates Univ Fed Sao Joao Del Rei REDEMAT Sao Joao Del Rei Brazil

This paper deals with Unmanned Aerial Vehicle (UAV) routing in dynamic grid scenarios with limited battery autonomy and multiple charging stations. Inspired by a multi-criteria view of real systems, we consider different objective functions, while respecting the navigation over forbidden areas and also a real-time flight autonomy. A multi-objective variant of Variable Neighborhood Search is considered for finding sets of non-dominated solutions. Twelve neighborhood structures were developed in order to explore the solution space, including learning techniques. The latter stores known routes in order to speed up the search. A case of study was developed where UAVs have to serve clients spread throughout a grid, representing a map. Each UAV starts in a given grid point with a given battery charge, where the grid is composed by four different kinds of points: a regular one and three special (prohibited, recharge and client). Any update can happen on the routes on real-time, so the metaheuristic should handle real-time information and update the plan and graphics user interface accordingly. Any sequence of valid adjacent points forms a route, but since this yields a huge number of combinations, a preprocessing technique is proposed to pre-compute distances in a given dynamic scenario. Computational results demonstrate the performance of different variants of the proposed algorithms.

关键词： Unmanned aerial vehicle Microgrids multi-objective problem Variable neighborhood search Biased random key genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Dynamic Programming Approach to Solve Real-World Application of multi-objective Unbounded Knapsack problem 1st

Dynamic Programming Approach to Solve Real-World Application...

引用

1st International Conference on Intelligent Systems and Applications

作者： Khandekar, Aayush P. Nargundkar, Aniket Vishwakarma Inst Technol Pune 411037 Maharashtra India Symbiosis Int Symbiosis Inst Technol Pune 412115 Maharashtra India

ISBN: (纸本)9789811965814;9789811965807

Knapsack problem is classified as a combinatorial optimization problem with the consideration of the optimal object being a part of the predefined set of finite objects allowed to be placed in the knapsack. The unbounded knapsack problem allows the repetition of objects, while demanding that the sum of the values of the objects in the optimal solution does not exceed the weight of the knapsack. In this paper, food order optimization problem is modeled as a multi-objective unbounded knapsack problem, as the problem has multiple objectives which need to be achieved simultaneously. Optimizing the number of given non-vegetarian dishes and maximizing the number of servings is considered as objective functions. These objectives are to be satisfied restricting to the budget constraint. Dynamic programming approach is applied to generate an optimal solution while satisfying the set constraints. The proposed approach successfully returns an optimal solution for all test cases.

关键词： Unbounded knapsack Dynamic programming Combinatorial optimization Food order optimization multi-objective problem

来源：评论

学校读者我要写书评

暂无评论

Solving multi-objective Modified Distributed Parallel Machine and Assembly Scheduling problem (MDPMASP) with eligibility constraints using metaheuristics

引用

PRODUCTION AND MANUFACTURING RESEARCH-AN OPEN ACCESS JOURNAL 2022年第1期10卷 198-225页

作者： Amallynda, Ikhlasul Santosa, Budi Univ Muhammadiyah Malang Dept Ind Engn Malang Indonesia Inst Teknol Sepuluh Nopember Dept Ind Engn Surabaya Indonesia

We present a new generalization and metaheuristics for the distributed parallel machine and assembly scheduling problem (DPMASP), namely MDPMASP with eligibility constraints. There is a set of unrelated factories or production lines. Each has a series of non-identical parallel machines with varying processing speeds. Then, it was disposed of as a single assembly machine in a series. Each product is assembled from a set of components (jobs). Each item necessitates multiple unidentical jobs. The objectives are to minimize mean flow time and the number of tardy jobs. We suggest four basic heuristics and three meta heuristics to tackle the problem. The Taguchi approach is used to discuss and calibrate various metaheuristic parameters. Algorithms are compared using the four performance measures. The computational results show that the proposed algorithms can solve moderate-sized problems efficiently and near-optimal solutions in most cases. Moreover, based on the four performance measures, MGA is the best method, followed by MSA, MPSO, SH2, SH4, SH1 and SH3.

关键词： MDPMASP eligibility constraints multi-objective problem mean flow time the number of tardy heuristic metaheuristic

来源：评论

学校读者我要写书评

暂无评论

A depth-based heuristic to solve the multi-objective influence spread problem using particle swarm optimization

引用

OPSEARCH 2023年第3期60卷 1267-1285页

作者： Riquelme, Fabian Munoz, Francisco Olivares, Rodrigo Univ Valparaiso Valparaiso Chile

The influence spread in a social network is an iterative process that can take several steps. It begins with an activation seed and finishes when the current activation cannot influence more actors. The multi-objective influence spread problem corresponds to finding the smallest number of actors capable of maximizing the influence spread within the network. This problem has been solved by metaheuristic optimization algorithms using swarm intelligence methods. This article proposes a heuristic to improve the existing solution: when two sets of actors can influence the same number of actors, the one whose spread requires the least number of steps is chosen. The proposed solution is tested on two different real networks. The results show that the heuristic allowed better results for both networks and decreased the average number of steps in the influence spread processes (in 15.5 and 0.07 average steps, respectively), thus improving execution times. Moreover, the heuristic allowed decreasing the number of steps in 83% (against 17% of increasing) and 13% (against 7% of increasing) of the particles, respectively.

关键词： Social network Influence diffusion multi-objective problem Swarm optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：