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检索条件"主题词=multi-objective evolutionary algorithm"

共 545 条记录，以下是421-430 订阅

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Stochastic Dual-objective Disassembly Sequence Planning with Consideration of Learning Effect 16

Stochastic Dual-objective Disassembly Sequence Planning with...

引用

16th IEEE International Conference on Networking, Sensing and Control (ICNSC)

作者： Guo, XiWang Zhou, MengChu Fu, YaPing Qi, Liang You, Dan Liaoning Shihua Univ Coll Comp & Commun Engn Fushun 113001 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Qingdao Univ Inst Complex Sci Qingdao 266071 Shandong Peoples R China Shandong Univ Sci & Technol Dept Intelligent Sci & Technol Qingdao 266590 Shandong Peoples R China Zhejiang Gongshang Univ Dept Informat & Elect Engn Hangzhou 310008 Zhejiang Peoples R China

ISBN: (纸本)9781728100845

In an actual remanufacturing process, a human operator is able to continuously learn the disassembly knowledge of an end-of-life product when he/she disassembles it, which makes him/her disassemble it more proficiently. In order to describe this feature, this work proposes a stochastic dual objective disassembly sequencing planning problem considering human learning effects. In this problem, actual disassembly and setup time of operations are a function of their normal time and starting time. A new mathematical model is established to maximize total disassembly profit and minimize disassembly time. In order to solve this problem efficiently, a multi-population multi-objective evolutionary algorithm is developed. In this algorithm, some special strategies, e.g., solution representation, crossover operator and mutation operator, are newly designed based on this problem's characteristics. Its effectiveness is well illustrated through several numerical cases and by comparing it with two prior approaches, i.e., nondominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition. Experimental results demonstrate that the proposed algorithm performs well in solving this problem.

关键词： remanufacture disassembly sequencing planning problem learning effect multi-population multi-objective evolutionary algorithm stochastic dual-objective

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Decentralizing and Optimizing Nation-Wide Employee Allocation While Simultaneously Maximizing Employee Satisfaction 5th

Decentralizing and Optimizing Nation-Wide Employee Allocatio...

引用

4th International Conference on Optimization and Learning (OLA)

作者： Tabassum, B. Aniqua Ahmed, Ashna Nawar Alam, Subhe Tasnia Tabassum, Noushin Mahbub, Md Shahriar Seattle Univ Seattle WA 98122 USA Ahsanullah Univ Sci & Technol Dhaka Bangladesh

ISBN: (纸本)9783031220388;9783031220395

Despite the studies on human resource allocation, a problem of dissatisfied employees arises in developing and under-developed countries while decentralizing human resources nationwide since rural areas have fewer facilities than urban areas. Randomly allocating employees contributes to employees' dissatisfaction if they are displeased with where they are assigned, leading to an unstable work environment. However, allocating employees solely based on their satisfaction may lead to a centralized solution around the urban cities. Therefore, employee satisfaction and dispersion are the two most essential but opposing factors for employee decentralization in developing countries. In this study, we have addressed the problem of employee decentralization by proposing a multi-objective Optimization approach that maximizes the two conflicting objectives: employee satisfaction (ES) and employee dispersion (ED). A neural network is applied that predicts the ES of an employee allocated in an area/city. Moreover, we have formulated a dispersion function that provides a score based on how well dispersed a specific allocation is. Using a multi-objective evolutionary algorithm, we have developed an allocation framework that maximizes these conflicting objectives and finds optimal allocations.

关键词： Employee dispersion Employee satisfaction Human resource allocation in developing countries multi-objective evolutionary algorithm

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A Cooperative multi-objective Island Parallel Model for Wind Farm Planning

A Cooperative Multi-Objective Island Parallel Model for Wind...

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2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering

作者： Marquez, A. L. Gil, C. Banos, R. Montoya, M. G. Montoya, F. G. Manzano-Agugliaro, F. Univ Almeria Dept Comp Architecture & Elect La Canada De San Urbano Almeria Spain Univ Almeria Dept Rural Engn La Canada De San Urbano Almeria Spain

ISBN: (纸本)9781905088423

Wind energy is widely used to produce electricity all over the world, but it is not a reliable and constant source. To solve this issue, the use of statistics over an extended period of time is advisable, in order to quantify an average amount of energy production for the wind farm. This means that it is possible to identify three distinct objectives in conflict for the problem at hands: maximizing the energy production, reducing the amount of unused installed power, and minimizing the standard deviation of the energy produced through the day. To that end, the data saved through year 2008 in Cancun, Mexico, are used in order to generate the wind regime through an average day. The power curve of 26 different aerogenerator models is also used. To solve this three-objective problem, it is advisable to use a multi-objective evolutionary algorithm (MOEA), but most of them work better for 2-objective problems. This is solved by using a Hybrid Island Parallel MOEA, that combines 4 different MOEAs (NSGA-II, SPEA2, PESA, msPESA) and also different sets of objectives for different islands. This means that an adaption operation is needed in order to migrate solutions between islands, and also in order to grow from two objectives on each island to the final three-objective solutions returned by the procedure.

关键词： hybrid algorithm threaded computation island parallel model multi-objective evolutionary algorithm wind farm planning aerogenerators

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A New Technique for Assessing the Diversity of Close-Pareto-Optimal Front

A New Technique for Assessing the Diversity of Close-Pareto-...

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IEEE Congress on evolutionary Computation

作者： Zeng, Sanyou Chen, Guang Wang, Rui Li, Hui Shi, Hui Ding, Lixin Kang, Lishan China Univ Geosci Dept Comp Sci Technol Res Ctr Space Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China

ISBN: (纸本)9781424418220

The quality of an approximation set usually includes two aspects-- approaching distance and spreading diversity. This paper introduces a new technique for assessing the diversity of an approximation to an exact Pareto-optimal front. This diversity is assessed by using an "exposure degree" of the exact Pareto-optimal front against the approximation set. This new technique has three advantages: Firstly, The "exposure degree" combines the uniformity and the width of the spread into a direct physical sense. Secondly, it makes the approaching distance independent from the spreading diversity at the most. Thirdly, the new technique works well for problems with any number of objectives, while the widely used diversity metric proposed by Deb would work poor in problems with 3 objectives or over. Experimental computational results show that the new technique assesses the diversity well.

关键词： Quality assessment approximation set multi-objective optimization Pareto optimal front multi-objective evolutionary algorithm

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AN INVESTIGATION OF LOCAL SEARCHES IN MEMETIC algorithmS FOR multi-objective SEQUENCING PROBLEMS ON MIXED-MODEL ASSEMBLY LINES

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38th International Conference on Computers and Industrial Engineering

作者： Chutima, Parames Pinkoompee, Penpak Chulalongkorn Univ Dept Ind Engn Fac Engn Bangkok 10330 Thailand

ISBN: (纸本)9787121074370

Mixed model assembly lines are a type of production line where a variety of product models with similar product characteristics are assembled in a just-in-time (JIT) production system. In this paper, we consider two objectives;which are setup times and production rates variation. to be minimized simultaneously. The two objectives are inversely related to each other and, therefore, simultaneous optimization of both is challenging. This type of problem is also an NP-hard problem. We show in this study how the performance of such evolutionary multi-objective optimization algorithm as memetic algorithm can be improved by hybridization with local search. There are seven local search procedures applied in memetic algorithms to solve multi-objective sequencing problems on mixed-model assembly lines in UT production systems. Experimental results show that the performance of local search in memetic algorithm is significantly better than highly meta-heuristics as Strength Pareto evolutionary algorithm 2 (SPEA2) and Non-dominated Sorting Genetic algorithm II (NSGA-II) in terms of ability to find Pareto-optimal solution.

关键词： Memetic algorithms Local Search Heuristics multi-objective evolutionary algorithm Mixed model assembly line JIT production system

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Diversity Maintenance Strategy Based on Global Crowding

引用

6th International Symposium on Neural Networks

作者： Chen, Qiong Xiong, Shengwu Liu, Hongbing Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China

ISBN: (纸本)9783642015120

In the design of multi-objective evolutionary algorithm, the diversity maintenance is essential to access the convergence of multi-objective optimization solutions. This paper presents a new diversity maintenance strategy based on global crowding, which is addressed for pruning non-dominated solutions as well as preserving a wide-spread distributed solution set and maintaining population diversity. Later on, inspired by the conception of entropy in information theory, the entropy metrics is defined and applied to assess the proposed strategy. Two-dimensional and multi-dimensional numerical experiment results demonstrate that the proposed strategy shows better performance in the entropy reduction and losses Of uniform distribution than traditional diversity maintenance strategies.

关键词： Global Crowding Diversity Maintenance Strategies Entropy Metrics multi-objective evolutionary algorithm

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A Novel Diversity Preservation Strategy based on Ranking Integration for Solving Some Specific multi-objective Problems

A Novel Diversity Preservation Strategy based on Ranking Int...

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9th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES 2010)

作者： Yu Long Wang Pan Zhu Haoshen Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

ISBN: (纸本)9780769541105

In recent decades, multi-objective evolutionary algorithms (MOEAs) are developed as powerful tools to solve multi-objective optimization problems. While the diversity of Pareto front (PF) plays an important role in the performance evaluation of MOEAs, various diversity preservation strategies (DPS) have been developed. In this paper, a novel approach that inspired from the crowding distance technique is proposed to maintain the diversity of solutions in multi-objective problems (MOPs) with quite different spans of value range. In order to improve its performance, this approach is applied in a well-know MOEA NSGA II by replacing its original DPS. According to 3 test MOPs, the modified NSGA II shows a better diversity and distribution in the PF compared with the original version. Furthermore, the influence of the spans of value range on the performance of original DPS in NSGA II is discussed and the robustness of the new DPS is illustrated.

关键词： multi-objective evolutionary algorithm crowding distance diversity preservation NSGA II

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Design of the High-sensitivity RFID Sensor Tag with MOEA/D-DE 21

Design of the High-sensitivity RFID Sensor Tag with MOEA/D-D...

引用

21st International Symposium on Antennas and Propagation (ISAP)

作者： Song, Xiaotian Wang, Gang He, Yuxing Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Chinese Acad Sci Key Lab Electromagnet Space Informat Hefei 230027 Peoples R China

ISBN: (纸本)9784885523137

The MOEA/D-DE (multi-objective evolutionary algorithm based on decomposition combined with differential evolution) is firstly applied to design a high-sensitivity RFID sensor tag with the consideration of its communication performance. For demonstration, an RFID temperature sensor tag is designed and tested. Both simulated and measured results show the designed sensor tag achieves a three times higher sensing sensitivity and a better communication performance than the one in the literature.

关键词： RFID sensor tag sensitivity temperature sensing multi-objective evolutionary algorithm

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multi-objective Maximum Diversity Problem 43

Multi-Objective Maximum Diversity Problem

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43rd Latin American Computer Conference (CLEI)

作者： Vera, Katherine Lopez-Pires, Fabio Baran, Benjamin Sandoya, Fernando Natl Univ Asuncion San Lorenzo Paraguay Itaipu Technol Pk Hernandarias Paraguay ESPOL Polytech Univ Dept Math Guayaquil Ecuador

ISBN: (纸本)9781538630570

The Maximum Diversity (MD) problem is the process of selecting a subset of elements where the diversity among selected elements is maximized. Several diversity measures were already studied in the literature, optimizing the problem considered in a pure mono-objective approach. This work presents for the first time multi-objective approaches for the MD problem, considering the simultaneous optimization of the following five diversity measures: (i) Max-Sum, (ii) Max-Min, (iii) Max-MinSum, (iv) Min-Diff and (v) Min-P-center. Two different optimization models are proposed: (i) multi-objective Maximum Diversity (MMD) model, where the number of elements to be selected is defined a-priori, and (ii) multi-objective Maximum Average Diversity (MMAD) model, where the number of elements to be selected is also a decision variable. To solve the formulated problems, a multi-objective evolutionary algorithm (MOEA) is presented. Experimental results demonstrate that the proposed MOEA found good quality solutions, i.e. between 89.20% and 99.92% of the optimal Pareto front when considering the hyper-volume for comparison purposes.

关键词： multi-objective Maximum Diversity multi-objective Maximum Average Diversity multi-objective evolutionary algorithm

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Computation of the improvement directions of the Pareto front and its application to MOEAs

Computation of the improvement directions of the Pareto fron...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Botello-Aceves, Salvador Hernandez-Aguirre, Arturo Valdez, S. Ivvan Ctr Res Math Guanajuato Mexico Ctr Invest Ciencias Informac Geoespacial Queretaro Mexico

ISBN: (纸本)9781450371285

This paper introduces the mathematical development and algorithm of the Improvement-Directions Mapping (IDM) method, which computes improvement directions to "push" the current solutions toward the true Pareto front. The main idea is to compute normal vectors to the front, as improvement directions in the objective space, to be then transformed into search directions in the variable space through a transformation tensor. The main contributions of the IDM as a local search operator versus previous approaches are the following: 1) It does not require of a priori information about improvement directions or location of the true Pareto front, 2) It uses a local quadratic approximation of the Pareto front to compute the transformation tensor, thus, reducing numerical problems and avoiding abrupt changes in the search direction which could lead to erratic searches. These features allow the IDM to be implemented as a local search operator within any multi-objective evolutionary algorithm (MOEA). The potential of the IDM is shown by hybridizing two well-known multi-objective algorithms: a) MOEA/D + IDM;b) NSGA-II + IDM. In the first approach, IDM "pushes" the offspring population in each iteration. A similar experiment is performed with the second approach. Furthermore, one more experiment evaluates the IDM as a refinement step that is applied to the last Pareto front delivered by NSGA-II.

关键词： multi-objective optimization multi-objective evolutionary algorithm Memetic algorithm Local search

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