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

共 545 条记录，以下是81-90 订阅

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A Novel multi-objective evolutionary algorithm Based on External Dominated Clustering

A Novel Multi-Objective Evolutionary Algorithm Based on Exte...

引用

International Conference on Computational-Intelligence and Security

作者： Fan, Lei Wang, Yuping Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

ISBN: (纸本)9781424441969

evolutionary algorithms (EAs) have wide applications in practice and many advantages over traditional methods in solving nonlinear and complex optimal problems. In this paper, we propose a novel clustering technique, in which the infeasible solutions are employed to divide the feasible solutions into several clusters. There is no more one infeasible individual in each cluster A novel evolutionary algorithm based on this technique called ED-MOEA is proposed for dealing with constrained multi-objective problems. Simulation results on five test problems indicate the proposed algorithm is effective.

关键词： test problem multi-objective evolutionary algorithm evolutionary algorithm Novels Cluster Analysis collective agreement Feasible Solution TRADITIONAL METHODS

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Selection Model Based on multi-objective evolutionary algorithm for Coordinated multi Point Schemes 22

Selection Model Based on Multi-Objective Evolutionary Algori...

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22nd Asia-Pacific Conference on Communications (APCC)

作者： Lozano-Garzon, Carlos Donoso, Yezid Vila, Pere Univ Los Andes Syst & Comp Engn Dept Bogota Colombia Univ Girona Inst Informat & Aplicac Girona Spain

ISBN: (纸本)9781509006762

The capacity, coverage and interference problems in the infrastructure deployed by mobile operators are increasing due to exponential growth of subscribers and traffic requirements of the new services. In order to solve these problems, 3GPP proposed the Coordinated multi Point (CoMP) which is a set of cooperative communication techniques between base stations. In this paper we address the decision problem as a multi-objective optimization problem with the aim of improving the user's quality of experience and the load of all base stations involved. We propose an optimization model as well as an evolutionary algorithm based on the SPEA2 algorithm, to select the best cooperation technique over the download channel. Through simulation over experimental scenarios, we demonstrate that our proposal provides optimal solutions, while being efficient and scalable.

关键词： Coordinated multi Point Dynamic Point Selection Joint Transmission Load Balancing multi-objective evolutionary algorithm Quality of Experience

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A Bi-Population Based multi-objective evolutionary algorithm Using Hybrid Identification Method for Finding Knee Points

A Bi-Population Based Multi-Objective Evolutionary Algorithm...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Tang, Junfeng Wang, Handing Xidian Univ Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9781728190488

In the preference-based multi-objective optimization, decision makers may be interested in only a part of the representative solutions and hardly specify their preferences. In this case, knee points are considered as the naturally preferred trade-off solutions. Most research utilizes the trade-off information or certain properties to find knee points. However, little attention has been paid to combine them to further enhance the knee identification. This paper proposes a multi-objective evolutionary algorithm using a hybrid identification method and a bi-population structure to find knee points. The hybrid identification method is based on the localized alpha-dominance and the distance to the hyperplane. Firstly, two populations are partitioned by a set of predefined reference vectors and apply the localized alpha-dominance to guide the search towards potential knee regions. Then knee solutions are detected based on the distance to hyperplane constructed by the extreme points. Finally in the environmental selection, a niche-preserving operation is applied to take the knee solutions of all sub-populations into account. The first population is the main part of the search, and affects the offspring generation and environmental selection of the second population. The experiments demonstrate that the proposed method is effective and competitive in identifying knee solutions.

关键词： multi-objective evolutionary algorithm knee points bi-population structure hybrid identification method

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Ultra-Wideband Planar Antenna Optimized by a multi-objective evolutionary algorithm

Ultra-Wideband Planar Antenna Optimized by a Multi-Objective...

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Asia Pacific Microwave Conference

作者： Yang, Xue-Song Man, Kim Fung Yeung, Sai Ho Li, Jia-Lin Wang, Bing-Zhong City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China Univ Elect Sci & Technol China Inst Appl Phys Chengdu 610054 Peoples R China

ISBN: (纸本)9781424407484

An ultra-wideband (UWB) planar antenna optimized by a newly developed multi-objective evolutionary algorithm- Jumping Genes Genetic algorithm (JGGA) is proposed. The optimization procedure aims at finding an antenna not only with low voltage standing wave ratio (VSWR) and small size, but also with stable radiation pattern over the wide band. There are five objective functions and they are based on VSWR, antenna size, radiation pattern and gain. Both the simulated and measured results are given and they agree well with each other.

关键词： multi-objective evolutionary algorithm Jumping Genes Genetic algorithm UWB antenna

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A multi-objective evolutionary algorithm with New Reproduction and Decomposition Mechanisms for the multi-Point Dynamic Aggregation Problem

A Multi-objective Evolutionary Algorithm with New Reproducti...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Gao, Guanqiang Xin, Bin Mei, Yi Lu, Shengyu Ding, Shuxin Beijing Inst Technol Beijing Peoples R China Victoria Univ Wellington Wellington New Zealand China Acad Railway Sci Corp Ltd Signal & Commun Res Inst Beijing Peoples R China

ISBN: (纸本)9781450392372

An emerging optimisation problem from real-world applications, named the multi-point dynamic aggregation (MPDA) problem, has become an active research of the multi-robot system. This paper focuses on a multi-objective MPDA (MO-MPDA) problem which is to design execution plans of robots for minimising the cost of used robots and maximising the efficiency of task execution. The MOMPDA problem has the issues of conflicting objectives, redundant representation, and variable-length encoding, posing extra challenges to address the MO-MPDA problem effectively. Combining the..-constraint method and decomposition mechanisms, a novel multi-objective evolutionary algorithm is proposed. The proposed algorithm selects the efficiency objective as the main objective and converts the cost objective as constraints. Thus, the multi-objective problem is decomposed into a series of scalar constrained optimisation subproblems by assigning each subproblem with an upper bound constraint. All the subproblems are optimised and evolved simultaneously with the transferring knowledge from other subproblems to solve the MO-MPDA problem parallelly and efficiently. Besides, considering the characteristics of parent individuals, this paper designs a hybrid reproduction mechanism to transmit effective information to offspring individuals for tackling the encoding redundancy and varying-length. Experimental results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms in terms of most-used metrics.

关键词： multi-objective evolutionary algorithm hybrid reprodcution mechanism multi-robot system multi-point dynamic aggregation

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Solving the exam timetabling problem via a multi-objective evolutionary algorithm - A more general approach

Solving the exam timetabling problem via a multi-objective e...

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IEEE Symposium on Computational Intelligence in Scheduling

作者： Cheong, C. Y. Tan, K. C. Veeravalli, B. Natl Univ Singapore Dept Elect & Comp Engn 4 Engn Dr 3 Singapore 117576 Singapore

ISBN: (纸本)9781424407040

This paper studies a multi-objective instance of the university exam timetabling problem. On top of satisfying universal hard constraints such as seating capacity and no overlapping exams, the solution to this problem requires the minimization of the timetable length as well as the number of occurrences of students having to take exams in consecutive periods within the same day. While most existing approaches to the problem, as well as the more popular single-objective instance, require prior knowledge of the desired timetable length, the multi-objective evolutionary algorithm proposed in this paper is able to generate feasible solutions even without the information. The effectiveness of the proposed algorithm is benchmarked against a few recent and established optimization techniques and is found to perform well in comparison.

关键词： education evolutionary computation minimisation scheduling minimization multiobjective evolutionary algorithm seating capacity universal hard constraints university exam timetabling problem Seating capacity evolutionary algorithm multi-objective evolutionary algorithm

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A multi-objective evolutionary algorithm for Interaction Systems Based on Laser Pointers 18

A Multi-objective Evolutionary Algorithm for Interaction Sys...

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18th European Conference on Applications of evolutionary Computation (EvoApplications) as part of EvoSTAR Conference

作者： Chavez, Francisco Clemente, Eddie Hernandez, Daniel E. De Vega, Francisco Fernandez Olague, Gustavo Univ Extremadura Dept Comp Sci C Santa Teresa De Jornet 38 Merida 06800 Spain Ctr Invest Cient & Educ Super Ensenada Carretera Ensenada Tijuana 3918 Ensenada 22860 BC Mexico

ISBN: (纸本)9783319165486;9783319165493

In this paper we face the problem of accurate location of a laser spot that is used as interaction system in real environments. The work presented is compared with previous approaches where different algorithms work with a single objective, using images that has been previously simplified to reduce computing time. Instead, the new approach presented in this paper is capable of processing whole images. The results show that the inclusion of multi-objective methods allows us not only to detect the presence of the laser spot, the single objective in previous works, but also to obtain accurate information of the laser spot in the image, and thus provide the location of the device on which the user wants to act.

关键词： multi-objective evolutionary algorithm Laser pointer Interaction systems

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Risk-adjusted portfolio optimisation using a parallel multi-objective evolutionary algorithm

Risk-adjusted portfolio optimisation using a parallel multi-...

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IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)

作者： Maguire, Phil O'Sullivan, Donal Moser, Philippe Dunne, Gavin Natl Univ Ireland Dept Comp Sci Maynooth Kildare Ireland

ISBN: (纸本)9781467318037

In this article we describe the use of a multi-objective evolutionary algorithm for portfolio optimisation based on historical data for the S&P 500. Portfolio optimisation seeks to identify manageable investments that provide a high expected return with relatively low risk. We developed a set of metrics for qualifying the risk/return characteristics of a portfolio's historical performance and combined this with an island model genetic algorithm to identify optimised portfolios. The algorithm was successful in selecting investment strategies with high returns and relatively low volatility. However, although these solutions performed well on historical data, they were not predictive of future returns, with optimised portfolios failing to perform above chance. The implications of these findings are discussed.

关键词： multi-objective evolutionary algorithm historical data Investments

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evolutionary Computation of multi-Band Antenna Using multi-objective evolutionary algorithm Based on Decomposition

Evolutionary Computation of Multi-Band Antenna Using Multi-O...

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2nd International Conference on Information Computing and Applications

作者： Ding, Dawei Wang, Hongjin Wang, Gang UJS Dept Telecommun Engn Zhenjiang Peoples R China USTC Dept Elect Engn & Informat Hefei Peoples R China

ISBN: (纸本)9783642252549

Design of multi-band antenna involves multiple characteristics such as return loss in multiple operation bands. To apply MOEA/D (multi-objective evolutionary algorithm based on decomposition) to antenna structure optimization, it was introduced into this field for the first time. MOEA/D framework was demonstrated at first. Then it was in conjunction with electromagnetic solver, HFSS (high frequency structure simulator) to optimize and design tri-band bow-tie antenna to serve as an example. The evolutionary results showed that MOEA/D worked efficiently and generated multiple candidate structures at one single iteration, and that MOEA/D had lower computational overhead than NSGA-II (non-dominated sorting genetic algorithm II) for this problem. Therefore, MOEA/D shows great potential for antenna structure optimization and design.

关键词： Bow-tie antenna multi-objective evolutionary algorithm multi-objective optimization

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multi-time constrained dual-resource flexible job shop scheduling based on multi-objective evolutionary algorithm 4

Multi-time constrained dual-resource flexible job shop sched...

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4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)

作者： Yang, Luda Lv, Zhuoxuan Du, Baigang Guo, Jun Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

ISBN: (纸本)9798350375084;9798350375077

This paper presents a new generalization of the uniform dual-resource constrained flexible job shop scheduling problem (DRCFJSP) for automated flexible manufacturing system, considering two multi-time constraints of setup time and transportation time. Design a mixed integer nonlinear programming model with multiple optimization objectives of minimizing maximum completion time, minimizing transportation time and minimizing setup time. And a hybrid multi-objective evolutionary algorithm (HMOEA) is proposed to solve the problem. Through simulation experiments on different data sets and algorithm comparison, the effectiveness and feasibility of the proposed algorithm are verified. Finally, we designed thirteen processing cases under different conditions and compared them with four different comparison algorithms, and concluded that the HMOEA algorithm is superior in solving the DRCFJSP problem, which is of great significance to the use of algorithms for optimal design in actual production.

关键词： Flexible manufacturing systems Dual-resource constrained flexible job shop scheduling Setup and transportation time multi-objective evolutionary algorithm

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