检索结果-内蒙古大学图书馆

2010 IEEE World Congress on Computational Intelligence

作者： Chen, Chih-Ming Chen, Ying-ping Shen, Tzu-Ching Zao, John K. Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781424481262

Luby Transform code (LT code) is the first practical digital fountain code and has been widely used as basic components in many communication applications. The coding behavior of LT code is mainly decided by a probability distribution of codeword degrees. In order to customize a degree distribution for different purposes, multi-objective evolutionary algorithm is introduced to optimize degree distributions in this paper. Two critical performance indicators of LT code are considered in our experiments. Some applications hope to minimize the overhead of extra packets and some require to limit the computational cost of the coding system. To handle this problem, MOEA/D is applied to optimize two objectives simultaneously. We expect to obtain the Pareto front (PF) formed by partial optimal solutions and provide those available degree distributions to different LT code applications. Not only promising results are represented in this paper but also the behavior of LT code is thoroughly explored by optimizing the degree distribution according to multi-objectives.

关键词： LT code Luby transform code MOEA/D Pareto front Pareto optimisation codes codeword degree probability distribution decomposition technique digital fountain code evolutionary computation multiobjective evolutionary algorithm probability transform coding

来源：评论

学校读者我要写书评

暂无评论

Optimal Design of a Pin-Fin Heat Sink Using a Surrogate-Assisted multiobjective evolutionary algorithm

Optimal Design of a Pin-Fin Heat Sink Using a Surrogate-Assi...

引用

International Conference on Advanced Design and Manufacturing Engineering (ADME 2011)

作者： Kanyakam, Siwadol Bureerat, Sujin Khon Kaen Univ Fac Engn Dept Mech Engn Khon Kaen 40002 Thailand

ISBN: (纸本)9783037852132

In this work, performance enhancement of a multiobjective evolutionary algorithm (MOEA) by integrating a surrogate model to the design process is presented. The MOEA used in this work is multiobjective population-based incremental learning (PBIL). The bi-objective design problem of a pin-fin heat sink (PFHS) is posed to minimize junction temperature and fan pumping power while meeting design constraints. A Kriging (KRG) model is used for improving the performance of PBIL. The training points for constructing a surrogate KRG model are sampled by means of a Latin hypercube sampling (LHS) technique. It is shown that hybridization of PBIL and KRG can enhance the search performance of PBIL.

关键词： Pin-fin heat sink multiobjective evolutionary algorithm Population-based incremental learning Surrogate model Kriging model Computational fluid dynamics

来源：评论

学校读者我要写书评

暂无评论

A multiobjective evolutionary algorithm based on Decomposition and Probability Model

A Multiobjective Evolutionary Algorithm based on Decompositi...

引用

IEEE Congress on evolutionary Computation (CEC)

作者： Zhou, Aimin Zhang, Qingfu Zhang, Guixu East China Normal Univ Dept Comp Sci Shanghai 200241 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

ISBN: (纸本)9781467315098

Many real world applications require optimizing multiple objectives simultaneously. multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a new framework for dealing with such kind of multiobjective optimization problems (MOPs). MOEA/D focuses on how to maintain a set of scalarized sub-problems to approximate the optimum of a MOP. This paper addresses the offspring reproduction operator in MOEA/D. It is arguable that, to design efficient offspring generators, the properties of both the algorithm to use and the problem to tackle should be considered. To illustrate this idea, a generator based on multivariate Gaussian models is proposed under the MOEA/D framework in this paper. In the new generator, both the local and global population distribution information is extracted by a set of Gaussian distribution models;new trial solutions are sampled from the probability models. The proposed approach is applied to a set of benchmark problems with complicated Pareto sets. The comparison study shows that the offspring generator is promising for dealing with continuous MOPs.

关键词： multiobjective evolutionary algorithm decomposition probabilistic model

来源：评论

学校读者我要写书评

暂无评论

A multiobjective evolutionary algorithm for Distribution System Reconfiguration Problems 12

A Multiobjective Evolutionary Algorithm for Distribution Sys...

引用

Joint 12th International Conference on Soft Computing and Intelligent Systems / 23rd International Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： Sekizaki, Shinya Nishizaki, Ichiro Hayasida, Tomohiro Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima Japan

ISBN: (纸本)9781665499248

Real-world multiobjective optimization problems (MOPs) are generally constrained and multiobjective evolutionary algorithms (MOEAs) have been proposed for solving constrained MOPs (CMOPs). When solving CMOPs observed in the real world, which are computationally demanding and have severe constraints, finding a well-converged population to constrained Pareto front is not easy. In such cases, infeasible nondominated solutions with slight constraint violations can provide useful information for decision makers. In this paper, we propose a MOEA that can provide decision makers with trade-off information between constraint violations and objective values of those untractable CMOPs. The proposed MOEA partitions the objective space of a CMOP, with objectives to minimize constraint violations, into multiple subspaces and maintains the diversity of the population by preserving feasible and infeasible solutions in the subspaces. Solutions with slight constraint violations and small objective values are preserved in the population using different selection pressures for the feasible and infeasible populations. Computational experiments for solving a distribution system reconfiguration problem, which is a computationally demanding and severely constrained CMOP in the real world, are performed to demonstrate the effectiveness of the proposed MOEA.

关键词： multiobjective evolutionary algorithm Constrained multiobjective optimization Distribution system reconfiguration

来源：评论

学校读者我要写书评

暂无评论

Hybrid multiobjective evolutionary algorithm with fast sampling strategy-based global search and route sequence difference-based local search for VRPTW

引用

EXPERT SYSTEMS WITH APPLICATIONS 2020年 145卷 113151-113151页

作者： Zhang, Wenqiang Yang, Diji Zhang, Guohui Gen, Mitsuo Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Peoples R China Zhengzhou Univ Aeronaut Sch Management Engn Zhengzhou Peoples R China Tokyo Univ Sci Fuzzy Log Syst Inst Tokyo Japan

The vehicle routing problem with time windows (VRPTW) is an important and widely studied combinatorial optimization problems. This paper aims at VRPTW with the objectives of reducing the number of vehicles and minimizing the time-wasting during the delivery process caused by early arrival. To solve this NP-hard problem, a hybrid multiobjective evolutionary algorithm with fast sampling strategy-based global search and route sequence difference-based local search (HMOEA-GL) is proposed. Firstly, fast sampling strategy-based global search (FSS-GS) of HMOEA-GL extensively explores the entire solution space to quickly guide the search direction towards the center and edge areas of Pareto frontier. Secondly, route sequence difference-based local search (RSD-LS) is executed on the individuals with poor performance in the population obtained by FSS-GS to enhance the search ability of HMOEA-GL. In addition, the suitable coding method and proper genetic operators are designed, especially, a simple insertion search is used to reduce the number of vehicles in VRPTW. Comparing with NSGA-II, SPEA2, and MOEA/D, experimental results on 12 Solomon benchmark test problems indicate that the proposed HMOEA-GL is effective, and more excellent in convergence, while maintaining a satisfying distribution performance. HMOEA-GL could be an effective intelligent algorithm for expert and intelligent decision support system to help logistics companies to make decisions. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： multiobjective evolutionary algorithm Vehicle routing problem Global search Local search Transportation

来源：评论

学校读者我要写书评

暂无评论

A reference vector based multiobjective evolutionary algorithm with Q-learning for operator adaptation

引用

SWARM AND evolutionary COMPUTATION 2023年 76卷

作者： Jiao, Keming Chen, Jie Xin, Bin Li, Li Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 201804 Peoples R China

Maintaining a balance between convergence and diversity is a challenge for multiobjective evolutionary optimization. As crossover operators can affect the offspring distribution, an adaptive operator selection and reference vector based evolutionary algorithm (OVEA) for multiobjective optimization is proposed, where adaptive operator selection (AOS) adopts Q-learning to choose crossover operators, and the reference vector assists individual selection. To make the objective vectors as close to the true Pareto Front (PF) as possible and distributed uniformly along with PF, the different crossover operators and the association between reference vectors and individuals are used to drive the population evolution. The selection range of each reference vector is controlled by the associated population subset, from which an elite individual is selected. Observing the performance of offspring, the appropriate crossover operator is picked up by Q-learning. Finally, the proposed algorithm is evaluated on the benchmark problems with different objective number ranging from 2 to 10 and compared against the state-of-the-art algorithms. The experimental results show that OVEA has remarked advantages over the compared algorithms.

关键词： multiobjective evolutionary algorithm Reference vector Adaptive operator selection Q-learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid multiobjective evolutionary algorithm considering combination timing for multi-type vehicle routing problem with time windows

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2022年 171卷

作者： Zhang, Wenqiang Li, Haoran Yang, Weidong Zhang, Guohui Gen, Mitsuo Henan Univ Technol Key Lab Grain Informat Proc & Control Zhengzhou Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Peoples R China Zhengzhou Univ Aeronaut Sch Management Engn Zhengzhou Peoples R China Tokyo Univ Sci Fuzzy Logic Syst Inst Tokyo Japan

Vehicle routing problem (VRP) has been a classical combinatorial optimization challenge, which has been extensively investigated in recent decades. As a VRP typical variant, multi-type vehicle routing problem with time windows (MT-VRPTW) has been concerned with multiple types of heterogeneous fleets of vehicles, within the delivery time window and limited capacity constraints. For such complicated multiobjective optimization problems, hybrid multiobjective evolutionary algorithm (HMOEA) has become an effective method, however, the research on hybrid algorithms and combination timing is still challenging. This study proposes a hybrid multiobjective evolutionary algorithm based on combination timing (HMOEA-CT) to solve MT-VRPTW with the criteria of minimizing the number of vehicles and wasting time simultaneously. The HMOEA-CT combines a fast convergence in multiple directions (FCMDs)-based global search and a problem-specific difference (PSD)-based local search in different evolutionary stages. The FCMD is assumed to be the global exploration search strategy, which converges faster at the center and edge regions of the Pareto front. The PSD is used to improve the local exploitation search ability by directing poor-performing individuals closer to better-performing individuals. Furthermore, the PSD cooperates with FCMD at the right time according to the different evolutionary stages to improve efficiency. Experimental results on Solomon benchmark problems demonstrate the competitive performance comparison of the HMOEA-CT with traditional multiobjective evolutionary algorithms.

关键词： multiobjective evolutionary algorithm Vehicle routing problem with time windows Multi-type vehicle Hybrid algorithm Combination timing

来源：评论

学校读者我要写书评

暂无评论

A decomposition-based multiobjective evolutionary algorithm using Simulated Annealing for the ambulance dispatching and relocation problem during COVID-19

引用

APPLIED SOFT COMPUTING 2023年 141卷 110282页

作者： Hemici, Meriem Zouache, Djaafar Brahmi, Boualem Got, Adel Drias, Habiba Univ Mohamed El Bachir El Ibrahimi Bordj Bou Arrer Dept Math Bordj Bou Arreridj Algeria Univ Mohamed El Bachir El Ibrahimi Bordj Bou Arrer Dept Comp Sci Bordj Bou Arreridj Algeria Univ Mohamed El Bachir El Ibrahimi Bordj Bou Arrer Dept Operat Res Bordj Bou Arreridj Algeria Univ Sci & Technol Houari Boumediene Lab Res Artificial Intelligence Algiers Algeria

The outbreak of the COVID-19 epidemic has had a significant impact in increasing the number of emergency calls, which causes significant problems to emergency medical services centers (EMS) in many countries around the world, such as Saudi Arabia, which attracts a huge number of pilgrims during pilgrimage seasons. Among these issues, we address real-time ambulance dispatching and relocation problems (real-time ADRP). This paper proposes an improved MOEA/D algorithm using Simulated Annealing (G-MOEA/D-SA) to handle the real-time ADRP issue. The simulated annealing (SA) seeks to obtain optimal routes for ambulances to cover all emergency COVID-19 calls through the implementation of convergence indicator based dominance relation (CDR). To prevent the loss of good solutions once they are found in the G-MOEA/D-SA algorithm, we employ an external archive population to store the non-dominated solutions using the epsilon dominance relationship. Several experiments are conducted on real data collected from Saudi Arabia during the Covid-19 pandemic to compare our algorithm with three relevant state-of-art algorithms including MOEA/D, MOEA/D-M2M and NSGA-II. Statistical analysis of the comparative results obtained using ANOVA and Wilcoxon test demonstrate the merits and the outperformance of our G-MOEA/D-SA algorithm. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Ambulance dispatching and relocation problems multiobjective evolutionary algorithm based on decomposition (MOEA/D) COVID-19 calls Simulated annealing (SA)

来源：评论

学校读者我要写书评

暂无评论

A hypervolume distribution entropy guided computation resource allocation mechanism for the multiobjective evolutionary algorithm based on decomposition

引用

APPLIED SOFT COMPUTING 2022年第0期116卷 108297-108297页

作者： Wang, Zhao Gong, Maoguo Li, Peng Gu, Jie Tian, Weidong Xidian Univ Key Lab Elect Informat Countermeasure & Simulat T Minist Educ 2 South TaiBaiRd Xian Peoples R China Sci & Technol Elect Informat Control Lab Chengdu Peoples R China

The computation resource allocation is a key issue to the multiobjective evolutionary algorithms. Present studies still have some difficulties addressing this issue such as low accuracy, indispensable parameter for balancing the convergence and diversity, discontinuous improvement values of different domination relations and poor discernibility. To solve these problems, a hypervolume distribution model regarding two non-adjacent individuals of each subproblem is proposed in this paper. Based on the hypervolume distribution model, a comprehensive hypervolume distribution entropy (HDE) by integrating the entropy and the Kullback-Leibler divergence is proposed to measure the improvement of the subproblems. The proposed measurement is continuous over different domination relations, requires no parameters and has high precision and discernibility. Thereafter, a hypervolume distribution entropy guided multiobjective evolutionary based on decomposition (HDE-MOEA/D) is proposed. The proposed algorithm is more efficient on solving multiobjective optimization problems. The proposed algorithm is tested on some popular test suits against another five typical and popular algorithms. The proposed HDE-MOEA/D achieves the best generational distances and the inverse generational distances in 57.9% ranking comparisons. The HDE-MOEA/D also outperforms another compared algorithm in 70.2% one-to-one comparisons. The experiment results prove the superiority of the proposed algorithm and reveal some important discoveries. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Kullback-Leibler divergence multiobjective evolutionary algorithm based on decomposition Resource allocation mechanism Entropy

来源：评论

学校读者我要写书评

暂无评论

Introduction of a new concept, age, into the multiobjective evolutionary algorithm in the two dimensional space

引用

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2003年第7期E86D卷 1304-1309页

作者： Kang, YH Bien, Z Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Taejon 305701 South Korea

Recently, several promising multiobjective evolutionary algorithms such as PESA, NSGA-II, and SPEA2 ([2], [4], [12]) have been developed. In this paper, we also propose a new multiobjective evolutionary algorithm whose performance is comparable to or better than those promising algorithms. In the new algorithm proposed here, an age concept is introduced and utilized to make the efficiency of the offspring generation high. The performance of the proposed algorithm is superior to those of the promising algorithms mentioned above for several test functions. In this paper, the proposed algorithm will be explained only in two dimensional parameter and objective space to show manifestly the meaning of an age concept.

关键词： age concept multiobjective evolutionary algorithm

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：