检索结果-内蒙古大学图书馆

A Particle Swarm Optimization and Variable Neighborhood Search based multipopulation algorithm for Inter-Domain Path Computation problem

引用

APPLIED SOFT COMPUTING 2023年 136卷

作者： Anh, Do Tuan Binh, Huynh Thi Thanh Thai, Nguyen Duc Thanh, Pham Dinh Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam Taybac Univ Fac Nat Sci & Technol Son La Vietnam

It is a common occurrence nowadays for a network to be gigantic in size and complex in architecture. Network navigation thus faces many new problems in terms of routing and resource utilization, one of which arises in multi-domain networks. This paper focuses on the Inter-Domain Path Computation under Node-defined Domain Uniqueness Constraint (IDPC-NDU) problem, whose purpose is to find the shortest path between two nodes in a network under a constraint that such a path is only allowed to traverse each domain at most once. Considering that this is an NP-Hard problem, an approximate approach is more practical than an exact one. Meanwhile, Particles Swarm Optimization algorithm (PSO) has been long known for its powerful ability to discover near-optimal solutions in a reasonable time;however, its application in discrete search space is limited. Therefore, we decide to use the multi-population framework, a sub-branch of multitasking optimization which allows information exchange not only between problems but also between different optimization heuristics, to improve upon the basic PSO method. Specifically, this paper introduces a hybridization between PSO and Variable Neighborhood Search (VNS). The encoding and decoding method are created specifically for the IDPC-NDU problem and for the PSO algorithm, and VNS serves to enhance further the algorithm's ability to escape local optima. Experiments are carried out to prove the new algorithm's efficacy, especially in the context of similar multitasking methods. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Multi-population multitasking evolutionary algorithm multifactorial evolutionary algorithm Inter-domain path computation problem evolutionary algorithms Multitasking optimization

来源：评论

学校读者我要写书评

暂无评论

A Multi-task Approach For Maximum Survival Ratio Problem In Large-Scale Wireless Rechargeable Sensor Networks

A Multi-task Approach For Maximum Survival Ratio Problem In ...

引用

IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Le Van Cuong Tran Thi Huong Huynh Thi Thanh Binh Hanoi Univ Sci & Technol Hanoi Vietnam VNU Univ Sci Hanoi Vietnam

ISBN: (纸本)9781728183923

With the breakthrough of electromagnetic power transfer technology, wireless charging has emerged as a hopeful solution for the energy provisioning problem in wireless sensor networks. One of the prominent issues that affect the potential exploitation of this technology is the charging scheduling problem. However, existing works on this topic either focus mainly on using a single mobile charger for the whole network or suffer from several common limitations such as enforcing the chargers to visit all sensors or applying the rigid full-charging scheme. Moreover, they rarely delve into maximizing the survival nodes ratio, which impacts directly on the multi-hop communication of the network. This paper addresses the charging scheduling for multiple mobile chargers without the above limitations. We first formulate a maximum survival ratio problem and prove its NP-hardness. A charging scheme that exploits the advantages of the multifactorial evolutionary algorithm is then proposed to optimize the charging paths of all chargers simultaneously. We finally evaluate the efficacy of the proposed algorithm through extensive simulations. The experimental results demonstrate that our scheduling scheme provides promising outcomes in terms of survival ratio and the traveling energy of chargers.

关键词： Wireless rechargeable sensor network multiple mobile chargers maximum survival ratio problem multifactorial evolutionary algorithm multi-task optimization

来源：评论

学校读者我要写书评

暂无评论

evolutionary Multitask Optimization: Fundamental research questions, practices, and directions for the future

引用

SWARM AND evolutionary COMPUTATION 2022年 75卷

作者： Osaba, Eneko Del Ser, Javier Suganthan, Ponnuthurai N. TECNALIA Basque Res & Technol Alliance BRTA P Tecnol Ed 700 Derio 48160 Spain Univ Basque Country UPV EHU Bilbao 48013 Spain Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore TECNALIA Basque Res & Technol Alliance BRTA P Tecnol Ed 700 Derio 48170 Bizkaia Spain

Transfer Optimization has gained a remarkable attention from the Swarm and evolutionary Computation community in the recent years. It is undeniable that the concepts underlying Transfer Optimization are formulated on solid grounds. However, evidences observed in recent contributions confirm that there are critical aspects that are not properly addressed to date. This short communication aims to engage the readership around a reflection on these issues, and to provide rationale why they remain unsolved. Specifically, we emphasize on three critical points of evolutionary Multitasking Optimization: (i) the plausibility and practical applicability of this paradigm;(ii) the novelty of some proposed multitasking methods;and (iii) the methodologies used for evaluating newly proposed multitasking algorithms. As a result of this research, we conclude that some important efforts should be directed by the community in order to keep the future of this promising field on the right track. Our ultimate purpose is to unveil gaps in the current literature, so that prospective works can attempt to fix these gaps, avoiding to stumble on the same stones and eventually achieve valuable advances in the area.

关键词： Transfer Optimization Multitasking optimization evolutionary multitasking multifactorial evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Hetero-Dimensional Multitask Neuroevolution for Chaotic Time Series Prediction

引用

IEEE ACCESS 2020年 8卷 123135-123150页

作者： Zhang, Daoqing Jiang, Mingyan Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China

Chaotic time series prediction has important research and application value, and neural network-based prediction methods have problems such as low accuracy and difficulty in determining the number of nodes in the hidden layer. In recent years, as a representative of evolutionary multitask optimization algorithms(EMTO), multifactorial evolutionary algorithms (MFEA) have achieved excellent results in Multitask Optimization (MTO) problems, but it does not work well in solving hetero-dimensional multitask problem. We analyzed this phenomenon and proposed the concept of harmful transfer. Then to solve these problems, in this paper, we propose the hetero-dimensional multitask neuroevolution algorithms (HD-MFEA neuroevolution) for chaotic time series prediction. First, we propose hetero-dimensional assortative mating algorithm and self-adaption elite replacement algorithm, so that the hetero-dimensional multitask evolutionary algorithm (HD-MFEA) can overcome the problems caused by harmful transfer on hetero-dimensional multitask problem. Second, in order to adapt to the hierarchical characteristics of neural networks, we propose the neural level segmental coding strategy. Third, we consider the optimization of neural networks with different hidden layer nodes as hetero-dimensional multitask problem and use the HD-MFEA neuroevolution to solve this problem. We verify the effectiveness of the HD-MFEA on six hetero-dimensional multitask benchmark problems. The Mackey-Glass, Lorenz and Sunspot time series are used to demonstrate the performance of the HD-MFEA neuroevolution. Experimental results have showed that the HD-MFEA neuroevolution performs well in predicting the chaotic time series problems.

关键词： Chaotic time series prediction evolutionary algorithm evolutionary multitask optimization multifactorial evolutionary algorithm neuroevolution

来源：评论

学校读者我要写书评

暂无评论

An efficient strategy for using multifactorial optimization to solve the clustered shortest path tree problem

引用

APPLIED INTELLIGENCE 2020年第4期50卷 1233-1258页

作者： Thanh, Pham Dinh Binh, Huynh Thi Thanh Trung, Tran Ba Taybac Univ Fac Math Phys Informat Son La Vietnam Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

Arising from the need of all time for optimization of irrigation systems, distribution network and cable network, Clustered Shortest-Path Tree Problem (CluSPT) has been attracting a lot of attention and interest from the research community. On the other hand, the multifactorial evolutionary algorithm (MFEA) is one of the most recently exploited realms of evolutionary algorithms (EAs) and its performance in solving optimization problems has been very promising. Considering these characteristics, this paper describes a new approach using the MFEA for solving the CluSPT. The MFEA has two tasks: the goal of the first task is to determine the best tree (w.r.t. cost minimization) which envelops all vertices of the CluSPT while the goal of the second task is to find the fittest solution possible for the problem. The purpose of the second task is to find good materials for implicit genetic transfer process in MFEA to improve the quality of CluSPT. To apply this new algorithm, a decoding scheme for deriving individual solutions from the unified representation in the MFEA is also introduced in this paper. Furthermore, evolutionary operators such as population initialization, crossover and mutation operators are also proposed. These operators are applicable for constructing valid solution from both sparse and complete graph. Although the proposed algorithm is slightly complicated for implementation, it can enhance ability to explore and exploit the Unified Search Space (USS). To prove this increment in performance i.e, to assess the effectiveness of the proposed algorithm and methods, the authors implemented them on both Euclidean and Non-Euclidean instances. Experiment results show that the proposed MFEA outperformed existing heuristic algorithms in most of the test cases. The impact of the proposed MFEA was analyzed and a possible influential factor that may be useful for further study was also pointed out.

关键词： multifactorial evolutionary algorithm Clustered shortest-path tree problem evolutionary algorithms Genetic algorithm multifactorial optimization

来源：评论

学校读者我要写书评

暂无评论

A bi-level encoding scheme for the clustered shortest-path tree problem in multifactorial optimization

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 100卷 104187-104187页

作者： Huynh Thi Thanh Binh Ta Bao Thang Nguyen Duc Thai Pham Dinh Thanh Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam Taybac Univ Fac Nat Sci & Technol Son La Vietnam

The Clustered Shortest-Path Tree Problem (CluSPT) plays an important role in various types of optimization problems in real-life. Recently, some multifactorial evolutionary algorithms (MFEAs) have been introduced to deal with the CluSPT, but these researches still have some shortcomings, such as evolution operators only perform on complete graphs and huge resource consumption for finding the solution on large search spaces. To overcome these limitations, this paper describes an MFEA-based approach to solve the CluSPT. The proposed algorithm utilizes Dijkstra?s algorithm to construct the spanning trees in clusters while using evolutionary operators for building the spanning tree connecting clusters. This approach takes advantage of both exact and approximate algorithms, so it enables the algorithm to function efficiently on complete and sparse graphs alike. Furthermore, evolutionary operators such as individual encoding and decoding methods are also designed with great consideration regarding performance and memory usage. We have included proof of the repairing method?s efficacy in ensuring all solutions are valid. We have conducted tests on various types of Euclidean instances to assess the effectiveness of the proposed algorithm and methods. Experiment results point out the effectiveness of the proposed algorithm existing heuristic algorithms in most of the test cases. The impact of the proposed MFEA was analyzed, and a possible influential factor that may be useful for further study was also pointed out.

关键词： multifactorial evolutionary algorithm Clustered Shortest-Path Tree Problem evolutionary algorithms multifactorial optimization

来源：评论

学校读者我要写书评

暂无评论

multifactorial Cellular Genetic algorithm (MFCGA): algorithmic Design, Performance Comparison and Genetic Transferability Analysis

Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithm...

引用

IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Osaba, Eneko Martinez, Aritz D. Lobo, Jesus L. Del Ser, Javier Herrera, Francisco TECNALIA Basque Res & Technol Alliance BRTA Derio 48160 Bizkaia Spain Univ Basque Country UPV EHU Bilbao 48013 Bizkaia Spain Univ Granada DaSCI Andalusian Inst Data Sci & Computat Intelli Granada 18071 Spain

ISBN: (纸本)9781728169293

Multitasking optimization is an incipient research area which is lately gaining a notable research momentum. Unlike traditional optimization paradigm that focuses on solving a single task at a time, multitasking addresses how multiple optimization problems can be tackled simultaneously by performing a single search process. The main objective to achieve this goal efficiently is to exploit synergies between the problems (tasks) to be optimized, helping each other via knowledge transfer (thereby being referred to as Transfer Optimization). Furthermore, the equally recent concept of evolutionary Multitasking (EM) refers to multitasking environments adopting concepts from evolutionary Computation as their inspiration for the simultaneous solving of the problems under consideration. As such, EM approaches such as the multifactorial evolutionary algorithm (MFEA) has shown a remarkable success when dealing with multiple discrete, continuous, single-, and/or multi-objective optimization problems. In this work we propose a novel algorithmic scheme for multifactorial Optimization scenarios - the multifactorial Cellular Genetic algorithm (MFCGA) - that hinges on concepts from Cellular Automata to implement mechanisms for exchanging knowledge among problems. We conduct an extensive performance analysis of the proposed MFCGA and compare it to the canonical MFEA under the same algorithmic conditions and over 15 different multitasking setups (encompassing different reference instances of the discrete Traveling Salesman Problem). A further contribution of this analysis beyond performance benchmarking is a quantitative examination of the genetic transferability among the problem instances, eliciting an empirical demonstration of the synergies emerged between the different optimization tasks along the MFCGA search process.

关键词： Transfer Optimization evolutionary Multitasking Cellular Genetic algorithm multifactorial evolutionary algorithm Traveling Salesman Problem

来源：评论

学校读者我要写书评

暂无评论

Multi-objective multi-factorial memetic algorithm based on bone route and large neighborhood local search for VRPTW

Multi-objective multi-factorial memetic algorithm based on b...

引用

IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Zhou, Zifeng Ma, Xiaoliang Liang, Zhengping Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

ISBN: (纸本)9781728169293

Multi-tasking optimization (MO) has attracted increasing attention in the domain of evolutionary computation. Different from single-tasking optimization, MTO can solve multiple optimization tasks simultaneously to improve the performance of solving each optimization task by inter-task knowledge transfer. multifactorial evolutionary algorithm (MFEA) is one of the most widely used MTO algorithm based on assortative mating and vertical cultural transmission. This work extends MFEA by integrating bone route and large neighborhood local search to solve multi-objective vehicle routing problem with time window (VRPTW). The VRPTW is modeled as two related tasks, i.e., one is a multi-objective version of VRPTW (the main task), and the other is a single-objective version of VRPTW (the auxiliary task). The resultant new algorithm namely multi-objective multi-factorial memetic algorithm (MOMFMA) solve the two tasks simultaneously where the information between the tasks is exchanged in the evolutionary process. In addition to the implicit information transfer of MFEA, the bone route is introduced to enable explicit information transfer between tasks. Particularly, bone routes are constructed as semi-finished product solutions and used in large neighborhood local search. The bone route and the large neighborhood local search work together to speed up the convergence of the algorithm. MOMFMA is tested on Solomon's 56 datasets and the experimental results demonstrate that the efficiency of MOMFMA.

关键词： multi-tasking optimization multifactorial evolutionary algorithm multi-objective optimization vehicle routing problem

来源：评论

学校读者我要写书评

暂无评论

Parameter analysis on multi-factorial evolutionary algorithm

引用

JOURNAL OF ENGINEERING-JOE 2020年第13期2020卷 620-625页

作者： Xu, Qingzheng Zhang, Jianhang Fei, Rong Li, Wei Natl Univ Def Technol Coll Informat & Commun Xian 710106 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Air Force Engn Univ Air & Missile Def Coll Xian 710051 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China

As a novel and representative multi-task optimisation (MTO) paradigm, multi-factorial evolutionary algorithm (MFEA) can solve multiple self-contained tasks simultaneously. Its overall performance highly depends on control parameters. The aim of this research work is to analyse three parameters, namely, probability of individual learning, probability of intra-crossover and probability of inter-crossover, controlled by the user. Experimental results on MTO problems demonstrate the superiority of MFEA with a smaller probability of individual learning in a fair competitive environment. While the influence of probabilities of intra-crossover and inter-crossover is unpredictable based on the task's features, the basic selection principle and the optimal value are provided based on massive simulated data.

关键词： learning (artificial intelligence) evolutionary computation probability data analysis MTO problems massive simulated data control parameters multiple self-contained tasks multifactorial evolutionary algorithm individual learning smaller probability MFEA

来源：评论

学校读者我要写书评

暂无评论

multifactorial Differential Evolution with Opposition-based Learning for Multi-tasking Optimization

Multifactorial Differential Evolution with Opposition-based ...

引用

IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Yu, Yanan Zhu, Anmin Zhu, Zexuan Lin, Qiuzhen Yin, Jian Ma, Xiaoliang Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China

ISBN: (纸本)9781728121536

Recently, multi-tasking optimization (MTO) has become a rising research topic in the field of evolutionary computation that has attracted increasing attention of academia. Comparing with single-objective optimization (SOO) and multi-objective optimization (MOO), MTO can solve different optimization tasks simultaneously by utilizing inter-task similarities and complementarities. Based on crossover operator, the classical multifactorial evolutionary algorithm (MFEA) transfers inter-task knowledge. To broaden the search region and accelerate the convergence, this paper integrates differential evolution (DE) and opposition-based learning (OBL) into MFEA and hence proposes MFEA/DE-OBL. The motivation of integrating DE and OBL is that they have different search neighborhoods and strong complementarity with simulated binary crossover (SBX) used in MFEA. Furthermore, integrating DE and OBL can help MFEA jump out of local optima. The effectiveness and efficiency of integrating DE and OBL into MFEA are experimentally studied on a set of benchmark problems with different degrees of similarities. Experimental results demonstrate that the proposed MFEA/ DE-OBL dramatically improves the performance compared with the MFEA.

关键词： multi-tasking optimization multifactorial evolutionary algorithm opposition-based learning differential evolution

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：