检索结果-内蒙古大学图书馆

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

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

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

Parting Ways and Reallocating Resources in evolutionary Multitasking

Parting Ways and Reallocating Resources in Evolutionary Mult...

引用

IEEE Congress on evolutionary Computation (CEC)

作者： Wen, Yu-Wei Ting, Chuan-Kang Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi 621 Taiwan Natl Chung Cheng Univ Adv Inst Mfg Hightech Innovat Chiayi 621 Taiwan

ISBN: (纸本)9781509046010

evolutionary multitasking aims to explore implicit synergy among multiple optimization tasks. Through the effect of hitchhiking, evolutionary multitasking is capable of improving the performance of evolutionary algorithms on exploration as well as exploitation. multifactorial evolutionary algorithm (MFEA) presented an effectual implementation of evolutionary multitasking, which simultaneously seeks the solutions to multiple optimization problems by unifying their search spaces. The MFEA enables information sharing across tasks during evolution. This mechanism can improve the evolutionary efficiency in the early phase;however, it will impair the exploitation and consume extra resources later on, due to the essential difference among the fitness landscapes of optimization problems. This study proposes detecting the occurrence of parting ways, at which the information sharing begins to fail. In addition, we develop the resource allocation mechanism to reallocate the fitness evaluations on different types of offspring by ceasing information sharing when parting ways. Experiments are conducted to evaluate the proposed methods. The experimental results show that applying parting ways detection and resource reallocation for MFEA can achieve better solution quality in most of testing cases, especially when the tasks share low similarity of landscapes.

关键词： evolutionary multitasking information sharing multifactorial evolutionary algorithm parting ways resource allocation

来源：评论

学校读者我要写书评

暂无评论

A hybrid and adaptive evolutionary approach for multitask optimization of post-disaster traveling salesman and repairman problems

引用

COMPUTERS & OPERATIONS RESEARCH 2024年 167卷

作者： Ban, Ha-Bang Binh, Huynh Thi Thanh Do, Tuan Anh Tran, Cong Dao Nguyen, Su Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam RMIT Univ Melbourne Australia FPT Software AI Ctr Hanoi Vietnam

Traveling Salesman Problem (TSP) and Traveling Repairman Problem (TRP) have been studied due to their real -world applications. While the TSP minimizes the total time to travel to all customers, the TRP minimizes the total waiting time between a main depot and customers. Solving two problems simultaneously is shown to obtain promising results due to their high similarity in solution representation and overlap in search space. However, these two problems in the context of post -disaster have not yet been considered in line with the three following restrictions. First, the salesman cannot travel on several destroyed roads. Second, the salesman needs additional time to remove debris, which means the debris removal time is added to the travel cost. Finally, we do not consider each vertex equally because they have different characteristics depending on their population or vulnerability. Therefore, reaching a vertex with higher priority takes more benefit overall. To tackle these problems, we first formalize TSP and TRP in post -disaster scenarios as TSPPD and TRPPD, respectively, and then propose the effective metaheuristic, MFEA-TS, combining a multifactorial evolutionary algorithm (MFEA) and Tabu Search (TS) to solve them simultaneously and efficiently. In the proposed MFEATS, the MFEA can explore the search space well by transferring knowledge between two problems, while the TS can exploit good solutions in the search space. The proposed algorithm overcomes the drawbacks of the existing MFEA algorithms due to good exploitation capacity and prevention of revisiting previous solution spaces. We further conduct extensive experiments to verify the performance of our MFEA-TS with TRPPD and TSPPD. The empirical results show that the proposed algorithm can give high -quality solutions on a range of benchmarks, thus confirming the impressive efficiency of the proposed formulations and algorithm MFEA-TS.

关键词： Traveling salesman problem Traveling repairman problem Post-disaster scenarios multifactorial evolutionary algorithm Tabu search

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

Enhancing evolutionary multitasking optimization by leveraging inter-task knowledge transfers and improved evolutionary operators

引用

KNOWLEDGE-BASED SYSTEMS 2023年 259卷

作者： Ma, Xiaoliang Xu, Mingyu Yu, Yanan Liu, Hongjie Wang, Yan Wang, Lei Qi, Yutao Xiong, Jian Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol SIAT Shenzhen 518055 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Southwestern Univ Finance & Econ Sch Business Adm Chengdu 610074 Peoples R China

It is inefficient and time-consuming to begin the search from scratch for each optimization task. evolutionary multitasking optimization (EMTO) handles multiple tasks simultaneously, aiming at im-proving the solving quality of every task via the evolutionary algorithm (EA) and inter-task knowledge transfer. Thus, suitable evolutionary operators and inter-task effective knowledge transfer are two key factors for the success of EMTO. As one of the representative EMTO algorithms, the multifactorial evolutionary algorithm (MFEA) has attracted a lot of attention. However, MFEA has suffered from the issue of premature convergence and negative knowledge transfer among tasks with a low correlation. To handle these issues, this article enhances MFEA with two proposed strategies, namely a carefully -designed opposition-based learning (OBL) strategy and a carefully-designed differential evolution (DE) strategy, named MFDE-OBL for short. Both the proposed OBL and the proposed DE contain an inter -task strategy and an intra-task strategy. To improve the effectiveness of knowledge transfer, the inter-task OBL strategy learns a linear subspace mapping among task/tasks' subpopulations to transfer different search scales among tasks, while the inter-task DE strategy uses genetic information from another task to improve the population diversity with different scales and directions. Besides, the intra-task generalized-opposite-point-based OBL is used to enhance the global search ability, while the intra-task DE strategy consists of two complementary DE strategies to maintain a good balance between exploitation and exploration. Finally, the proposed algorithm is tested on both single-objective and multi-objective multi-tasking test suites. Experimental results have shown the efficiency and effectiveness of the proposed algorithm compared with both classical and state-of-the-art algorithms.(c) 2022 Published by Elsevier B.V.

关键词： evolutionary multitasking optimization multifactorial evolutionary algorithm Inter -task knowledge transfer Opposition -based learning

来源：评论

学校读者我要写书评

暂无评论

Compositional model based on factorial evolution for realizing multi-task learning in bacterial virulent protein prediction

引用

ARTIFICIAL INTELLIGENCE IN MEDICINE 2019年 101卷 101757-000页

作者： Singh, Deepak Singh, Pradeep Sisodia, Dilip Singh Natl Inst Technol Dept Comp Sci & Engn Raipur CG India

The ability of multitask learning promulgated its sovereignty in the machine learning field with the diversified application including but not limited to bioinformatics and pattern recognition. Bioinformatics provides a wide range of applications for Multitask Learning (MTL) methods. Identification of Bacterial virulent protein is one such application that helps in understanding the virulence mechanism for the design of drug and vaccine. However, the limiting factor in a reliable prediction model is the scarcity of the experimentally verified training data. To deal with, casting the problem in a Multitask Learning scenario, could be beneficial. Reusability of auxiliary data from related multiple domains in the prediction of target domain with limited labeled data is the primary objective of multitask learning model. Due to the amalgamation of multiple related data, it is possible that the probability distribution between the features tends to vary. Therefore, to deal with change amongst the feature distribution, this paper proposes a composite model for multitask learning framework which is based on two principles: discovering the shared parameters for identifying the relationships between tasks and common underlying representation of features amongst the related tasks. Through multi-kernel and factorial evolution, the proposed framework able to discover the shared kernel parameters and latent feature representation that is common amongst the tasks. To examine the benefits of the proposed model, an extensive experiment is performed on the freely available dataset at VirulentPred web server. Based on the results, we found that multitask learning model performs better than the conventional single task model. Additionally, our findings state that if the distribution between the tasks is high, then training the multiple models yield slightly better prediction. However, if the data distribution difference is low, multitask learning significantly outperforms the individua

关键词： multifactorial evolutionary algorithm Multitask learning Multi-kernel learning Virulent protein

来源：评论

学校读者我要写书评

暂无评论

Node depth Representation-based evolutionary Multitasking Optimization for Maximizing the Network Lifetime of Wireless Sensor Networks

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 128卷

作者： Dao, Tran Cong Tam, Nguyen Thi Binh, Huynh Thi Thanh Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam VNU Univ Sci Hanoi Vietnam FPT Software AI Ctr Hanoi Vietnam

Wireless Sensor Networks (WSNs) face challenges related to limited energy resources in sensor nodes, making network lifetime and energy consumption critical considerations. In this paper, we introduce a novel approach to extend network lifetime and reduce energy consumption in WSNs by optimizing network architecture selection. We explore various architecture options, including single-layer and multi-layer networks, to determine the most suitable configuration. Several studies have applied knowledge transfer in evolutionary multitasking optimization to maximize network lifetime, achieving state-of-the-art results for the problem. However, they still have some drawbacks, such as redundant representations, i.e., many genotypes represent the same phenotype, and invalid solution generation. To address these limitations, we propose a new evolutionary multitasking algorithm incorporating efficient encoding to represent tree-structure solutions (network architectures) to the problem. Additionally, we tailor new genetic operators, such as recombination and mutation, to meet the specific requirements of our proposed evolutionary multitasking algorithm. The standout feature of our proposal lies in its ability to consistently generate valid solutions for the problem, which significantly reduces redundancy within the genotype search space and effectively satisfies the problem constraints. Additionally, our multitask evolutionary algorithm facilitates the exploration of various network architectures, harnessing knowledge transfer to enhance optimal performance and reduce computation time compared to single-task methods. We conduct comprehensive experiments on diverse datasets and use statistical tests, e.g., the Wilcoxon signed rank test, to verify the performance of our proposed algorithms. The empirical results demonstrate that our proposed algorithm outperforms existing methods and achieves state-of-the-art results in terms of solution quality, convergence rate, and running ti

关键词： evolutionary multitasking optimization multifactorial evolutionary algorithm Node-depth encoding Network lifetime Wireless sensor network

来源：评论

学校读者我要写书评

暂无评论

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

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：