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Adaptive Knowledge Transfer in multifactorial evolutionary algorithm for the Clustered Minimum Routing Cost Problem

APPLIED SOFT COMPUTING 2021年 105卷 107253-107253页

作者： Thang, Ta Bao Long, Nguyen Binh Hoang, Ngo Viet Binh, Huynh Thi Thanh Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

Having tremendous promising impact on society, the Internet of Things refers to the connection of millions of worldwide devices to communicate and exchange data, which requires to aggregate similar devices into clusters for maximizing the network efficiency. Minimum Routing Cost Clustered Tree Problem (CluMRCT) is a lately investigated topic that has significant application towards enhancing the interconnectivity among various devices. Belonging to NP-Hard class, the CluMRCT problem can be solved effectively by a meta-heuristic approach such as multifactorial evolutionary algorithm (MFEA), which can facilitate to find better solutions for multiple problems simultaneously. Recently, an improved framework (called MFEA-II) has been introduced to overcome the weakness of algorithm performance governed by the degree of underlying inter-task synergies in the previous version. Therefore, this paper proposes to apply MFEA-II for solving multiple CluMRCT problems concurrently with the population representation under a probabilistic distribution model adept at online learning. In the proposed algorithm, evolutionary operators are applied in two levels: the first level is to construct a spanning tree in each cluster, while the second one builds a spanning tree for connecting all clusters. To reduce consuming resources, this paper also introduces a new method to calculate the cost of CluMRCT solution in linear time complexity. Numerous types of test instances are implemented to demonstrate the effectiveness of the proposed algorithm when its performance surpassed other state-of-the-art algorithms in most of the datasets. (C) 2021 Elsevier B.V. All rights reserved.

关键词： multifactorial evolutionary algorithm Minimum Routing Cost Clustered Tree Problem Adaptive Knowledge Transfer

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Effective multifactorial evolutionary algorithm for Solving the Cluster Shortest Path Tree Problem

Effective Multifactorial Evolutionary Algorithm for Solving ...

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

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

ISBN: (纸本)9781509060177

Arising from the need of all time for optimization of irrigation systems, distribution network and cable network, the Cluster Shortest Path Tree Problem (CSTP) has been attracting a lot of attention and interest from the research community. For such an NP-Hard problem with a great dimensionality, the approximation approach is usually taken. evolutionary algorithms, based on biological evolution, has been proved to be effective in finding approximate solutions to problems of various fields. The multifactorial evolutionary algorithm (MFEA) is one of the most recently exploited realms of EAs and its performance in solving optimization problems has been very promising. The main difference between the MFEA and the traditional Genetic algorithm (GA) is that the former can solve multiple tasks at the same time and take advantage of implicit genetic transfer in a multitasking problem, while the latter solves one problem and exploit one search space at a time. Considering these characteristics, this paper proposes a MFEA for CSTP tasks, together with novel genetic operators: population initialization, crossover, and mutation operators. Furthermore, a novel decoding scheme for deriving factorial solutions from the unified representation in the MFEA, which is the key factor to the performance of any variant of the MFEA, is also introduced in this paper. For examining the efficiency of the proposed techniques, experiments on a wide range of diverse sets of instances were implemented and the results showed that the proposed algorithms outperformed an existing heuristic algorithm for most of the testing cases. In the experimental results section, we also pointed out which cases allowed for a good performance of the proposed algorithm.

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

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An Effective Representation Scheme in multifactorial evolutionary algorithm for Solving Cluster Shortest-Path Tree Problem

An Effective Representation Scheme in Multifactorial Evoluti...

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

作者： Pham Dinh Thanh Dinh Anh Dung Tran Ngoc Tien Huynh Thi Thanh Binh Taybac Univ Fac Math Phys Informat Chieng Ngan Son La Vietnam Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

ISBN: (纸本)9781509060177

The wide range of applications of Cluster Tree Problems has been motivating extensive research into various algorithms and techniques with a view to promoting both efficiency of the solving and qualities of solutions. A representative of Cluster Tree Problems, the Cluster Shortest-Path Tree Problem (CSTP) arose from the practical need to optimize network systems such as irrigation systems, network cables and distribution systems. In this paper, we proposed the multifactorial evolutionary algorithm (MFEA) to approach the CSTP with a representation scheme based on the Cayley Code. The proposed algorithm exploit advantages of Cayley Code for improving the MFEAs performance and quality solutions. This approach also applied new decoding method to transform the solution from the unified search space to the tasks. Experiments were conducted to compare the performances of the proposed to another approximation algorithm on various set of instances. The experimental results show that proposed algorithm surpass existing algorithm on almost test cases.

关键词： multifactorial evolutionary algorithm Clustered Shortest-Path Tree Problem evolutionary algorithm Genetic algorithm Cayley Code Prufer Code

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AT-MFCGA: An Adaptive Transfer-guided multifactorial Cellular Genetic algorithm for evolutionary Multitasking

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INFORMATION SCIENCES 2021年 570卷 577-598页

作者： Osaba, Eneko Del Ser, Javier Martinez, Aritz D. Lobo, Jesus L. Herrera, Francisco TECNALIA Basque Res & Technol Alliance BRTA Derio 48160 Bizkaia Spain Basque Ctr Appl Math BCAM Bilbao 48009 Spain Univ Granada DaSCI Andalusian Inst Data Sci & Computat Intelli Granada Spain

Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, evolutionary Multitasking resorts to concepts from evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with evolutionary Multitasking environments coined as Adaptive Transfer-guided multifactorial Cellular Genetic algorithm (ATMFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned evolutionary Multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Multitasking Transfer optimization evolutionary multitask optimization multifactorial evolutionary algorithm

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Simpler and Enhanced multifactorial evolutionary algorithms for Continuous Optimization Tasks

Simpler and Enhanced Multifactorial Evolutionary Algorithms ...

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IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)

作者： Chang, Yangyang Sobelman, Gerald E. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781665420358

multifactorial optimization has become one of the most promising paradigms for evolutionary multitasking within the field of computational intelligence. This methodology can improve the performance results for multiple, simultaneous optimization problems by exploiting the transfer of genetic information between them. In this paper, we present an in-depth analysis of this approach by considering several variations of the standard multifactorial evolutionary algorithm (MFEA). By using a simpler structure together with some enhanced operators, two new multifactorial evolutionary algorithms are proposed. We demonstrate that, compared with the traditional MFEA, our approach produces better results on a set of continuous optimization benchmark problems.

关键词： single-objective optimization evolutionary computation multifactorial optimization continuous optimization problems multifactorial evolutionary algorithm

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Analysis on the Efficiency of multifactorial evolutionary algorithms 16th

Analysis on the Efficiency of Multifactorial Evolutionary Al...

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16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Huang, Zhengxin Chen, Zefeng Zhou, Yuren Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Youjiang Med Univ Nationalities Dept Comp Sci & Informat Technol Baise 533000 Peoples R China

ISBN: (纸本)9783030581145;9783030581152

Many experimental studies have demonstrated the superiority of multifactorial evolutionary algorithms (MFEAs) over traditional methods of solving each task independently. In this paper, we investigate this topic from theoretical analysis aspect. We present a runtime analysis of a (4+2) MFEA on several benchmark pseudo-Boolean functions, which include problems with similar tasks and problems with dissimilar tasks. Our analysis results show that, by properly setting the parameter rmp (i.e., the random mating probability), for the group of problems with similar tasks, the upper bound of expected runtime of the (4+2) MFEA on the harder task can be improved to be the same as on the easier one. As for the group of problems with dissimilar tasks, the expected upper bound of (4+2) MFEA on each task are the same as that of solving them independently. This study theoretically explains why some existing MFEAs perform better than traditional methods in experimental studies and provides insights into the parameter setting of MFEAs.

关键词： evolutionary multitasking multifactorial evolutionary algorithm Running time analysis

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A comparison of different clustering algorithms for the project time buffering problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 199卷

作者： Cao, Fangfang Servranckx, Tom Vanhoucke, Mario He, Zhengwen Xi An Jiao Tong Univ Sch Management 28 Xianning W Rd Xian 710049 Peoples R China Univ Ghent Fac Econ & Business Adm Tweekerkenstr 2 B-9000 Ghent Belgium Vlerick Business Sch Operat & Technol Ctr Reep 1 B-9000 Ghent Belgium Univ Coll London UCL UCL Sch Management 1 Canada Sq London E14 5AA England

This paper studies the decentralised time buffering problem (TBP) to absorb project risk by building sufficient buffers with the aim of obtaining astable project schedule. First, the position of the buffers in the project network should be determined and, subsequently, each buffer must be optimally sized. We investigate different activity clustering methods (K-means, rank order, criticality-based and network clustering) to determine the ideal groups of activities to be clustered together and protected by an allocated buffer. The obtained clusters of activities are then inputted in a multi-population multi-factorial evolutionary algorithm (MPMFEA) for creating buffers based on the characteristics of the activities in each cluster. To the best of our knowledge, this is the first study to integrate existing clustering methods into a buffering algorithm in order to optimise the project stability. Previous studies hybridising both methods use a single clustering algorithm (e.g. K-means) that does not use the same information than the buffering algorithm or require more complex (simulation- based) buffering methods. The computational experiments on a large set of artificial instances validate the effectiveness of the proposed MPMFEA for solving the TBP, especially in combination with the network clustering method. Although the generic K-Means method is still considered a viable option for clustering, the more pragmatic clustering methods are more effective. We inform project managers that considering precedence relations between activities during clustering is crucial, but mimicking this behaviour in all clustering methods does not guarantee successful protection of their projects.

关键词： Clustering Project scheduling Time buffering problem multifactorial evolutionary algorithm

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multifactorial memetic algorithm with adaptive auxiliary tasks for service migration optimization in mobile edge computing

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MEMETIC COMPUTING 2025年第2期17卷

作者： Li, Guo Liu, Zhaobo Liu, Ling Zhu, Zexuan Shenzhen Univ Shenzhen City Key Lab Embedded Syst Design Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510530 Peoples R China Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ China Chongqing 400044 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China

In high-speed mobile networks, mobile edge computing is tasked with service migration optimization, i.e., assigning mobile users to the right servers to minimize the response time. Service migration optimization is a complex problem posing significant challenges to conventional optimization methods. To tackle this problem, we develop a multifactorial memetic algorithm with adaptive auxiliary tasks or MFMA-AAT for short. MFMA-AAT solves the target service migration optimization problem and an adaptively selected auxiliary task simultaneously, where the auxiliary task is a simplified version of the target problem to guide the search towards promising regions faster via knowledge transfer. Multiple auxiliary tasks are pre-constructed based on the distribution of the mobile users and the one with the best improvement at each generation is selected for knowledge transfer. A community detection-based memetic operator is also introduced to accelerate the local convergence of the proposed algorithm. Experimental results on test problems demonstrate that MFMA-AAT is more efficient than traditional service migration approaches and other state-of-the-art multifactorial evolutionary algorithms.

关键词： evolutionary multitasking multifactorial evolutionary algorithm Multi-form evolutionary optimization Service migration optimization Mobile edge computing

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A buffer allocation evolutionary algorithm for resource-constrained projects with activity clusters

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JOURNAL OF SCHEDULING 2025年 1-29页

作者： Cao, Fangfang Servranckx, Tom He, Zhengwen Vanhoucke, Mario Xi An Jiao Tong Univ Sch Management Xianning West Rd 28 Xian 710049 Peoples R China Univ Ghent Fac Econ & Business Adm Tweekerkenstr 2 B-9000 Ghent Belgium Vlerick Business Sch Technol & Operat Management Reep 1 B-9000 Ghent Belgium UCL UCL Sch Management 1 Canada Sq London E14 5AA England

We propose a novel approach for sizing the activity buffers in the project by clustering similar activities and allocating the buffers using a unique attribute in each cluster. Since the number of clusters as well as the assignment of attributes to these clusters has an impact on the buffer sizing, the problem is solved using an adapted multifactorial evolutionary algorithm (aMFEA) in which multiple buffer allocation problems (BAPs) are solved simultaneously. Several decoding schemes are compared to improve the synergies between the different BAPs and the evolutionary operators. The results show the added value of the evolutionary components of the aMFEA and show that the proposed approach is superior to existing benchmarking procedures. Furthermore, the solution quality improves with an increasing number of clusters, while the solution quality goes down again as the number of clusters becomes too large. From a practical perspective, this study highlights the need to identify good activity attributes that are linked to the buffer sizing decisions and the importance of activity clustering in order to reduce the time and effort needed for better buffer sizing decisions.

关键词： multifactorial evolutionary algorithm Project scheduling Buffer allocation problem Clustering

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Towards urban energy sustainability and resiliency through smart and effective renewable resource allocation

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SUSTAINABLE CITIES AND SOCIETY 2024年 107卷

作者： Cheng, Lijun Samah, Azurah Zhang, Yubo Jia, Zongwei Li, Fuzhong Univ Technol Malaysia Fac Comp Skudai Malaysia Shanxi Agr Univ Sch Software Taigu 030801 Shanxi Peoples R China Shanxi Agr Univ Sch Informat Sci & Engn Taigu 030801 Shanxi Peoples R China

Urban energy sustainability and resiliency are paramount in addressing the challenges of modern urbanization. This paper introduces a novel framework that seamlessly integrates smart agriculture practices with an effective renewable resource allocation strategy to optimize energy utilization in urban and suburban areas. The optimization models employ a multifactorial evolutionary algorithm, providing a dynamic and adaptive approach to fine-tune system parameters. The results, presented through numeric tables and colorful figures, showcase optimized agricultural production levels, renewable resource utilization, and the impact of specific factors and parameters on algorithmic performance. Notably, the study reveals the intricate balance achieved through the integration of smart agriculture and renewable energy, offering actionable insights for sustainable urban development. The visualizations not only enhance result comprehension but also serve as powerful tools for effective communication. This research contributes to the evolving discourse on urban energy sustainability, providing a systematic approach for future investigations at the intersection of agriculture, renewable energy, and optimization algorithms.

关键词： Urban energy sustainability Smart agriculture Renewable resource allocation multifactorial evolutionary algorithm Optimization models

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