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Helper objective-based multifactorial evolutionary algorithm for continuous optimization

SWARM AND evolutionary COMPUTATION 2023年 78卷

作者： Yang, Xu Li, Wenhua Wang, Rui Yang, Kang Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China

Searching for global optima is challenging for complex optimization problems since multi-modality commonly exists. To address this issue, researchers have made many attempts, including high-performance initializing methods, novel variant operators and multimodal-based environmental selection strategies. However, such approaches are highly sensitive to the parameters or their implementation is complex. To this end, in this work, a helper problem (objective function) is proposed to assist evolutionary algorithms (EAs) in dealing with complex optimization problems, where the multitask optimization framework is adopted to transfer knowledge between the helper and the original problems. Specifically, the helper objective is a smoother fit of the original problem, which leads EAs to concentrate on the global optima of the original problem. Then, the helper objective-assisted multifactorial differential evolutionary algorithm is proposed, termed h-MFDE. Experimental results show that h-MFDE is competitive in finding the global optimal on complex optimization problems comparing with other state-of-the-art EAs.

关键词： Helper objective Surrogate model Multitask optimization Gaussian process modeling multifactorial evolutionary algorithm Differential evolution

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An online transfer learning based multifactorial evolutionary algorithm for solving the clustered Steiner tree problem

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KNOWLEDGE-BASED SYSTEMS 2024年 296卷

作者： Long, Nguyen Binh Anh, Do Tuan Ban, Ha-Bang Binh, Huynh Thi Thanh Queensland Univ Technol Sch Elect Engn & Robot Brisbane Australia Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

The rapid increase in the number and computing power of devices interacting through the network over the past few years has led to a growing trend of decentralized clustering network architecture. The Clustered Steiner Tree Problem (CluSteiner) is a recently introduced NP-Hard problem and is assessed to be the core of efficient multicast routing in such networks. However, research on this problem in terms of theory and algorithmic design is still in its infancy as current solving approaches are limited in the literature. Lately, the multifactorial evolutionary algorithm (MFEA) has been applied to solve several clustering-structure network design problems for the reason that these problems rarely exist independently and are often deployed concurrently in practice. To effectively transfer knowledge between problems while solving them simultaneously, this paper proposes an approach based on the novel data-driven MFEA-II to deal with the CluSteiner problem. In the proposal, an efficient encoding and a two-level decoding mechanism are introduced, combined with a module capable of online learning the amount of knowledge transferred between tasks that help the algorithm to explore potential regions of the search space. Experiments are undertaken on various test instances to evaluate the efficacy of the proposed algorithm. The empirical results indicate that our proposal can perform well on both metric and non-metric graphs with the statistical tests used to verify its efficiency over other baseline algorithms regarding the solution quality and convergence trend.

关键词： multifactorial evolutionary algorithm Clustered Steiner tree problem Adaptive knowledge transfer

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

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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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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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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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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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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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Hetero-Dimensional Multitask Neuroevolution for Chaotic Time Series Prediction

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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

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