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multifactorial evolutionary algorithm for optimal reconfiguration capability of distribution networks

SWARM AND evolutionary COMPUTATION 2024年 88卷

作者： Li, Qingxia Huang, Shengjun Zhang, Xueyang Li, Wenhua Wang, Rui Zhang, Tao Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China

Most existing studies on distribution network reconfiguration (DNRC) are predominantly based on a fixed initial topology and optimize switch operations to achieve various objectives, without considering the flexible allocation of backup lines. This paper proposes a novel problem of optimizing the reconfiguration capability of distribution networks (DNs) by considering the expansion of power lines under resource constraints. The reconfiguration capability reflects the ability of a DN to respond to uncertainties and disturbances by adjusting its topology. A novel metric based on the number of spanning trees is proposed to quantify the reconfiguration capability of a DN. Moreover, an optimization model is formulated to maximize the reconfiguration capability of a DN subject to resource constraints on line expansions. To solve this model efficiently, a multifactorial evolutionary algorithm (MFEA) is developed, which can optimize multiple tasks with different expansion line quantities simultaneously by exploiting knowledge transfer across tasks. The proposed metric, model, and algorithm are validated on two case studies using the IEEE 33 -bus and 70 -bus test systems, and the results show their effectiveness and superiority over existing methods.

关键词： Reconfiguration capability Topology optimization Distribution network multifactorial evolutionary algorithm

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multifactorial evolutionary algorithm for solving clustered tree problems: competition among Cayley codes Case studies on the clustered shortest-path tree problem and the minimum inter-cluster routing cost clustered tree problem

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MEMETIC COMPUTING 2020年第3期12卷 185-217页

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

The multifactorial evolutionary algorithm (MFEA) has emerged as an effective variant of the evolutionary algorithm. MFEA has been successfully applied to deal with various problems with many different types of solution encodings. Although clustered tree problems play an important role in real life, there haven't been much research on exploiting the strengths of MFEA to solve these problems. One of the challenges in applying the MFEA is to build specific evolutionary operators of the MFEA algorithm. To exploit the advantages of the Cayley Codes in improving the MFEA's performance, this paper introduces MFEA with representation scheme based on the Cayley Code to deal with the clustered tree problems. The new evolutionary operators in MFEA have two different levels. The purpose of the first level is to construct a spanning tree which connects to a vertex in each cluster, while the objective of the second one is to determine the spanning tree for each cluster. We focus on evaluating the efficiency of the new MFEA algorithm on known Cayley Codes when solving clustered tree problems. In the aspect of the execution time and the quality of the solutions found, each encoding type of the Cayley Codes is analyzed when performed on both single-task and multi-task to find the solutions of one or two different clustered tree problems respectively. In addition, we also evaluate the effect of those encodings on the convergence speed of the algorithms. Experimental results show the level of effectiveness for each encoding type and prove that the Dandelion Code outperforms the remaining encoding mechanisms when solving clustered tree problems.

关键词： multifactorial evolutionary algorithm Clustered tree problems evolutionary algorithms Cayley code Prufer code Dandelion code Blob code

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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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evolutionary algorithm and multifactorial evolutionary algorithm on clustered shortest-path tree problem

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INFORMATION SCIENCES 2021年 553卷 280-304页

作者： Phan Thi Hong Hanh Pham Dinh Thanh Huynh Thi Thanh Binh Quantitat Hedge Fund Singapore Singapore Taybac Univ Fac Nat Sci Technol Son La Vietnam Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

In literature, Clustered Shortest-Path Tree Problem (CluSPT) is an NP-hard problem. Previous studies focus on approximation algorithms which search for an optimal solution in relatively large space. Thus, these algorithms consume a large amount of computational resources while the quality of obtained results is lower than expected. In order to enhance the performance of the search process, this paper proposes two different approaches which are inspired by two perspectives of analyzing the CluSPT. The first approach intuition is to narrow down the search space by reducing the original graph into a multi-graph with fewer nodes while maintaining the ability to find the optimal solution. The problem is then solved by a proposed evolutionary algorithm. This approach performs well on those datasets having small number of edges between clusters. However, the increase in the size of the datasets would cause the excessive redundant edges in multi-graph that pressurize searching for potential solutions. The second approach overcomes this limitation by breaking down the multi-graph into a set of simple graphs. Every graph in this set is corresponding to a mutually exclusive search space. From this point of view, the problem could be modeled into a bi-level optimization problem in which the search space includes two nested search spaces. Accordingly, the Nested Local Search evolutionary algorithm (N-LSEA) is introduced to search for the optimal solution of glscluspt, the upper level uses a simple Local Search algorithm while the lower level uses the Genetic algorithm. Due to the neighboring characteristics of the local search step in the upper level, the lower level reduced graphs share the common traits among each others. Thus, the Multi-tasking Local Search evolutionary algorithm (MLSEA) is proposed to take advantages of these underlying commonalities by exploiting the implicit transfer across similar tasks of multi-tasking schemes. The improvement in experimental results ove

关键词： Bi-level Optimization Clustered Shortest-Path Tree Problem evolutionary algorithms multifactorial evolutionary algorithm

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Parameter adaptation in multifactorial evolutionary algorithm for many-task optimization

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MEMETIC COMPUTING 2021年第4期13卷 433-446页

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

The advent of multifactorial optimization (MFO) has made a wind of change in intelligence computation in general and specifically in evolutionary computing. Based on the implicit parallelism of population-based search, MFO optimizes different problems simultaneously and entirely. However, the randomness of knowledge transfers raises the question of how to diminish harmful interactions among tasks for more effective transfers. In recent years, many proposals have been devised to handle this paradigm and improve existing algorithms. Notwithstanding the diversity in their concept, there are few efforts to solve many-task optimization (MaTO) that contains beyond three tasks. In light of this reason, this paper proposes two algorithms named SA-MFEA and LSA-MFEA for MaTO. Instead of utilizing fixed parameters, SA-MFEA and LSA-MFEA adapt the probability of random mating parameter to reduce negative transfers based on the historical memory of successful rmp. Besides, LSA-MFEA is capable of enhancing the exploitation by linear population size reduction. To examine the efficiency of the two proposed algorithms, experiments on various many-task benchmark problems and comparison with several state-of-the-art algorithms have been conducted. The results demonstrated that SA-MFEA and LSA-MFEA are competitive in terms of quality of solutions, convergence trend, and computation time.

关键词： evolutionary multi-task optimization multifactorial evolutionary algorithm Parameters adaptation strategy Many-task optimization

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Helper objective-based multifactorial evolutionary algorithm for continuous optimization

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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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multifactorial evolutionary algorithm for Inter-Domain Path Computation under Domain Uniqueness Constraint

Multifactorial Evolutionary Algorithm for Inter-Domain Path ...

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

作者： Huynh Thi Thanh Binh Ta Bao Thangy Nguyen Binh Long Ngo Viet Hoang Pham Dinh Thanh Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam Tay Bac Univ Fac Nat Sci & Technol Son La Vietnam

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169293

Nowadays, connectivity among communication devices in networks has been playing a significant role, especially when the number of devices is increasing dramatically that requires network service providers to have a better architecture of management system. One of the popular approach is to divide those devices inside a network into different domains, in which the problem of minimizing path computation in general or Inter-Domain Path Computation under Domain Uniqueness constraint (IDPC-DU) problem in specific has received much attention from the research community. Since the IDPC-DU is NP-complete, an approximate approach is usually taken to tackle this problem when the dimensionality is high. Although multifactorial evolutionary algorithm (MFEA) has emerged as an effective approximation algorithm to deal with various fields of problems, there are still some difficulties to apply directly MFEA to solve the IDPC-DU problem, i.e. different chromosomes may have different numbers of genes or to construct a feasible solution not violating the problem's constraint. Therefore, to overcome these limitations, MFEA algorithm with a new solution representation based on Priority-based Encoding is introduced. With the new representation of the solution, a chromosome consists of two parts: the first part encodes the priority of the vertex while the second part encodes information of edges in the solution. Besides, the paper also proposed a corresponding decoding method as well as novel crossover and mutation operators. Those evolutionary operators always produce valid solutions. For examining the efficiency of the proposed MFEA, experiments on a wide range of test sets of instances were implemented and the results pointed out the effectiveness of the proposed algorithm. Finally, the characteristics of the proposed algorithm are also indicated and carefully analyzed.

关键词： multifactorial evolutionary algorithm Inter-Domain Path Computation under Domain Uniqueness constraint Path Computation Element evolutionary algorithm

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A multifactorial evolutionary algorithm For Minimum Energy Cost Data Aggregation Tree In Wireless Sensor Networks

A Multifactorial Evolutionary Algorithm For Minimum Energy C...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Tran Cong Dao Tran Huy Hung Nguyen Thi Tam Huynh Thi Thanh Binh Hanoi Univ Sci & Technol Hanoi Vietnam VNU Univ Sci Hanoi Vietnam

ISBN: (纸本)9781728183923

In wireless sensor networks, the majority of data transmitted by sensor nodes is repeated over and over, and performing processes on them in many cases leads to increased power consumption and reduced network lifetime. Data aggregation is one of the techniques in reducing redundancy and improving energy efficiency;it also increases the lifespan of wireless sensor networks. In this paper, we address the issues of constructing the data aggregation tree that minimizes the total energy cost of data transmissions for two types of networks: without relay nodes and using relay nodes. Traditionally, evolutionary algorithms focus on constructing data aggregation trees for either without relay node networks or using relay nodes networks. Therefore, we propose Potential individuals based Multi-factorial evolutionary algorithm (P-MFEA) to solve both issues simultaneously. The proposed scheme shows improved performance in terms of energy consumption.

关键词： data aggregation tree network lifetime energy efficiency multifactorial evolutionary algorithm

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A hybrid multifactorial evolutionary algorithm and firefly algorithm for the clustered minimum routing cost tree problem

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KNOWLEDGE-BASED SYSTEMS 2022年 241卷 108225-108225页

作者： Ta Bao Thang Huynh Thi Thanh Binh Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

The clustered minimum routing cost tree (CluMRCT) problem is a recent problem with a wide range of real-life applications, especially in designing computer networks with peer-to-peer architecture. Many multifactorial evolutionary algorithms have been proposed to solve multiple CluMRCT problems simultaneously. However, these algorithms only function effectively on complete graphs with smalland-medium sizes. Moreover, the blindness and randomness in the transfer of genetic materials cause a reduction in the exploitation ability and make these algorithms ineffective to solve low-similarity tasks. This paper proposes a hybrid multitasking algorithm named multifactorial firefly algorithm, which integrates the firefly algorithm's strong exploitation ability to enhance the self-evolution of each task when facing low-similarity tasks while improving inter-task knowledge transfers by delivering higherquality solutions. Also, the proposed algorithm is equipped with new encoding and decoding to focus more on potential search areas on both complete and sparse graphs. The experiments and Wilcoxon signed-rank tests were conducted on various instances to verify our proposal with several state-of-theart methods. The results portrayed that the proposed encoding scheme helped multitasking algorithms improve solution quality by 32% on average. Besides, the statistical test values proved the superiority of the proposed hybrid algorithm in terms of solution quality and convergence trend. (c) 2022 Elsevier B.V. All rights reserved.

关键词： multifactorial evolutionary algorithm Firefly algorithm Hybrid algorithm Minimum routing cost Clustered Tree Problem

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multifactorial evolutionary algorithm For Clustered Minimum Routing Cost Problem 19

Multifactorial Evolutionary Algorithm For Clustered Minimum ...

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10th International Symposium on Information and Communication Technology (SoICT)

作者： Tran Ba Trung Le Tien Thanh Ly Trung Hieu Pham Dinh Thanh Huynh Thi Thanh Binh Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam Tay Bac Univ Fac Nat Sci & Technol Sonla Vietnam

ISBN: (纸本)9781450372459

Minimum Routing Cost Clustered Tree Problem (C1uMRCT) is applied in various fields in both theory and application. Because the C1uMRCT is NP-Hard, the approximate approaches are suitable to find the solution for this problem. Recently, multifactorial evolutionary algorithm (MFEA) has emerged as one of the most efficient approximation algorithms to deal with many different kinds of problems. Therefore, this paper studies to apply MFEA for solving C1uMRCT problems. In the proposed MFEA, we focus on crossover and mutation operators which create a valid solution of C1uMRCT problem in two levels: first level constructs spanning trees for graphs in clusters while the second level builds a spanning tree for connecting among clusters. To reduce the consuming resources, we will also introduce a new method of calculating the cost of C1uMRCT solution. The proposed algorithm is experimented on numerous types of datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, partially on large instances.

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

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