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Distributed robust multitask clustering in wireless sensor networks using multi-factorial evolutionary algorithm

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2025年 198卷

作者： Panwar, Anita Nanda, Satyasai Jagannath Malaviya Natl Inst Technol Dept Elect & Commun Engn Jaipur 302017 Rajasthan India

When data collected at the local nodes of a wireless sensor network (WSN) are volumetric in nature, there is a need for local processing, then distributed clustering plays an important role. Traditional clustering algorithms based on K-means, K-medoid are not effective in these scenarios for accurate data segregation. Further, there is a requirement of techniques that can effectively handle outliers and noise present in the sensed data. Thus, there is a need to design robust distributed data clustering algorithms. multi-Task Optimization (MTO) has taken the attention of researchers in the last couple of years after the introduction of multi-factorial evolutionary algorithm (MFEA). The MFEA can handle several single objective tasks usually related to one another and share implicit knowledge or abilities common to them. In this manuscript, the MFEA is employed to solve two tasks: 1) outlier detection and 2) perform distributed clustering at the nodes of WSN. The resultant algorithm, termed as Distributed MFEA (DMFEA), effectively removes noise and segregates data present at multiple nodes of WSN. Simulation study reveals the superior performance of DMFEA over benchmark algorithms like distributed versions of K-means, particle swarm optimization, and moth-flame optimization on two synthetic and six real- life datasets based on forest fire monitoring, air pollution indexing, Intel laboratory environment sensing, agriculture soil quality labeling, river water quality analysis, and land mine detection. The superior performance of DMFEA is demonstrated based on the Silhouette Index of obtained clusters and the percentage of outliers detected. Additionally, the DMFEA average rank in Kruskal Wallis test, is better over the three comparative algorithms.

关键词： multitask optimization multi-factorial evolutionary algorithm Distributed clustering Outliers detection Normality test

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multi-factorial evolutionary algorithm based novel solution approach for multi-objective pollution-routing problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 130卷 757-771页

作者： Rauniyar, Amit Nath, Rahul Muhuri, Pranab K. South Asian Univ Dept Comp Sci New Delhi 110021 India

The rapid increase in transportation has led to alarming levels of pollution globally, which has, in turn adverse effects on both the environment and the health of people. This has motivated researchers to develop efficient solutions for limiting the fuel consumption of vehicles so that greenhouse gas emission can be reduced. The pollution emitted by a vehicle depends primarily on two controllable factors viz. load and distance traveled. This paper considers a Pollution-Routing Problem (PRP) formulation with two objectives, minimization of fuel consumption (CO2 emissions), and minimization of total distance to be traversed, and proposes a novel solution based on the well-known Non-Dominated Sorting Genetic algorithm-II (NSGA-II). Since the problem requires optimization of several routes formed at the same time, traditional NSGA-II frameworks are incapable of handling it efficiently. Thus, we incorporate a new paradigm of evolutionary algorithm, called multi-factorial optimization into NSGA-II to solve the problem of several routes generated simultaneously. The results of our experiments with benchmark datasets confirm the feasibility of the proposed approach with better solutions and faster convergence.

关键词： multi-factorial evolutionary algorithm multi-objective optimization Pollution routing problem NSGA-II, SPEA2

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multi-Objective multi-factorial evolutionary algorithm for Container Placement

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2023年第2期11卷 1430-1445页

作者： Liu, Ruochen Yang, Ping Lv, Haoyuan Li, Weibin Xidian Univ Minist Educ Key Lab Intelligent Percept Image Understanding Xian 710071 Peoples R China

The use of containerization technology in microservice architecture has become widespread, owing to its potential to support fast deployment of web applications and improve the resource utilization in cloud data centers. evolutionary algorithms (EAs) have been performed promising on the deployment of applications created using microservices. However, with the growing demand for microservice application, the existing EAs fail to solve the large-scale container placement problem due to the high time complexity and poor scalability. A multi-factorial evolutionary algorithm (MFEA) is proposed in this article, which can evolve multiple optimization problems simultaneously for the container placement problem in heterogeneous cluster environments. First, a system model integrated the heterogeneous clusters, microservices, containers, and four optimization objectives is presented. Then, embedded with local search strategy, a multi-objective container placement MFEA (MOCP-MFEA) algorithm is developed to address the container placement problem. MOCP-MFEA is applied to a variety of container placement problems with different application sizes in heterogeneous cluster environments. Experimental results show that compared with various conventional and evolutionary-based approaches, MOCP-MFEA could shorten optimization time significantly and offer a competitive placement solution for the container placement problem and show a good elasticity in heterogeneous cluster environments. Moreover, the deployment scheme of container to physical machines is crucial to lowering resources wastage.

关键词： Container placement heterogeneous cluster resources wastage multi-factorial evolutionary algorithm

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Rigorous Analysis of multi-factorial evolutionary algorithm as multi-Population Evolution Model

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第2期12卷 1121-1133页

作者： Wang, Na Xu, Qingzheng Fei, Rong Yang, Jungang Wang, Lei Natl Univ Def Technol Coll Informat & Commun 8 Zhangba East Rd Xian 710106 Peoples R China Xian Univ Technol Sch Comp Sci & Engn 5 Jinhua North Rd Xian 710048 Peoples R China

multi-task optimization algorithm is an emergent paradigm which solves multiple self-contained tasks simultaneously. It is thought that multi-factorial evolutionary algorithm (MFEA) can be seen as a novel multi-population algorithm, wherein each population is represented independently and evolved for the selected task only. However, the theoretical and experimental evidence to this conclusion is not very convincing and especially, the coincidence relation between MFEA and multi-population evolution model is ambiguous and inaccurate. This paper aims to make an in-depth analysis of this relationship, and to provide more theoretical and experimental evidence to support the idea. In this paper, we clarify several key issues unsettled to date, and design a novel across-population crossover approach to avoid population drift. Then MFEA and its variation are reviewed carefully in view of multi-population evolution model, and the coincidence relation between them are concluded. MFEA is completely recoded along with this idea and tested on 25 multi-task optimization problems. Experimental results illustrate its rationality and superiority. Furthermore, we analyze the contribution of each population to algorithm performance, which can help us design more efficient multi-population algorithm for multi-task optimization. (C) 2019 The Authors. Published by Atlantis Press SARL.

关键词： multi-factorial evolutionary algorithm multi-population evolution model multi-task optimization Knowledge transfer Across-population

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A new transfer evolutionary multi-task optimization algorithm for bi-level optimal configuration of distributed generations and energy storage systems considering uncertainties

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JOURNAL OF ENERGY STORAGE 2025年 113卷

作者： Wang, Chen Jiao, Shangbin Zhang, Youmin Li, Yujun Li, Yuxing Zhang, Qing Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China Xian Univ Technol Shaanxi Key Lab Complex Syst Control & Intelligen Xian 710048 Peoples R China Concordia Univ Diag Flight Control & Simulat DFCS Lab Montreal PQ H3G 1M8 Canada

Large-scale Distributed Generation (DG) is integrated into the Active Distribution Networks (ADNs). Due to its limited regulatory capabilities, this integration can result in issues such as poor resilience, imbalanced supply and demand, and increased operational and maintenance costs. This paper develops a multi-scenario framework for the optimal configuration analysis of DG and Energy Storage System (ESSs), employing a bi-level approach based on multi-task optimization. Additionally, it designs a transfer learning-assisted multi-factorial evolutionary algorithm (MFEA) to serve as the optimizer for the framework, enabling the simultaneous resolution of multiple optimization tasks of outer model. The bi-level model includes an inner model that comprehensively evaluates the vulnerability and operational costs, while the outer model focuses on minimizing the annual comprehensive cost. Secondly, the integration of transfer discriminant subspace learning and manifold regularization enhances the ability of the MFEA to extract useful information from the population during the iterative process. This improvement significantly boosts the algorithm's optimization capability across multiple scenarios. Elitismbased differential evolution, elitism-based opposition-based learning, and elitism-based chaotic perturbation mutation strategies are designed to bolster the performance of the proposed algorithm under small population size. To enhance the accuracy of Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) in generating diverse scenario data, the Temporal Convolutional Network-Attention network serves as the neural architecture for WGAN-GP, thereby improving its capability to capture the characteristics of photovoltaic and load in the generation of source-load scenarios. The empirical validation of the proposed algorithm on the modified 33-bus system underscores its superior accuracy and robustness when compared to contemporary algorithms. Furthermore, r

关键词： Transfer learning multi-factorial evolutionary algorithm Generative adversarial network Scenario generation Bi-level optimization Optimal configuration

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multi-Objective Cloud Task Scheduling Optimization Based on evolutionary multi-Factor algorithm

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2023年第4期11卷 3685-3699页

作者： Cui, Zhihua Zhao, Tianhao Wu, Linjie Qin, A. K. Li, Jianwei Taiyuan Univ Sci & Technol Coll Comp Sci Taiyuan 030024 Shanxi Peoples R China Swinburne Univ Technol Dept Comp Technol Hawthorn Vic 3122 Australia

Cloud platforms scheduling resources based on the demand of the tasks submitted by the users, is critical to the cloud provider's interest and customer satisfaction. In this paper, we propose a multi-objective cloud task scheduling algorithm based on an evolutionary multi-factorial optimization algorithm. First, we choose execution time, execution cost, and virtual machines load balancing as the objective functions to construct a multi-objective cloud task scheduling model. Second, the multi-factor optimization (MFO) technique is applied to the task scheduling problem, and the task scheduling characteristics are combined with the multi-objective multi-factor optimization (MO-MFO) algorithm to construct an assisted optimization task. Finally, a dynamic adaptive transfer strategy is designed to determine the similarity between tasks according to the degree of overlap of the MFO problem and to control the intensity of knowledge transfer. The results of simulation experiments on the cloud task test dataset show that our method significantly improves scheduling efficiency, compared with other evolutionary algorithms (EAs), the scheduling method simplifies the decomposition of complex problems by a multi-factor approach, while using knowledge transfer to share the convergence direction among sub-populations, which can find the optimal solution interval more quickly and achieve the best results among all objective functions.

关键词： Task analysis Cloud computing Optimization Virtual machining Costs Linear programming Job shop scheduling Adaptive strategy cloud computing multi-factorial evolutionary algorithm optimization task scheduling

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Helper Objective Assisted evolutionary algorithm for multi-modal Optimization

Helper Objective Assisted Evolutionary Algorithm for Multi-m...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Yang, Xu Wang, Rui Li, Wenhua Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha Peoples R China

ISBN: (纸本)9781728183923

evolutionary algorithms (EAs) have been widely applied in various optimization problems. However, EAs are found as less effective on multi-modal optimization due to their multiple local optima. Inspired by the idea of self-paced learning, that is, problems can be solved step by step, from easy to difficult. In this study, we propose to design a helper objective function to assist the optimization of multi-modal problems via multi-task optimization framework. In principle the helper objective function shares common features with the original function but is easier to solve. Thus, the information gained by solving the helper objective can be utilized to tackle the multi-modal problems. Specifically, the Gaussian process is applied to build the helper objective function. The multi-factorial evolutionary algorithm is applied to optimize the helper and original objective functions simultaneously. Experimental results show that the idea is effective on a set of multi-modal optimization benchmarks.

关键词： surrogate model helper objective multi-task optimization Gaussian process modeling multi-factorial evolutionary algorithm differential evolution

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