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M-E-AWA: A Novel Task Scheduling Approach Based on Weight Vector Adaptive Updating for Fog Computing

PROCESSES 2023年第4期11卷 1053页

作者： Dai, Zhiming Ding, Weichao Min, Qi Gu, Chunhua Yao, Baohua Shen, Xiaohan East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China Shanghai Jian Qiao Univ Sch Informat Technol Shanghai 201306 Peoples R China Shanghai Inst Civil Def Sci Shanghai 200020 Peoples R China

Task offloading and real-time scheduling are hot topics in fog computing. This paper aims to address the challenges of complex modeling and solving multi-objective task scheduling in fog computing environments caused by widely distributed resources and strong load uncertainties. Firstly, a task unloading model based on dynamic priority adjustment is proposed. Secondly, a multi-objective optimization model is constructed for task scheduling based on the task unloading model, which optimizes time delay and energy consumption. The experimental results show that M-E-AWA (MOEA/D with adaptive weight adjustment based on external archives) can effectively handle multi-objective optimization problems with complex Pareto fronts and reduce the response time and energy consumption costs of task scheduling.

关键词： fog computing task scheduling multi-objective evolutionary algorithm MOEA D

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Optimum Charge Plan of Steelmaking Continuous Casting Based on the Modified multi-objective evolutionary algorithm

Optimum Charge Plan of Steelmaking Continuous Casting Based ...

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4th International Conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Song, Hong Xi Zhang, Jian Xin Gao, Kun Meng, Wei Cheng, Li Inner Mongolia Univ Technol Sch Elect Power Hohhot 010080 Inner Mongolia Peoples R China

ISBN: (纸本)9783037856932

The charge plan is a basic problem of the metallurgical industry Through a comprehensive consideration of the process constraints and production costs, charge plan is a typical multi-objective optimization problem. A multi-objective optimization model of charge plan was built in this paper. As direct encoding is hard to be used in the many constraints problem case, a new method of generate feasible solutions based on constraint satisfaction was proposed. A novel adaptive grid algorithm was also proposed to improve the current multi-objective evolutionary algorithm's population maintain strategy. Finally, the proposed multi-objective charge plan model was solved, and more meaningful solutions for this problem were obtained.

关键词： Steelmaking and continuous casting Charge plan Adaptive grid Maintain strategy constraint satisfaction multi-objective evolutionary algorithm

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Ensemble summarization of bio-medical articles integrating clustering and multi-objective evolutionary algorithms

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APPLIED SOFT COMPUTING 2021年 106卷 107347-107347页

作者： Mallick, Chirantana Das, Asit Kumar Ding, Weiping Nayak, Janmenjoy Indian Inst Engn Sci & Technol Sibpur 711103 Howrah India Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Aditya Inst Technol & Management AITAM Tekkali 532201 Andhra Pradesh India

The rapid exponential growth of scientific literature of bio-medicine, genomics, and other bio-sciences increases difficulties to access useful information timely and efficiently. The medical researchers, physicians and clinicians need to extract the gist of the novel articles effectively to gather knowledge and apply it properly for better treatment of the patients. In the paper, the bio-medical articles available in an open access source, PubMed MEDLINE, are considered for generating their extractive summaries using clustering and multi-objective evolutionary algorithm based ensemble summarization technique. Initially, we have extracted only the medical terms of an article and determined the concepts of all medical terms using Unified Medical Language System (UMLS) to represent the article in terms of a set of concepts. Next, extractive summaries of the article have been generated using various popularly used clustering algorithms and different centrality measures. The generated summaries are referred to as the base summaries of the article. Finally, multi-objective evolutionary algorithm is applied on these base summaries for generating an ensemble summary of the given article. The evolutionary algorithm uses two competent objective functions to measure the fitness value of each chromosome in the population. One objective function is the linear combination of jaccard similarity and Word2Vec similarity between the original article and the ensemble summary generated by the chromosome. The other objective function is defined as the information gain of the chromosome about the article by generating the ensemble summary. The best pareto optimal solution of the final population of the evolutionary algorithm provides the ensemble summary of the given article. The proposed ensemble method is compared with some state-of-art methods to demonstrate that it is both effective and statistically significant. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Extractive ensemble summarization Natural language processing multi-objective evolutionary algorithm Centrality measures Clustering Bio-medical informatics

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Integrated optimization of real-time order acceptance and flexible job-shop rescheduling with multi-level imperfect maintenance constraints

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SWARM AND evolutionary COMPUTATION 2023年 77卷

作者： An, Youjun Chen, Xiaohui Gao, Kaizhou Zhang, Lin Li, Yinghe Zhao, Ziye Chongqing Univ State Key Lab Mech Transmiss Chongqing 400030 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Collaborat Lab Intelligent Sci & Syst Macau 999078 Peoples R China

Nowadays, the make-to-order (MTO) production has gradually become a new manufacturing trend, and the real-time arrival of orders has brought great challenges to order acceptance, production scheduling and maintenance planning in the actual production. Therefore, in this study, we focus on the integrated optimization of real-time order acceptance and flexible job-shop rescheduling with multi-level imperfect maintenance constraints. More precisely, (1) a multi-level imperfect maintenance model with minimal repair (MR), preventive maintenance (PM), overhaul maintenance (OM) and replacement is designed for each production machine, and the optimality is derived;(2) an integrated multi-objective optimization model is developed to characterize the concerned problem;and (3) an improved non-dominated sorting genetic algorithm III with adaptive reference vector (INSGA-III/ARV) is proposed to deal with the problem. In the numerical simulation, the effect of different order sorting rules is first analyzed. Second, the effectiveness of improved operators is demonstrated by internal analysis of the proposed algorithm. Third, the superiority and competitiveness of the proposed algorithm is verified by comparing with the variants of five state-of-the-art algorithms. Fourth, the benefits of both the designed multi-level imperfect maintenance model and real-time order acceptance and scheduling (ROAS) strategy are proved by contrasting with other two traditional maintenance models and all order acceptance and scheduling (AOAS) strategy, respectively. Finally, a comprehensive sensitivity analysis is performed to illustrate the impact of several key parameters on the integrated optimization model.

关键词： Real-time order acceptance Flexible job-shop rescheduling multi-level imperfect maintenance multi-objective evolutionary algorithm (MOEA)

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Optimal site and size of FACTS devices with the integration of uncertain wind generation on a solution of stochastic multi-objective optimal power flow problem

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Hafeez, Abdul Ali, Aamir Keerio, M. U. Mugheri, Noor Hussain Abbas, Ghulam Khan, Aamir Mirsaeidi, Sohrab Yousef, Amr Touti, Ezzeddine Bouzguenda, Mounir Quaid e Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah Sindh Pakistan Southeast Univ Sch Elect Engn Nanjing Peoples R China Beijing Jiaotong Univ Sch Elect Engn Beijing Peoples R China Univ Business & Technol Dept Elect Engn Jeddah 21432 Saudi Arabia Alexandria Univ Fac Engn Engn Math Dept Alexandria Egypt Northern Border Univ Coll Engn Dept Elect Engn Ar Ar Saudi Arabia King Faisal Univ Dept Elect Engn Al Hasa Saudi Arabia

To reduce the Carbon footprint and reduce emissions from the globe, the world has kicked-off to leave reliance of fossil fuels and generate electrical energy from renewable energy sources. The MOOPF problem is becoming more complex, and the number of decision variables is increasing, with the introduction of power electronics-based Flexible AC Transmission Systems (FACTS) devices. These power system components can all be used to increase controllability, effectiveness, stability, and sustainability. The added uncertainty and variability that FACTS devices and wind generation provide to the power system makes it challenging to find the right solution to MOOPF issues. In order to determine the best combination of control and state variables for the MOOPF problem, this paper develops three cases of competing objective functions. These cases include minimizing the total cost of power produced as well as over- and underestimating the cost of wind generation, emission rate, and the cost of power loss caused by transmission lines. In the case studies, power system optimization is done while dealing with both fixed and variable load scenarios. The proposed algorithm was tested on three different cases with different objective functions. The algorithm achieved an expected cost of $833.014/h and an emission rate of conventional thermal generators of 0.665 t/h in the case 1. In Case 2, the algorithm obtained a minimum cost of $731.419/h for active power generation and a cost of power loss is 124.498 $/h for energy loss. In Case 3, three objective functions were minimized simultaneously, leading to costs of $806.6/h for emissions, 0.647 t/h, and $214.9/h for power loss.

关键词： constraint handling technique FACTS devices multi-objective evolutionary algorithm optimal power flow wind generation

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Self-adaptive polynomial mutation in NSGA-II

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SOFT COMPUTING 2023年第23期27卷 17711-17727页

作者： Carles-Bou, Jose L. Galan, Severino F. Univ Nacl Educ Distancia UNED Escuela Int Doctorado EIDUNED Madrid Spain UNED Dept Artificial Intelligence Madrid Spain

evolutionary multi-objective optimization is a field that has experienced a rapid growth in the last two decades. Although an important number of new multi-objective evolutionary algorithms have been designed and implemented by the scientific community, the popular Non-Dominated Sorting Genetic algorithm (NSGA-II) remains as a widely used baseline for algorithm performance comparison purposes and applied to different engineering problems. Since every evolutionary algorithm needs several parameters to be set up in order to operate, parameter control constitutes a crucial task for obtaining an effective and efficient performance in its execution. However, despite the advancements in parameter control for evolutionary algorithms, NSGA-II has been mainly used in the literature with fine-tuned static parameters. This paper introduces a novel and computationally lightweight self-adaptation mechanism for controlling the distribution index parameter of the polynomial mutation operator usually employed by NSGA-II in particular and by multi-objective evolutionary algorithms in general. Additionally, the classical NSGA-II using polynomial mutation with a static distribution index is compared with this new version utilizing a self-adapted parameter. The experiments carried out over twenty-five benchmark problems show that the proposed modified NSGA-II with a self-adaptive mutator outperforms its static counterpart in more than 75% of the problems using three quality metrics (hypervolume, generalized spread, and modified inverted generational distance).

关键词： multi-objective evolutionary algorithm NSGA-II Polynomial mutation Distribution index self-adaptation

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Modeling and Control of a Peltier Thermoelectric System Applying a multi-objective Optimization Approach 12th

Modeling and Control of a Peltier Thermoelectric System Appl...

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12th Ecuadorian Conference on Information and Communication Technologies

作者： Huilcapi, Victor Garcia, Geovanny Ghia, Elias Soto, Brian Univ Politecn Salesiana Guayaquil Ecuador

ISBN: (纸本)9783031754302;9783031754319

This paper proposes a design procedure based on multi-objective optimization for modeling a Peltier thermoelectric system using experimental data. A multi-objective evolutionary algorithm was used to identify a set of optimal parameters that satisfactorily characterize the system's dynamics. The proposed methodology offers a designer valuable information about the dynamics of the temperatures of the Peltier's hot and cold surfaces and the trade-offs between their design objectives (visualized in the Pareto fronts). In this way, a control engineer can be sufficiently informed to choose, according to their preferences, a model for the Peltier cell with the best performance (for different system operation scenarios). A Peltier cold-side temperature model was selected to tune a proportional-integral-derivative (PID) controller and evaluate the robustness of the model. The tuned PID controller works directly on the nonlinear model of the Peltier cell, allowing it to effectively control the temperature of the cold surface of the thermoelectric module. The methodology uses the Integral Absolute Error (IAE) as a performance index to evaluate the quality of system modeling. The results show that the methodological approach applied to model and control the system performs very satisfactorily.

关键词： Peltier cell multi-objective optimization Pareto front multi-objective evolutionary algorithm Nonlinear model PID control

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multi-objective Optimization for Bandwidth-Limited Federated Learning in Wireless Edge Systems

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2023年 4卷 954-966页

作者： Zhou, Yu Liu, Xuemei Lei, Lei Cent South Univ Sch Automat Changsha 410083 Peoples R China Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

This paper studies a bandwidth-limited federated learning (FL) system where the access point is a central server for aggregation and the energy-constrained user equipments (UEs) with limited computation capabilities (e.g., Internet of Things devices) perform local training. Limited by the bandwidth in wireless edge systems, only a part of UEs can participate in each FL training round. Selecting different UEs could affect the FL performance, and selected UEs need to allocate their computing resource effectively. In wireless edge FL systems, simultaneously accelerating FL training and reducing computing-communication energy consumption are of importance. To this end, we formulate a multi-objective optimization problem (MOP). In MOP, the model training convergence is difficult to calculate accurately. Meanwhile, MOP is a combinatorial optimization problem, with the high-dimension mix-integer variables, which is proved to be NP-hard. To address these challenges, a multi-objective evolutionary algorithm for the bandwidth-limited FL system (MOEA-FL) is proposed to obtain a Pareto optimal solution set. In MOEA-FL, an age-of-update-loss method is first proposed to transform the original global loss function into a convergence reference function. Then, MOEA-FL divides MOP into $N$ single objective subproblems by the Tchebycheff approach and optimizes the subproblems simultaneously by evolving a population. Extensive experiments have been carried out on MNIST dataset and a medical case called TissueMNIST dataset for both the i.i.d and non-i.i.d data setting. Experimental results demonstrate that MOEA-FL performs better than other algorithms and verify the robustness and scalability of MOEA-FL.

关键词： Training Convergence Energy consumption Optimization Wireless communication Computational modeling Bandwidth Federated learning multi-objective evolutionary algorithm combinatorial optimization differential evolution decomposition

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Edge-Rotated Cone Orders in multi-objective evolutionary algorithms for Improved Convergence and Preference Articulation

Edge-Rotated Cone Orders in Multi-objective Evolutionary Alg...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Wang, Yali Deutz, Andre Back, Thomas Emmerich, Michael Leiden Univ LIACS Leiden Netherlands

ISBN: (纸本)9781728125473

The Pareto dominance relation is a special case of a cone order. Cone orders and the Pareto order are translation invariant and also multiplication invariant by any positive real. Employment of more general cone orders instead of the Pareto order gives rise to interesting exploration opportunities for algorithm design. In this paper, the standard Pareto dominance relation has been extended to cone dominance with a pointed convex obtuse cone, a superset of the Pareto dominance cone, i.e., the non-negative orthant, by rotating the edges of the Pareto order cone. The basic idea is in line with earlier work on coneorders in multicriteria decision nuking and here, in particular, the cone-order is used (1) to increase solutions' dominance area and hence improve the convergence speed of the algorithm and (2) to formulate trade-off constraints. The minima of the obtuse cone order are also Pareto optimal. However. not all minima of the Pardo order are also minima of the obtuse cone order. Therefore we use the edge-rotated cone in an alternating manner with the standard Pareto cone to guarantee coverage of the entire Pareto front. The edge-rotated cones have been integrated in several state-of-the-art multi-objective evolutionary algorithms (MOEAs) and proven to be able to improve the performance of MOEAs on multi-objective optimization problems with the linear, concave, convex and disconnected Pareto front. Furthermore, when the edges of the cone are rotated by different angles, the algorithm obtains solution sets located in different areas of the Pareto front. This behavior can be used to take the preference of the targeted region on the Pareto front into the algorithm. In particular, by using obtuse cones, regions where trade-off is very unbalanced can be discarded. This is quantified by showing a relationship between the angle and the trade-off rate corresponding to this angle.

关键词： Cone order Pareto domination multi-objective evolutionary algorithm preference-based optimization algorithm

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multi-objective evolutionary Ensemble Pruning Guided by Margin Distribution 17th

Multi-objective Evolutionary Ensemble Pruning Guided by Marg...

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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Wu, Yu-Chang He, Yi-Xiao Qian, Chao Zhou, Zhi-Hua Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

ISBN: (纸本)9783031147142;9783031147135

Ensemble learning trains and combines multiple base learners for a single learning task, and has been among the state-of-the-art learning techniques. Ensemble pruning tries to select a subset of base learners instead of combining them all, with the aim of achieving a better generalization performance as well as a smaller ensemble size. Previous methods often use the validation error to estimate the generalization performance during optimization, while recent theoretical studies have disclosed that margin distribution is also crucial for better generalization. Inspired by this finding, we propose to formulate ensemble pruning as a three-objective optimization problem that optimizes the validation error, margin distribution, and ensemble size simultaneously, and then employ multi-objective evolutionary algorithms to solve it. Experimental results on 20 binary classification data sets show that our proposed method outperforms the state-of-the-art ensemble pruning methods significantly in both generalization performance and ensemble size.

关键词： Machine learning Ensemble pruning multi-objective optimization Margin distribution multi-objective evolutionary algorithm

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