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A multi-objective evolutionary algorithm based on Pareto prediction for automatic test task scheduling problems

APPLIED SOFT COMPUTING 2018年 66卷 394-412页

作者： Lu, Hui Zhou, Rongrong Fei, Zongming Shi, Jinhua Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Univ Kentucky Dept Comp Sci Lexington KY 40506 USA

The test task scheduling problem (TTSP) is a combinatorial optimization problem still under investigation. A multi-objective evolutionary algorithm based on Pareto prediction (PP-MOEA) is proposed fully considering the characteristics of TTSP. In a multi-objective TTSP, multiple solutions in the decision space correspond to a point in the objective space and the number of true Pareto front in the objective space is relatively small. Moreover, there are less solutions distributing in either high or low objective values while most solutions distribute in average. Based on these characteristics, a pure elitism strategy and a novel scale-down non-dominated sorting (SDNS) method are combined to improve the efficiency and convergence of the algorithm. Additionally, an extended encoding range approach and a Pareto prediction strategy are proposed to help exploring new solutions and increase the diversity of population. The Pareto prediction strategy is implemented by following the historical evolutionary information of the current Pareto set and then predicting the next generation of several individuals. The makespan (maximal test completion time) and the mean workload of the instruments are considered in this research. Based on the general framework of MOEAs, the proposed algorithm is more effective and efficient in tackling multi-objective TTSPs. PP-MOEA has the better performance of convergence and diversity compared with other algorithms based on the statistical analysis of experiments results. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithms Scheduling theory Encoding mechanism Pareto prediction strategy Scale-down non-dominated sorting method

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multiobjective evolutionary algorithm with a discrete differential mutation operator developed for service restoration in distribution systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 62卷 700-711页

作者： Sanches, Danilo Sipoli London, Joao Bosco A., Jr. Delbem, Alexandre Claudio B. Prado, Ricardo S. Guimaraes, Frederico G. Neto, Oriane M. de Lima, Telma W. Fed Technol Univ Parana Cornelia Procopio Brazil Univ Sao Paulo Sao Carlos Sch Engn Sao Carlos SP Brazil Univ Sao Paulo Inst Math & Comp Sci Sao Carlos SP Brazil Fed Inst Minas Gerais Ouro Preto Brazil Univ Fed Minas Gerais Dept Elect Engn Belo Horizonte MG Brazil Univ Fed Goias Inst Informat Goiania Go Brazil

Network reconfiguration for service restoration in distribution systems is a combinatorial complex optimization problem that usually involves multiple non-linear constraints and objective functions. For large scale distribution systems no exact algorithm has found adequate restoration plans in real-time. On the other hand, the combination of multi-objective evolutionary algorithms (MOEAs) with the Node-Depth Encoding (NDE) has been able to efficiently generate adequate restoration plans for relatively large distribution systems (with thousands of buses and switches). The approach called MEAN results from the combination of NDE with a technique of MOEA based on subpopulation tables. In order to improve the capacity of MEAN to explore both the search and objective spaces, this paper proposes a new approach that results from the combination of MEAN with characteristics from the mutation operator of the Differential Evolution (DE) algorithm. Simulation results have shown that the proposed approach, called MEAN-DE, is able to find adequate restoration plans for distribution systems from 3860 to 30,880 switches. Comparisons have been performed using the Hypervolume metric and the Wilcoxon rank-sum test. In addition, a MOEA using subproblem Decomposition and NDE (MOEA/D-NDE) was investigated. MEAN-DE has shown the best average results in relation to MEAN and MOEA/D-NDE. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Large-Scale Distribution Systems Service restoration Node-Depth Encoding multi-objective evolutionary algorithms Differential Evolution

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multi-objective evolutionary Algorithm for single and multiple fault service restoration in large-scale distribution systems

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ELECTRIC POWER SYSTEMS RESEARCH 2014年第May期110卷 144-153页

作者： Sanches, Danilo Sipoli London Junior, Joao Bosco A. Delbem, Alexandre Claudio B. Fed Technol Univ Parana BR-86300000 Cornell Procopio PR Brazil Univ Sao Paulo Sao Carlos Sch Engn BR-13566590 Sao Carlos SP Brazil Univ Sao Paulo Inst Math & Comp Sci BR-13566590 Sao Carlos SP Brazil

Network reconfiguration for service restoration (SR) in distribution systems is a complex optimization problem. For large-scale distribution systems, it is computationally hard to find adequate SR plans in real time since the problem is combinatorial and non-linear, involving several constraints and objectives. Two multi-objective evolutionary algorithms that use Node-Depth Encoding (NDE) have proved able to efficiently generate adequate SR plans for large distribution systems: (i) one of them is the hybridization of the Non-Dominated Sorting Genetic Algorithm-II(NSGA-II) with NDE, named NSGA-N;(ii) the other is a multi-objective evolutionary Algorithm based on subpopulation tables that uses NDE, named MEAN. Further challenges are faced now, i.e. the design of SR plans for larger systems as good as those for relatively smaller ones and for multiple faults as good as those for one fault (single fault). In order to tackle both challenges, this paper proposes a method that results from the combination of NSGA-N, MEAN and a new heuristic. Such a heuristic focuses on the application of NDE operators to alarming network zones according to technical constraints. The method generates similar quality SR plans in distribution systems of significantly different sizes (from 3860 to 30,880 buses). Moreover, the number of switching operations required to implement the SR plans generated by the proposed method increases in a moderate way with the number of faults. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Large-scale distribution system Service restoration multiple faults Node-Depth Encoding multi-objective evolutionary algorithms

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evolutionary neural architecture search based on efficient CNN models population for image classification

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multiMEDIA TOOLS AND APPLICATIONS 2023年第16期82卷 23917-23943页

作者： Termritthikun, Chakkrit Jamtsho, Yeshi Muneesawang, Paisarn Zhao, Jia Lee, Ivan Naresuan Univ Sch Renewable Energy & Smart Grid Technol SGtech Phitsanulok 65000 Thailand Royal Univ Bhutan Coll Sci & Technol Phuentsholing 21101 Bhutan Naresuan Univ Fac Engn Dept Elect & Comp Engn Phitsanulok 65000 Thailand Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China Univ South Australia STEM Adelaide SA 5095 Australia

The aim of this work is to search for a Convolutional Neural Network (CNN) architecture that performs optimally across all factors, including accuracy, memory footprint, and computing time, suitable for mobile devices. Although deep learning has evolved for use on devices with minimal resources, its implementation is hampered by that these devices are not designed to tackle complex tasks, such as CNN architectures. To address this limitation, a Network Architecture Search (NAS) strategy is considered, which employs a multi-objective evolutionary Algorithm (MOEA) to create an efficient and robust CNN architecture by focusing on three objectives: fast processing times, reduced storage, and high accuracy. Furthermore, we proposed a new Efficient CNN Population Initialization (ECNN-PI) method that utilizes a combination of random and selected strong models to generate the first-generation population. To validate the proposed method, CNN models are trained using CIFAR-10, CIFAR-100, ImageNet, STL-10, FOOD-101, THFOOD-50, FGVC Aircraft, DTD, and Oxford-IIIT Pets benchmark datasets. The MOEA-Net algorithm outperformed other models on CIFAR-10, whereas MOEANet with the ECNN-PI method outperformed other models on CIFAR-10 and CIFAR-100. Furthermore, both the MOEA-Net algorithm and MOEA-Net with the ECNN-PI method outperformed DARTS, P-DARTS, and Relative-NAS for small-scale multi-class and fine-grained datasets.

关键词： Deep learning Neural architecture search multi-objective evolutionary algorithms Image classification

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An archive-based multi-objective evolutionary algorithm with adaptive search space partitioning to deal with expensive optimization problems: Application to process eco-design

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COMPUTERS & CHEMICAL ENGINEERING 2016年 87卷 95-110页

作者： Ahmadi, Aras Tiruta-Barna, Ligia Capitanescu, Florin Benetto, Enrico Marvuglia, Antonino Univ Toulouse INSA UPS INPLISBP 135 Ave Rangueil F-31077 Toulouse France INRA UMR792 Lab Ingn Syst Biol & Proc F-31400 Toulouse France CNRS UMR5504 F-31400 Toulouse France LIST Environm Res & Innovat ERIN Dept 41 Rue Brill L-4422 Belvaux Luxembourg

In eco-design, the integration of environmental aspects into the earliest stage of design is considered with the aim of reducing adverse environmental impacts throughout a product's life cycle. An eco-design problem is therefore multi-objective, where several objectives (environmental, economic, and technological) are to be simultaneously optimized. The optimization of industrial processes usually requires solving expensive multi-objective optimization problems (MOPs). Aiming to solve efficiently MOPs, with a limited computational budget, this paper proposes a new framework called AMOEA-MAP. The framework relies on the structure of the NSGAII algorithm and possesses two novel operators: a memory-based adaptive partitioning strategy, which provides an adaptive reticulation of the search space for a quick identification of optimal zones with less computational effort;and a bi-population evolutionary algorithm, tailored for expensive optimization problems. To ascertain its generality, the framework is first tested on several tough benchmarks. Its performance is subsequently validated on a real-world eco-design problem. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Expensive simulation-based multi-objective optimization multi-objective evolutionary algorithms Convergence improvement heuristics Industrial eco-design

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A Novel multi-objective Optimization Based evolutionary Algorithm for Optimize the Services of Internet of Everything

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IEEE ACCESS 2022年 10卷 106798-106811页

作者： Singh, Shailendra Pratap Dhiman, Gaurav Viriyasitavat, Wattana Kautish, Sandeep Galgotias Univ Sch Comp Sci & Engn Greater Noida 203201 India Lebanese Amer Univ Dept Elect & Comp Engn Byblos 1701 Lebanon Chandigarh Univ Ctr Res & Dev Dept Comp Sci & Engn Gharuan 140413 Mohali India Graph Era Deemed Univ Dept Comp Sci & Engn Dehra Dun 248002 Uttar Pradesh India Univ Int Iberoamer Dept Project Management Campeche 24560 Mexico Chulalongkorn Univ Fac Commerce & Accountancy Dept Stat Business Informat Technol Div Bangkok 10330 Thailand Lord Buddha Educ Fdn LBEF Campus Kathmandu 44600 Nepal

In the new era, the Internet of Everything (IoE) provides distributed services like data, processes, people, and things, etc. The services to the connected IoE significantly increase the time of service, workload, energy consumption, and delay. These objectives conflict with each other. To address the issue, a novel multi-objective based evolutionary algorithm is proposed. In the proposed method, a new rapid mutation operator is incorporated with multi-objective differential evolution (MODE) to overcome the stagnation of the local optimum. The proposed method to maintain the diversity and enhance the convergence speed of the existing MODE algorithm is described. The proposed method provides more diversity and convergence speed for choosing better candidate solutions. The addition of the proposed method is evaluated with the application of IoE services. We have designed the two objective and three objective-based IoE services scenarios. Furthermore, the proposed method optimizes services like service cost, service delay, and the lifetime of sensors. It is interesting to observe that the proposed approach better performs the most recent state-of-the-art multiobjective evolutionary algorithms.

关键词： Internet of Things Optimization evolutionary computation Delays Costs Heuristic algorithms Cloud computing Adaptation differential evolution multi-objective evolutionary algorithms Internet of Everything

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Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index

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SOFT COMPUTING 2009年第5期13卷 437-449页

作者： Botta, Alessio Lazzerini, Beatrice Marcelloni, Francesco Stefanescu, Dan C. IMT Lucca Inst Adv Studies I-55100 Lucca Italy Univ Pisa Dipartimento Ingn Infromaz I-56122 Pisa Italy Suffolk Univ Dept Math & Comp Sci Boston MA USA

Context adaptation (CA) based on evolutionary algorithms is certainly a promising approach to the development of fuzzy rule-based systems (FRBSs). In CA, a context-free model is instantiated to a context-adapted FRBS so as to increase accuracy. A typical requirement in CA is that the context-adapted system maintains the same interpretability as the context-free model, a challenging constraint given that accuracy and interpretability are often conflicting objectives. Furthermore, interpretability is difficult to quantify because of its very nature of being a qualitative concept. In this paper, we first introduce a novel index based on fuzzy ordering relations in order to provide a measure of interpretability. Then, we use the proposed index and the mean square error as goals of a multi-objective evolutionary algorithm aimed at generating a set of Pareto-optimum context-adapted Mamdani-type FRBSs with different trade-offs between accuracy and interpretability. CA is obtained through the use of specifically designed operators that adjust the universe of the input and output variables, and modify the core, the support and the shape of fuzzy sets characterizing the partitions of these universes. Finally, we show results obtained by using our approach on synthetic and real data sets.

关键词： Fuzzy rule-based systems Context adaptation multi-objective evolutionary algorithms Fuzzy partition interpretability

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Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

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CONTROL ENGINEERING PRACTICE 2014年第1期28卷 58-73页

作者： Reynoso-Meza, Gilberto Blasco, Xavier Sanchis, Javier Martinez, Miguel Univ Politecn Valencia Inst Univ Automat & Informat Ind Valencia 46022 Spain

Control engineering problems are generally multi-objective problems;meaning that there are several specifications and requirements that must be fulfilled. A traditional approach for calculating a solution with the desired trade-off is to define an optimisation statement. multi-objective optimisation techniques deal with this problem from a particular perspective and search for a set of potentially preferable solutions;the designer may then analyse the trade-offs among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are discussed. Throughout this paper it is noticeable that EMO research has been developing towards different optimisation statements, but these statements are not commonly used in controller tuning. Gaps between EMO research and EMO applications on controller tuning are therefore detected and suggested as potential trends for research. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： evolutionary multi-objective optimisation multi-objective evolutionary algorithms multi-criteria decision making multi-objective optimisation design procedure Controller tuning

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A multi-objective evolutionary approach to image quality/compression trade-off in JPEG baseline algorithm

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APPLIED SOFT COMPUTING 2010年第2期10卷 548-561页

作者： Lazzerini, Beatrice Marcelloni, Francesco Vecchio, Massimo INRIA ASAP Res Team F-91893 Orsay France Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

The JPEG algorithm is one of the most used tools for compressing images. The main factor affecting the performance of the JPEG compression is the quantization process, which exploits the values contained in two tables, called quantization tables. The compression ratio and the quality of the decoded images are determined by these values. Thus, the correct choice of the quantization tables is crucial to the performance of the JPEG algorithm. In this paper, a two-objective evolutionary algorithm is applied to generate a family of optimal quantization tables which produce different trade-offs between image compression and quality. Compression is measured in terms of difference in percentage between the sizes of the original and compressed images, whereas quality is computed as mean squared error between the reconstructed and the original images. We discuss the application of the proposed approach to well-known benchmark images and show how the quantization tables determined by our method improve the performance of the JPEG algorithm with respect to the default tables suggested in Annex K of the JPEG standard. (C) 2009 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithms Image compression JPEG algorithm

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Formulation of the evolutionary-Based Data Assimilation, and its Implementation in Hydrological Forecasting

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WATER RESOURCES MANAGEMENT 2012年第13期26卷 3853-3870页

作者： Dumedah, Gift Monash Univ Dept Civil Engn Melbourne Vic 3800 Australia

multi-objective evolutionary algorithms (MOEAs) have gained popularity for their capability to handle complex and nonlinear problems. MOEAs are population-based search tools which employ the concept of biological evolution and natural selection. While MOEAs have been applied in numerous hydrological studies for parameter estimation, their formulation for solving data assimilation (DA) problems has not been completely formalized in the literature. This study presents the evolutionary-based data assimilation (EDA) where it formulates the MOEA strategy into an applied DA procedure. The study outlines the stochastic and adaptive capabilities of MOEAs, and shows how MOEA operators including Pareto dominance, crossover, and random variation are naturally suited to handle DA problems. The EDA employs the cost function from variational DA to approximate the least squares estimate between ensemble simulations and perturbed observation. The EDA uses the MOEA strategy to evolve a population of competing members through several cycles of evolution at each assimilation step. The EDA determines several non-dominated members for each assimilation time step, allows these members to evolve, and evaluate updated members for subsequent time steps. Several ensemble members are evaluated for each assimilation time step but the updated ensembles are determined as a subset of the final evolved population which comprise the Pareto-optimal set. The EDA has been illustrated in a practical implementation to assimilate daily streamflow into the Sacramento Soil Moisture Accounting model.

关键词： multi-objective evolutionary algorithms Model-data integration Pareto-optimality

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