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A Shift Vector Guided multiobjective evolutionary algorithm Based on Decomposition for Dynamic Optimization

IEEE ACCESS 2020年 8卷 38391-38403页

作者： Zhu, Zhen Tian, Xiang Xia, Changgao Chen, Long Cai, Yingfeng Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China

This paper presents a novel algorithm to deal with dynamic multiobjective optimization problems, in which the objective functions change over time. The algorithm adopts the decomposition framework to decompose the multiobjective optimization problems into a number of scalar optimization subproblems. For each subproblem, its respective solutions obtained in several former consecutive environments can form a moving trajectory over time. A shift vector guided prediction model is proposed, which samples three intermediately previous solutions of each subproblem to construct two shift vectors. The shift vectors use the weighted summation to generate a new shift vector as the forthcoming motion of the target solution. Then a new location in the later environment is estimated based on the current location and the newly generated shift vector. When detecting an environmental change, the multiobjective evolutionary algorithm based on decomposition will update the population using the predicted solutions by the proposed model. Empirical results demonstrate that our proposed algorithm is effective in tracking dynamic optimal solutions and shows great superiority comparing with state-of-the-art methods.

关键词： Decomposition dynamic environments multiobjective evolutionary algorithm prediction shift vector

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Crowding-measure-based multiobjective evolutionary algorithm for job shop scheduling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2006年第1-2期30卷 112-117页

作者： Lei, Deming Wu, Zhiming Shanghai Jiao Tong Univ Inst Automat Shanghai Peoples R China

multiobjective evolutionary algorithm (MOEA) has attracted much attention in the past decade;however, the application of MOEA to practical problems such as job shop scheduling is seldom considered. In this paper, crowding-measure-based multiobjective evolutionary algorithm (CMOEA) is first designed, which makes use of the crowding measure to adjust the external population and assign different fitness for individuals;then CMOEA is applied to job shop scheduling to minimize makespan and the total tardiness of jobs. Finally, the comparison between CMOEA and SPEA demonstrates that CMOEA performs well in job shop scheduling.

关键词： crowding measure job shop scheduling multiobjective evolutionary algorithm

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A multiobjective evolutionary algorithm to find community structures based on affinity propagation

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2016年 453卷 203-227页

作者： Shang, Ronghua Luo, Shuang Zhang, Weitong Stolkin, Rustam Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian Peoples R China Univ Birmingham Sch Mech Engn Birmingham B15 2TT W Midlands England

Community detection plays an important role in reflecting and understanding the topological structure of complex networks, and can be used to help mine the potential information in networks. This paper presents a multiobjective evolutionary algorithm based on Affinity Propagation (APMOEA) which improves the accuracy of community detection. Firstly, APMOEA takes the method of affinity propagation (AP) to initially divide the network. To accelerate its convergence, the multiobjective evolutionary algorithm selects nondominated solutions from the preliminary partitioning results as its initial population. Secondly, the multiobjective evolutionary algorithm finds solutions approximating the true Pareto optimal front through constantly selecting nondominated solutions from the population after crossover and mutation in iterations, which overcomes the tendency of data clustering methods to fall into local optima. Finally, APMOEA uses an elitist strategy, called "external archive", to prevent degeneration during the process of searching using the multiobjective evolutionary algorithm. According to this strategy, the preliminary partitioning results obtained by AP will be archived and participate in the final selection of Pareto-optimal solutions. Experiments on benchmark test data, including both computer-generated networks and eight real-world networks, show that the proposed algorithm achieves more accurate results and has faster convergence speed compared with seven other state-of-art algorithms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Complex network Community detection Affinity propagation multiobjective evolutionary algorithm

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Recognition of an Elastic-Plastic Constitutive Law by a multiobjective evolutionary algorithm

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2009年第6期27卷 729-740页

作者： Sheu, G. Feng Chia Univ Taichung Taiwan Chang Jung Christian Univ Tainan 714 Taiwan

This study presents the recognition of an elastic-plastic constitutive law by a multiobjective evolutionary algorithm (MOEA). This idea is illustrated by the identification of ellipse aspect ratio and plastic modulus of a reported bounding surface model. Based on the multi-goals of well predicting all available drained or undrained stress-strain behaviors simultaneously, the compromising solutions of these two parameters are found by a strength Pareto evolutionary algorithm 2 (SPEA2). Their fittest values are then determined by additionally introducing the Akaike information criterion. Experimental data for the Ottawa sand are used to test such processes. The results show that an MOEA is an efficient and automatic tool to identify the fittest form of an elastic-plastic constitutive law from a large amount of experimental data. However, sufficient data are required to determine the correct searching range of parameters to be identified.

关键词： multiobjective evolutionary algorithm Strength Pareto evolutionary algorithm 2 Akaike information criterion Bounding surface model

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Antenna Optimization With a Computationally Efficient multiobjective evolutionary algorithm

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2009年第1期57卷 260-263页

作者： John, Matthias Ammann, Max J. Dublin Inst Technol Sch Elect & Commun Engn Ctr Telecommun Value Chain Res Dublin 8 Ireland

An efficient multiobjective evolutionary algorithm is described for optimizing a novel spline based printed monopole antenna. The antenna geometry is based on spline outlines. Both radiating element and groundplane are simultaneously optimized by the algorithm. The resulting antenna performance is evaluated. It is shown that the evolutionary algorithm and the spline geometry can be used to efficiently generate ultrawideband antennas on limited computing resources.

关键词： Antenna optimization multiobjective evolutionary algorithm ultrawideband (UWB) antenna spline

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Optimizing Personalized Touristic Itineraries by a multiobjective evolutionary algorithm

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2016年第6期15卷 1269-1312页

作者： De Falco, Ivanoe Scafuri, Umberto Tarantino, Ernesto Natl Res Council Italy ICAR Via P Castellino I-80131 Naples Italy

The paper presents an electronic tourist guide, relying on an evolutionary optimizer, able to plan personalized multiple-day itineraries by considering several contrasting objectives. Since the itinerary planning can be modeled as an extension of the NP-complete team orienteering problem with time windows, a multiobjective evolutionary optimizer is proposed to find in reasonable times near-optimal solutions to such an extension. This optimizer automatically designs the itinerary by aiming at maximizing the tourists' satisfaction as a function of their personal preferences and environmental constraints, such as operating hours, visiting times and accessibility of the points of interests, and weather forecasting. Experimental evaluations have demonstrated that the proposed optimizer is effective in different simulated operating conditions.

关键词： Multiple-day orienteering problem with time windows multiobjective evolutionary algorithm personalized tour tourism

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A multiobjective evolutionary algorithm for Personalized Tours in Street Networks 18

A Multiobjective Evolutionary Algorithm for Personalized Tou...

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18th European Conference on Applications of evolutionary Computation (EvoApplications) as part of EvoSTAR Conference

作者： De Falco, Ivanoe Scafuri, Umberto Tarantino, Ernesto ICAR CNR Via P Castellino 111 I-80131 Naples Italy

ISBN: (纸本)9783319165486;9783319165493

The paper presents a novel optimizer to plan multiple-day walking itineraries, tailored to tourists' personal interests, in a street network modeled as a graph. The tour is automatically designed by maximizing the number of the Points of Interest (POIs) to visit as a function of both tourists' preferences and requirements, and constraints such as opening hours, visiting times and accessibility of the POIs, and weather forecasting. Since this itineray planning is classified as an NP-complete combinatorial optimization problem, a multiobjective evolutionary optimizer is here proposed. Such an optimizer is proven to be effective in designing personalized multiple-day tourist routes.

关键词： Multiple-day orienteering problem with time windows Personalized tour Tourism multiobjective evolutionary algorithm

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Extracting Information Cores with Multi-property Using a multiobjective evolutionary algorithm

Extracting Information Cores with Multi-property Using a Mul...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Ren, Zhenni Liu, Jing Xidian Univ Sch Artificial Intelligence Xian Shaanxi Peoples R China

ISBN: (纸本)9781728121536

Recommender systems are efficient tools to alleviate the information overload problem. The information core extraction problem aims at finding a group of users who carry reliable, objective information to represent the system to provide recommendations. Many greedy algorithms have been proposed to deal with this problem, but most algorithms only focus on finding an information core to achieve accurate recommendation results, losing sight of other recommendation objectives with equivalent importance. To this end, we model the information core extraction problem as a multiobjective optimization problem, and propose a multiobjective evolutionary algorithm to extract information cores, labeled as MOEA-IC. MOEA-IC extracts information cores with multi-property at the same time, which can provide candidate solutions with different properties for decision makers. In the experiments, the performance of MOEA-IC is validated on three datasets with varying selection rates and compared with that of two existing greedy algorithms. The experimental results demonstrate the efficiency of MOEA-IC and show that the information core extracted by MOEA-IC can not only be helpful in providing accuracy recommendation, but also provide recommendations of good performance in terms of coverage and diversity criteria. The significance of this study is that the information-core-based recommendation is timesaving, and decision makers can choose information cores with different properties provided by MOEA-IC based on practical requirements.

关键词： recommender system information core multiobjective evolutionary algorithm multiobjective optimization problem

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Constrained-multiobjective evolutionary algorithm for Distribution System Reconfiguration under Severe Constraints 12

Constrained-multiobjective Evolutionary Algorithm for Distri...

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12th IFAC Symposium on Control of Power and Energy Systems (CPES)

作者： Sekizaki, Shinya Nishizaki, Ichiro Hayashida, Tomohiro Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima Hiroshima Japan

Distribution system reconfiguration problems pose significant challenges due to their multiple objectives, high computational complexity, and severe constraints. These challenges become even more pronounced with the integration of renewable energy sources, which introduce uncertainties and further tighten the constraints. This paper proposes a constrained multiobjective optimization evolutionary algorithm (CMOEA) based on a dual-stage approach with a dual-population genetic operator. The proposed CMOEA is applied to a case study of a distribution system reconfiguration problem with severe constraints, uncertainties, and high computational demands, and it shows good performance in finding infeasible solutions with small constraint violations and feasible solutions with small objectives. Copyright (c) 2024 The Authors.

关键词： Distribution system Reconfiguration problem multiobjective optimization multiobjective evolutionary algorithm Constrained multiobjective optimization problem evolutionary algorithm

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A Framework of Gene Subset Selection Using multiobjective evolutionary algorithm

A Framework of Gene Subset Selection Using Multiobjective Ev...

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7th International-Association-for-Pattern-Recognition (IAPR) International Conference on Pattern Recognition in Bioinformatics (PRIB)

作者： Li, Yifeng Ngom, Alioune Rueda, Luis Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada

ISBN: (纸本)9783642341236

Microarray gene expression technique can provide snap shots of gene expression levels of samples. This technique is promising to be used in clinical diagnosis and genomic pathology. However, the curse of dimensionality and other problems have been challenging researchers for a decade. Selecting a few discriminative genes is an important choice. But gene subset selection is a NP hard problem. This paper proposes an effective gene selection framework. This framework integrates gene filtering, sample selection, and multiobjective evolutionary algorithm (MOEA). We use MOEA to optimize four objective functions taking into account of class relevance, feature redundancy, classification performance, and the number of selected genes. Experimental comparison shows that the proposed approach is better than a well-known recursive feature elimination method in terms of classification performance and time complexity.

关键词： gene selection sample selection non-negative matrix factorization multiobjective evolutionary algorithm

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