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Preference-Based evolutionary multiobjective optimization Through the Use of Reservation and Aspiration Points

IEEE ACCESS 2021年 9卷 108861-108872页

作者： Gonzalez-Gallardo, Sandra Saborido, Ruben Ruiz, Ana B. Luque, Mariano Univ Malaga Dept Appl Econ Math Campus EL Ejido Malaga 29071 Spain Univ Malaga ITIS Software Campus Teatinos Malaga 29071 Spain

Preference-based evolutionary multiobjective optimization (EMO) algorithms approximate the region of interest (ROI) of the Pareto optimal front defined by the preferences of a decision maker (DM). Here, we propose a preference-based EMO algorithm, in which the preferences are given by means of aspiration and reservation points. The aspiration point is formed by objective values which the DM wants to achieve, while the reservation point is constituted by values for the objectives not to be worsened. Internally, the first generations are performed in order to generate an initial approximation set according to the reservation point. Next, in the remaining generations, the algorithm adapts the search for new non-dominated solutions depending on the dominance relation between the solutions obtained so far and both the reservation and aspiration points. This allows knowing if the given points are achievable or not;this type of information cannot be known before the solution process starts. On this basis, the algorithm proceeds according to three different scenarios with the aim of re-orienting the search directions towards the ROI formed by the Pareto optimal solutions with objective values within the given aspiration and reservation values. Computational results show the potential of our proposal in 2, 3 and 5-objective test problems, in comparison to other state-of-the-art algorithms.

关键词： Pareto optimization Approximation algorithms Linear programming optimization Proposals Task analysis Licenses evolutionary multiobjective optimization preferences aspiration and reservation points weight vectors

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Design of combinational logic circuits through an evolutionary multiobjective optimization approach

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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING 2002年第1期16卷 39-53页

作者： Coello, CAC Aguirre, AH CINVESTAV IPN Dept Ingn Elect Secc Computac Mexico City 07300 DF Mexico Tulane Univ Dept Comp Sci & Elect Engn New Orleans LA 70118 USA

In this paper, we propose a population-based evolutionary multiobjective optimization approach to design combinational circuits. Our results indicate that the proposed approach can significantly reduce the computational effort required by a genetic algorithm (GA) to design circuits at a gate level while generating equivalent or even better solutions (i.e., circuits with a lower number of gates) than a human designer or even other GAs. Several examples taken from the literature are used to evaluate the performance of the proposed approach.

关键词： circuit design evolvable hardware evolutionary multiobjective optimization genetic algorithms multiobjective optimization

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Large-Scale evolutionary multiobjective optimization Assisted by Directed Sampling

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2021年第4期25卷 724-738页

作者： Qin, Shufen Sun, Chaoli Jin, Yaochu Tan, Ying Fieldsend, Jonathan Taiyuan Univ Sci & Technol Sch Elect Informat Engn Taiyuan 030024 Peoples R China Taiyuan Univ Sci & Technol Dept Comp Sci & Technol Taiyuan 030024 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England

It is particularly challenging for evolutionary algorithms to quickly converge to the Pareto front in large-scale multiobjective optimization. To tackle this problem, this article proposes a large-scale multiobjective evolutionary algorithm assisted by some selected individuals generated by directed sampling (DS). At each generation, a set of individuals closer to the ideal point is chosen for performing a DS in the decision space, and those nondominated ones of the sampled solutions are used to assist the reproduction to improve the convergence in evolutionary large-scale multiobjective optimization. In addition, elitist nondominated sorting is adopted complementarily for environmental selection with a reference vector-based method in order to maintain diversity of the population. Our experimental results show that the proposed algorithm is highly competitive on large-scale multiobjective optimization test problems with up to 5000 decision variables compared to five state-of-the-art multiobjective evolutionary algorithms.

关键词： optimization Statistics Sociology Search problems Convergence Sorting Computer science Directed sampling (DS) evolutionary multiobjective optimization large-scale multiobjective problems (LSMOPs) nondominated sorting reference vectors

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Adaptive directional local search strategy for hybrid evolutionary multiobjective optimization

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APPLIED SOFT COMPUTING 2014年 19卷 290-311页

作者： Kim, Hyoungjin Liou, Meng-Sing Sci Applicat Int Corp Cleveland OH 44135 USA NASA Glenn Res Ctr Cleveland OH 44135 USA

A novel adaptive local search method is developed for hybrid evolutionary multiobjective algorithms (EMOA) to improve convergence to the Pareto front in multiobjective optimization. The concepts of local and global effectiveness of a local search operator are suggested for dynamic adjustment of adaptation parameters. Local effectiveness is measured by quantitative comparison of improvements in convergence made by local and genetic operators based on a composite objective. Global effectiveness is determined by the ratio of number of local search solutions to genetic search solutions in the nondominated solution set. To be consistent with the adaptation strategy, a new directional local search operator, eLS (efficient Local Search), minimizing the composite objective function is designed. The search direction is determined using a centroid solution of existing neighbor solutions without making explicit calculations of gradient information. The search distance of eLS decreases adaptively as the optimization process converges. Performances of hybrid methods NSGA-II + eLS are compared with the baseline NSGA-II and NSGA-II + HCS1 for multiobjective test problems, such as ZDT and DTLZ functions. The neighborhood radius and local search probability are selected as adaptation parameters. Results show that the present adaptive local search strategy can provide significant convergence enhancement from the baseline EMOA by dynamic adjustment of adaptation parameters monitoring the properties of multiobjective problems on the fly. (C) 2014 Elsevier B.V. All rights reserved.

关键词： evolutionary multiobjective optimization Memetic algorithms Directional operator Adaptive local search

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A Constrained Decomposition Approach With Grids for evolutionary multiobjective optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2018年第4期22卷 564-577页

作者： Cai, Xinye Mei, Zhiwei Fan, Zhun Zhang, Qingfu Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210023 Jiangsu Peoples R China Shantou Univ Sch Engn Guangdong Prov Key Lab Digital Signal & Image Pro Shantou 515063 Peoples R China Shantou Univ Sch Engn Dept Elect Engn Shantou 515063 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

Decomposition-based multiobjective evolutionary algorithms (MOEAs) decompose a multiobjective optimization problem (MOP) into a set of scalar objective subproblems and solve them in a collaborative way. Commonly used decomposition approaches originate from mathematical programming and the direct use of them may not suit MOEAs due to their population-based property. For instance, these decomposition approaches used in MOEAs may cause the loss of diversity and/or be very sensitive to the shapes of Pareto fronts (PFs). This paper proposes a constrained decomposition with grids (CDG) that can better address these two issues thus more suitable for MOEAs. In addition, different subproblems in CDG defined by the constrained decomposition constitute a grid system. The grids have an inherent property of reflecting the information of neighborhood structures among the solutions, which is a desirable property for restricted mating selection in MOEAs. Based on CDG, a constrained decomposition MOEA with grid (CDG-MOEA) is further proposed. Extensive experiments are conducted to compare CDG-MOEA with the domination-based, indicator-based, and state-of-the-art decomposition-based MOEAs. The experimental results show that CDG-MOEA outperforms the compared algorithms in terms of both the convergence and diversity. More importantly, it is robust to the shapes of PFs and can still be very effective on MOPs with complex PFs (e.g., extremely convex, or with disparately scaled objectives).

关键词： Constrained decomposition evolutionary multiobjective optimization grids robust to Pareto front (PF)

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Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning

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SOFT COMPUTING 2011年第12期15卷 2415-2434页

作者： Ishibuchi, Hisao Nakashima, Yusuke Nojima, Yusuke Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Osaka 5998531 Japan

Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions.

关键词： Fuzzy rule-based classification Genetic algorithms Genetics-based machine learning multiobjective machine learning evolutionary multiobjective optimization

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An evolutionary multiobjective optimization Based Fuzzy Method for Overlapping Community Detection

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第11期28卷 2841-2855页

作者： Tian, Ye Yang, Shangshang Zhang, Xingyi Anhui Univ Inst Phys Sci Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Anhui Univ Minist Educ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China

In the last decade, the detection of overlapping communities has received increasing attention in network science. Among various clustering techniques, the fuzzy clustering has been widely adopted in overlapping community detection, since the soft assignment provided by it naturally meets the overlapping between multiple communities. The crucial step of fuzzy-clustering-based overlapping community detection is to find the optimal community centers, so that the overlapping communities can be obtained according to the membership degrees between nodes and community centers. In this article, we propose an evolutionary multiobjective optimization-based fuzzy method for overlapping community detection. In contrast to traditional fuzzy clustering methods, the proposed method optimizes the community centers by using a specially tailored multiobjective evolutionary algorithm. Moreover, it can also find an appropriate fuzzy threshold for each node, so that diverse overlapping community structures can be uncovered. In the experiments, we compare the proposed method with six state-of-the-art overlapping community detection approaches on synthetic and real-world networks with different scales and characteristics. The statistical results demonstrate that the proposed method can obtain the best results on most test instances.

关键词： Complex network evolutionary multiobjective optimization fuzzy clustering overlapping community detection

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evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers

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International Journal of Hybrid Intelligent Systems 2006年第3期3卷 129-145页

作者： Ishibuchi, Hisao Nojima, Yusuke Department of Computer Science and Intelligent Systems Graduate School of Engineering Osaka Prefecture University 1-1 Gakuen-cho Naka-ku Sakai Osaka 599-8531 Japan

In this paper, we examine the application of evolutionary multiobjective optimization (EMO) algorithms to the design of fuzzy rule-based ensemble classifiers. An EMO algorithm is used to search for a large number of non-dominated fuzzy rule-based classifiers along the accuracy-complexity tradeoff surface. The accuracy of each fuzzy rule-based classifier is measured by the number of correctly classified training patterns while its complexity is measured by the number of fuzzy rules and the total number of antecedent conditions. An ensemble classifier is designed by combining non-dominated fuzzy rule-based classifiers. We examine the performance of ensemble classifiers through computational experiments on six benchmark data sets in the UCI machine learning repository. We also examine the diversity of individual fuzzy rule-based classifiers in each ensemble classifier.

关键词： evolutionary multiobjective optimization fuzzy rule-based classifiers ensemble classifiers genetic rule selection fuzzy data mining

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Learning Value Functions in Interactive evolutionary multiobjective optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2015年第1期19卷 88-102页

作者： Branke, Juergen Greco, Salvatore Slowinski, Roman Zielniewicz, Piotr Univ Warwick Warwick Business Sch Coventry CV4 7AL W Midlands England Univ Catania Dept Econ & Business I-95124 Catania Italy Univ Portsmouth Portsmouth Business Sch Portsmouth PO1 2UP Hants England Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland Polish Acad Sci Syst Res Inst PL-01447 Warshaw Poland

This paper proposes an interactive multiobjective evolutionary algorithm (MOEA) that attempts to learn a value function capturing the users' true preferences. At regular intervals, the user is asked to rank a single pair of solutions. This information is used to update the algorithm's internal value function model, and the model is used in subsequent generations to rank solutions incomparable according to dominance. This speeds up evolution toward the region of the Pareto front that is most desirable to the user. We take into account the most general additive value function as a preference model and we empirically compare different ways to identify the value function that seems to be the most representative with respect to the given preference information, different types of user preferences, and different ways to use the learned value function in the MOEA. Results on a number of different scenarios suggest that the proposed algorithm works well over a range of benchmark problems and types of user preferences.

关键词： evolutionary multiobjective optimization interactive procedure ordinal regression preference learning

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An Efficient Approach to Nondominated Sorting for evolutionary multiobjective optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2015年第2期19卷 201-213页

作者： Zhang, Xingyi Tian, Ye Cheng, Ran Jin, Yaochu Anhui Univ Sch Comp Sci & Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230039 Peoples R China Univ Surrey Dept Comp Guildford GU2 7XH Surrey England

evolutionary algorithms have been shown to be powerful for solving multiobjective optimization problems, in which nondominated sorting is a widely adopted technique in selection. This technique, however, can be computationally expensive, especially when the number of individuals in the population becomes large. This is mainly because in most existing nondominated sorting algorithms, a solution needs to be compared with all other solutions before it can be assigned to a front. In this paper we propose a novel, computationally efficient approach to nondominated sorting, termed efficient nondominated sort (ENS). In ENS, a solution to be assigned to a front needs to be compared only with those that have already been assigned to a front, thereby avoiding many unnecessary dominance comparisons. Based on this new approach, two nondominated sorting algorithms have been suggested. Both theoretical analysis and empirical results show that the ENS-based sorting algorithms are computationally more efficient than the state-of-the-art nondominated sorting methods.

关键词： Computational complexity evolutionary multiobjective optimization nondominated sorting Pareto-optimality

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