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many-objective evolutionary algorithm based on parallel distance for handling irregular Pareto fronts

SWARM AND evolutionary COMPUTATION 2024年 86卷

作者： Wei, Zichen Wang, Hui Wang, Shuai Zhang, Zhixia Cui, Zhihua Wang, Feng Peng, Hu Zhao, Jia Nanchang Inst Technol Sch Informat Engn Nanchang Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China

In recent years, various many -objective evolutionary algorithms (MaOEAs) have been proved to be successful in solving many -objective optimization problems (MaOPs). However, the performance of most MaOEAs is seriously affected when handling MaOPs with irregular Pareto fronts (PFs). In this paper, a new MaOEA variant based on parallel distance (called PDMaOEA) is proposed to solve MaOPs with irregular PFs. Firstly, two new metrics based on parallel distance are designed. The first one termed diversity metric can adapt to irregular PFs. The second one called comprehensive selection metric can consider both diversity and convergence simultaneously. Based on the two metrics, a mating selection method and an environmental selection strategy are proposed. In the mating selection, solutions with good convergence or diversity are chosen to improve the quality of offspring population. In the environmental selection, the selection pressure is significantly enhanced by the two metrics. Experimental study is validated on 19 irregular problems with different shapes of PFs. Performance of the proposed PDMaOEA is compared with six state-of-the-art algorithms. Statistical analysis shows that the proposed approach is competitive in handling MaOPs with irregular PFs.

关键词： many-objective evolutionary algorithms (MaOEAs) many-objective optimization problems (MaOPs) Optimization Irregular Pareto front Parallel distance

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A hypervolume fraction-based adaptive evolutionary algorithm for many-objective optimization and the application to electromagnetic device design

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 137卷

作者： Lin, Jie Zhang, Sheng Xin Xu, Yi Jiao Zheng, Shao Yong Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China

Performance of many-objective evolutionary algorithms (MaOEAs) heavily depends on the environmental selection strategy which determines the offspring for next generations. One kind of selection strategy may only suit certain kinds of optimization problems. Moreover, one single strategy might not always work well at different evolutionary stages. To adaptively adjust the environmental selection strategy, this paper proposes a hyper- volume fraction-based adaptive evolutionary algorithm (HFAEA). First, a hypervolume fraction-based estimation method is proposed to address the difficulty in detecting the feature of Pareto front. It calculates the ratio of the hypervolume of population coverage to the hypervolume of coordinate axis coverage. With a small or large hypervolume fraction, Pareto front is regarded as irregular or regular respectively and an adaptive switching strategy adaptively selects a proposed vector angle-based strategy or an improved reference vector-based strategy. HFAEA is compared with five state-of-the-art algorithms on 24 problems with a large hypervolume fraction and 24 problems with a small hypervolume fraction. Experimental results show that HFAEA is the most competitive in handling different kinds of problems. It outperforms algorithms that designed for irregular problems as well as algorithms that use uniformly distributed reference vectors in irregular problems. These findings highlight the effectiveness of the proposed hypervolume fraction-based estimation method. The superior performance is also demonstrated in two electromagnetic device optimization problems, including the designs of a compact single-layer butler matrix and a broadband filtering power divider, where better results than original ones are achieved and HFAEA also outperforms state-of-the-art MaOEAs.

关键词： many-objective evolutionary algorithms Adaptive environmental selection Hypervolume fraction Electromagnetic device optimization

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Performance of Decomposition-Based many-objective algorithms Strongly Depends on Pareto Front Shapes

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2017年第2期21卷 169-190页

作者： Ishibuchi, Hisao Setoguchi, Yu Masuda, Hiroyuki Nojima, Yusuke Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

Recently, a number of high performance many-objective evolutionary algorithms with systematically generated weight vectors have been proposed in the literature. Those algorithms often show surprisingly good performance on widely used DTLZ and WFG test problems. The performance of those algorithms has continued to be improved. The aim of this paper is to show our concern that such a performance improvement race may lead to the overspecialization of developed algorithms for the frequently used many-objective test problems. In this paper, we first explain the DTLZ and WFG test problems. Next, we explain many-objective evolutionary algorithms characterized by the use of systematically generated weight vectors. Then we discuss the relation between the features of the test problems and the search mechanisms of weight vector-based algorithms such as multiobjective evolutionary algorithm based on decomposition (MOEA/D), nondominated sorting genetic algorithm III (NSGA-III), MOEA/dominance and decomposition (MOEA/DD), and.-dominance based evolutionary algorithm (theta-DEA). Through computational experiments, we demonstrate that a slight change in the problem formulations of DTLZ and WFG deteriorates the performance of those algorithms. After explaining the reason for the performance deterioration, we discuss the necessity of more general test problems and more flexible algorithms.

关键词： Decomposition-based evolutionary algorithms many-objective evolutionary algorithms many-objective optimization many-objective test problems

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A many-objective optimization recommendation algorithm based on knowledge mining

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INFORMATION SCIENCES 2020年 537卷 148-161页

作者： Cai, Xingjuan Hu, Zhaoming Chen, Jinjun Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Univ Technol Sydney Sydney NSW Australia

Recommendation system (RS) is a technology that provides accurate recommendation for users. In order to make the recommendation results more accurate and diverse, we proposed a rating-based many-objective hybrid recommendation model that can optimize the accuracy, recall, diversity, novelty and coverage of the recommendation simultaneously. Additionally, a new generation-based fitness evaluation strategy and a partition-based knowledge mining strategy are proposed to improve the many-objective evolutionary algorithms (MaOEAs) to enhance the performance of the recommendations generated by the model. Finally, comparing the proposed many-objective optimization recommendation algorithm with the existing standard MaOEAs, experimental results demonstrate that the proposed algorithm can provide recommendations with the more and novel items on the basis of accuracy and diversity for users. (C) 2020 Elsevier Inc. All rights reserved.

关键词： many-objective evolutionary algorithms Recommendation system Knowledge mining Hybrid recommendation algorithm

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An improved matrix factorization based model for many-objective optimization recommendation

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INFORMATION SCIENCES 2021年 579卷 1-14页

作者： Cui, Zhihua Zhao, Peng Hu, Zhaoming Cai, Xingjuan Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing Peoples R China Swinburne Univ Technol Melbourne Vic Australia

As the application scenarios of recommendation algorithms are becoming increasingly complex, the efficiency of traditional recommendation algorithm based on accuracy is no longer satisfied. To solve this problem, an improved matrix factorization based model for many-objective optimization recommendation is proposed to simultaneously optimize the four recommendation objectives of novelty, diversity, accuracy, and recall. As a novel double-layer recommendation model, two improved algorithms are composed: 1) For the bottom layer, an improved matrix factorization algorithm with additional regulariza-tion constraints is used to predict unknown item ratings;2) For the top layer, the recom-mendation list is optimized by a many-objective evolutionary algorithm. Comprehensive experiments demonstrate that the proposed model can effectively improve the four recom-mended evaluation metrics. And a recommended list with novel and diverse items is pro-vided for users in a more efficient way while maintaining accuracy. (c) 2021 Elsevier Inc. All rights reserved.

关键词： many-objective evolutionary algorithms Recommendation algorithms Novel item recommendation Matrix factorization

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A many-objective population extremal optimization algorithm with an adaptive hybrid mutation operation

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INFORMATION SCIENCES 2019年 498卷 62-90页

作者： Chen, Min-Rong Zeng, Guo-Qiang Lu, Kang-Di South China Normal Univ Sch Comp Sci Guangzhou 510631 Guangdong Peoples R China Wenzhou Univ Natl Local Joint Engn Lab Digitalize Elect Design Wenzhou 325035 Peoples R China Donghua Univ Dept Automat Coll Informat Sci & Technol Shanghai 201620 Peoples R China

many-objective optimization problems abbreviated as MaOPs with more than three objectives have attracted increasing interests due to their widely existing in a variety of real world applications. This paper presents a novel many-objective population extremal optimization called MaOPEO-HM algorithm for MaOPs by introducing a reference set based many-objective optimization mechanism into a recently developed population extremal optimization framework and designing an adaptive hybrid mutation operation for updating the population. Despite of the successful applications of extremal optimization in different kinds of numerical and engineering optimization problems, it has never been explored to the many-objective optimization domain so far. Because most of the existing many-objective evolutionary algorithms are usually guided by a single mutation operation, which has insufficient ability to exploit the search space of MaOPs and will get stuck at any local efficient front, it is the first attempt to design a novel hybrid mutation scheme in MaOPEO-HM algorithm by combining the advantages of polynomial mutation operator and multi-non-uniform mutation operator effectively. The experiment results for DTLZ test problems with 3, 5, 8, 10, and 15 objectives and WFG test problems with 3, 5, and 8 objectives have demonstrated the superiority of the proposed MaOPEO-HM to five state-of-the-art decomposition-based many-objective evolutionary algorithms including NSGA-III, RVEA, EFR-RR, theta-DEA, and MOEA/DD and two non-decomposition-based algorithms in-cluding GrEA and Two_Arch2. Furthermore, the great ability of the designed adaptive hybrid mutation operation incorporated into many-objective population extremal optimization (MaOPEO) has also been illustrated by comparing MaOPEO-HM and two MaOPEO algorithms only based on traditional multi-non-uniform mutation or polynomial mutation for DTLZ problems. (C) 2019 Elsevier Inc. All rights reserved.

关键词： many-objective optimization problems many-objective evolutionary algorithms many-objective population extremal optimization Adaptive hybrid mutation operation

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On the Performance of Multiple objective evolutionary algorithms for Software Architecture Discovery 14

On the Performance of Multiple Objective Evolutionary Algori...

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16th Genetic and evolutionary Computation Conference (GECCO)

作者： Ramirez, Aurora Raul Romero, Jose Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

ISBN: (纸本)9781450326629

During the design of complex systems, software architects have to deal with a tangle of abstract artefacts, measures and ideas to discover the most fitting underlying architecture. A common way to structure these systems is in terms of their interacting software components, whose composition and connections need to be properly adjusted. Its abstract and highly combinatorial nature increases the complexity of the problem. In this scenario, Search-based Software Engineering (SBSE) may serve to support this decision making process from initial analysis models, since the discovery of component-based architectures can be formulated as a challenging multiple optimisation problem, where different metrics and configurations can be applied depending on the design requirements and its specific domain. many-objective optimisation evolutionary algorithms can provide an interesting alternative to classical multi-objective approaches. This paper presents a comparative study of five different algorithms, including an empirical analysis of their behaviour in terms of quality and variety of the returned solutions. Results are also discussed considering those aspects of concern to the expert in the decision making process, like the number and type of architectures found. The analysis of many-objectives algorithms constitutes an important challenge, since some of them have never been explored before in SBSE.

关键词： Search-based Software Engineering component-based software architecture multi-objective evolutionary algorithms many-objective evolutionary algorithms

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Resource-aware multi-criteria vehicle participation for federated learning in Internet of vehicles

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INFORMATION SCIENCES 2024年 664卷

作者： Wen, Jie Zhang, Jingbo Zhang, Zhixia Cui, Zhihua Cai, Xingjuan Chen, Jinjun Taiyuan Univ Sci & Technol Shanxi Key Lab Adv Control & Equipment Intelligenc Taiyuan Shanxi Peoples R China Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Shanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Australia

Federated learning (FL), as a safe distributed training mode, provides strong support for the edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and safe data sharing. However, due to the resource limitation and the instability of training environment in the complex IoV, ideal performance of FL cannot be achieved. Since considering the actual resource constraints and federated task requirements, the diversified device selection criteria make the resource-aware vehicle selection problem become a multi-criteria selection problem. To effectively support FL for IoV, the resource-aware multi-criteria vehicle selection problem was described as a many-objective optimization problem, and a resource-aware many-objective vehicle selection model (RA-MaOVSM) is proposed to optimize resource efficiency. The RAMaOVSM considering heterogeneous resources (like computation resources, communication resources, energy resources and data resources) of on-board devices in IoV, and realizes the joint optimization of learning efficiency, energy cost and global performance. Additionally, a novel probability distribution combination game strategy is applied to many-objective evolutionary algorithm (MaOEA) for improving the model solving performance. Simulation results demonstrate that RA-MaOVSM can effectively optimize the IoV resources and FL model performance, and the designed algorithm exhibits good convergence and distribution, achieving a good balance among multiple device selection criteria.

关键词： Federated learning Multi-criteria devices participation many-objective evolutionary algorithms Internet of Vehicles

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A many-objective optimization algorithm with dual criteria and mixed distribution correction strategy

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第21期35卷

作者： Zhang, Zhixia Wen, Jie Cai, Xingjuan Cui, Zhihua Taiyuan Univ Sci & Technol Shanxi Key Lab Adv Control & Equipment Intelligenc Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China

In many-objective optimization algorithms, it is very important to maintain significant convergence and diversity of the population. And with the increasing demand in various fields, the optimization problem also becomes gradually complicated. Some existing many-objective optimization algorithms are faced with challenges such as domination resistance and dimensional crisis. To solve these challenges, a many-objective optimization algorithm based on dual criteria and mixed distribution correction strategy (MaOEA-CSMDC) is proposed in this paper. To be specific, a matching selection strategy based on dual criteria combined by pareto domination strategy and achievement scalar function, which alleviates the domination resistance phenomenon and enhances the selection pressure of the algorithm. After that, an environment selection strategy based on equal probability mixed distribution correction is designed to better balance convergence and diversity. In this strategy, normal distribution, exponential distribution, and Cauchy distribution are introduced to adjust the weight of convergence and diversity in evolution by means of equal probability, so as to alleviate the problem that the conflict between them is intensified in the later stage of the algorithm. The experimental results show that, MaOEA-CSMDC not only has advantages in convergence and diversity indicators, but also is more competitive in solving many-objective optimization problems.

关键词： dual criteria many-objective evolutionary algorithms mixed distribution correction multiobjective optimization

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A generalized many-objective optimization approach for scenario discovery

Futures and Foresight Science

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Futures and Foresight Science 2019年第2期1卷

作者： Kwakkel, Jan H. Faculty of Technology Policy and Management Delft University of Technology Delft Netherlands

Scenario discovery is a model-based approach for scenario development, aimed at finding one or more subspaces within the uncertainty space associated with a model that is decision-relevant. These identified subspaces can subsequently be translated into narratives or shared in other ways a broader participatory process. Finding such as subspace involves solving a three-objective optimization problem. A subspace should cover many of the decision relevant model runs, while containing as few as possible nondecision relevant model runs, and being easy to interpret. Existing techniques for scenario discovery, however, focus only on finding a subspace that minimizes the number of nondecision relevant model runs. Adopting a single objective optimization approach for a many-objective optimization problem implies that the full trade-off space is not identified. In this paper, we introduce a many-objective optimization approach for scenario discovery. We compare this with an improved usage of Patient Rule Induction Method (PRIM) for identifying the multidimensional trade-offs amongst coverage, density, and interpretability. We find that the many-objective optimization approach produces results which slightly dominate those of the improved version of PRIM on all three objectives. Qualitatively, however, both approaches identify essentially the same subspaces. The prime benefits of the many-objective optimization approach are its potential in bringing additional scenario relevant concerns such as consistency or diversity into the scenario discovery framework, as well as its ability to avoid overfitting. Potentially more important, it also paves the way for future work on using more sophisticated metaheuristic optimization approaches for scenario discovery. © 2019 The Authors Futures & Foresight Science Published by John Wiley & Sons Ltd.

关键词： deep uncertainty exploratory modelling many-objective evolutionary algorithms model-based scenarios scenario discovery

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