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Evaluation of many-objective evolutionary algorithms by Hesitant Fuzzy Linguistic Term Set and Majority Operator

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2018年第6期20卷 2043-2056页

作者： Yu, Xiaobing Lu, Yiqun Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Res Ctr Prospering Jiangsu Prov Talents Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol China Inst Mfg Developing Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Jiangsu Peoples R China

Over the past few decades, many-objective evolutionary algorithms have been proposed and presented as competitive compared with state-of-the-art algorithms. The evaluation of these algorithms involves many performance metrics, considered as a multiple criteria decision-making problem. In order to fairly and faithfully evaluate these algorithms, a novel evaluation approach based on hesitant fuzzy linguistic term set and majority operator is proposed. Hesitant fuzzy linguistic term set is used to express the opinions of experts, and majority operator is used to aggregate the opinions of experts. The framework for evaluation is presented, in which comprehensive performance metrics are proposed. An experimental study is designed to validate the proposed method. The experimental results indicate that the proposed approach is accurate and effective;the ability of algorithms to solve many-objective problems relies on both algorithms and the features of problems. Finally, the proposed method is used to evaluate the meteorological disaster that occurred in China in 2008.

关键词： many-objective evolutionary algorithm Hesitant fuzzy linguistic term set Majority Evaluation Meteorological disaster

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Efficient Parasitic-aware gm/ID-based Hybrid Sizing Methodology for Analog and RF Integrated Circuits

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ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS 2021年第2期26卷 1-31页

作者： Liao, Tuotian Zhang, Lihong Mem Univ Newfoundland Fac Engn & Appl Sci Dept Elect & Comp Engn St John NF A1B 3X5 Canada

As the primary second-order effect, parasitic issues have to be seriously addressed when synthesizing high-performance analog and RF integrated circuits (ICs). In this article, a two-phase hybrid sizing methodology for analog and RF ICs is proposed to take into account parasitic effect in the early design stage. It involves symbolic modeling and mixed-integer nonlinear programming (MINLP) in the first phase, and a many-objective evolutionary algorithm (many-OEA)-based sizing refiner in the second phase. With the aid of our proposed current density factor and piecewise curve fitting technique, the g(m)/I-D concept, which is typically utilized to solve the analog circuit design problem, can provide theoretical support to our accurate symbolic modeling. Thus, the intrinsic and interconnect parasitics can be accurately considered in our work with moderate modeling effort. A variety of electrical, geometric, and parasitic (including parasitic mismatch) constraints can be conveniently integrated into our MINLP problem formulation. Moreover, numerical simulations are embedded into the many-OEA-based sizing phase, which is able to tackle floorplan co-optimization. With such dynamic floorplan variation, the parasitics accuracy can be sustained along the evolution. The experimental results demonstrate high efficacy of our proposed parasitic-aware hybrid sizing methodology.

关键词： Analog and RF ICs circuit sizing circuit modeling floorplan optimization g(m)/I-D many-objective evolutionary algorithm mixed-integer nonlinear programming parasitic modeling

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A new gradient stochastic ranking-based multi-indicator algorithm for many-objective optimization

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SOFT COMPUTING 2019年第21期23卷 10911-10929页

作者： Chen, Ye Yuan, Xiaoping Cang, Xiaohui China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Jiangsu Peoples R China Zhejiang Univ Sch Med Zhejiang Childrens Hosp Div Med Genet & Genom Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Sch Med Inst Genet Hangzhou 310058 Zhejiang Peoples R China

In this paper, we propose a gradient stochastic ranking-based multi-indicator algorithm (GSRA) to guide the direction of Pareto front selection pressure. The proposed algorithm primarily aims to enhance the relationship among the different indicators in indicator-based MOEAs. In GSRA, we assigned the Gaussian niche-preservation operation to evaluate the perpendicular distance of every niche member, we also chose two indicators with different biases to balance the convergence and diversity. In environmental selection, we used a two-tier gradient stochastic ranking method to carry out the offspring selection. Seven state-of-the-art EMO algorithms are selected as the peer algorithms to validate GSRA. A series of extensive experiments is conducted on 15 test problems taken from MaF test suites, these functions in the suites are frequently used in many-objective optimization such as DTLZ and WFC problems. The experimental results revealed that the GSRA can achieve both the desired convergence and distribution properties. The solution set obtained by GrEA can achieve a better coverage of the Pareto front than that obtained by other algorithms on most of the tested problems.

关键词： many-objective evolutionary algorithm Multi-indicator Gaussian niche preservation operation Gradient stochastic ranking

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many-objective Cooperative Co-evolutionary Feature Selection: A Lexicographic Approach 15th

Many-Objective Cooperative Co-evolutionary Feature Selection...

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15th International Work-Conference on Artificial Neural Networks (IWANN)

作者： Gonzalez, Jesus Ortega, Julio Damas, Miguel Martin-Smith, Pedro Univ Granada CITIC Dept Comp Architecture & Technol Granada Spain

ISBN: (纸本)9783030205188;9783030205171

This paper presents a new wrapper method able to optimize simultaneously the parameters of the classifier while the size of the subset of features that better describe the input dataset is also being minimized. The search algorithm used for this purpose is based on a co-evolutionary algorithm optimizing several objectives related with different desirable properties for the final solutions, such as its accuracy, its final number of features, and the generalization ability of the classifier. Since these objectives can be sorted according to their priorities, a lexicographic approach has been applied to handle this many-objective problem, which allows the use of a simple evolutionary algorithm to evolve each one of the different sub-populations.

关键词： many-objective evolutionary algorithm Cooperative co-evolutionary algorithm Lexicographic optimization Feature selection Wrapper approach

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Ensemble of many-objective evolutionary algorithms for many-objective problems

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SOFT COMPUTING 2017年第9期21卷 2407-2419页

作者： Zhou, Yalan Wang, Jiahai Chen, Jian Gao, Shangce Teng, Luyao Sun Yat Sen Univ Dept Comp Sci Guangzhou Guangdong Peoples R China Guangdong Univ Finance & Econ Coll Informat Guangzhou Guangdong Peoples R China Toyama Univ Dept Intellectual Informat Engn Fac Engn Toyama Japan Victoria Univ Coll Engn & Sci Melbourne Vic Australia

The performance of most existing multiobjective evolutionary algorithms deteriorates severely in the face of many-objective problems. many-objective optimization has been gaining increasing attention, and many new many-objective evolutionary algorithms (MaOEA) have recently been proposed. On the one hand, solution sets with totally different characteristics are obtained by different MaOEAs, since different MaOEAs have different convergence-diversity tradeoff relations. This may suggest the potential usefulness of ensemble approaches of different MaOEAs. On the other hand, the performance of MaOEAs may vary greatly from one problem to another, so that choosing the most appropriate MaOEA is often a non-trivial task. Hence, an MaOEA that performs generally well on a set of problems is often desirable. This study proposes an ensemble of MaOEAs (EMaOEA) for many-objective problems. When solving a single problem, EMaOEA invests its computational budget to its constituent MaOEAs, runs them in parallel and maintains interactions between them by a simple information sharing scheme. Experimental results on 80 benchmark problems have shown that, by integrating the advantages of different MaOEAs into one framework, EMaOEA not only provides practitioners a unified framework for solving their problem set, but also may lead to better performance than a single MaOEA.

关键词： many-objective evolutionary algorithm Ensemble algorithm portfolios many-objective optimization

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Multifaceted evolution focused on maximal exploitation of domain knowledge for the consensus inference of Gene Regulatory Networks

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Computers in Biology and Medicine 2025年第PT A期196卷

作者： Adrián Segura-Ortiz Karen Giménez-Orenga José García-Nieto Elisa Oltra José F. Aldana-Montes Dept. de Lenguajes y Ciencias de la Computación ITIS Software Universidad de Málaga Málaga 29071 Spain Escuela de Doctorado Universidad Católica de Valencia San Vicente Mártir Valencia 46001 Spain Biomedical Research Institute of Málaga (IBIMA) Universidad de Málaga Málaga Spain Department of Pathology School of Medicine and Health Sciences Universidad Católica de Valencia San Vicente Mártir Valencia 46001 Spain

The inference of gene regulatory networks (GRNs) is a fundamental challenge in systems biology, aiming to decipher gene interactions from expression data. However, traditional inference techniques exhibit disparities in their results and a clear preference for specific datasets. To address this issue, we present BIO-INSIGHT (Biologically Informed Optimizer - INtegrating Software to Infer GRNs by Holistic Thinking), a parallel asynchronous many-objective evolutionary algorithm that optimizes the consensus among multiple inference methods guided by biologically relevant objectives. BIO-INSIGHT has been evaluated on an academic benchmark of 106 GRNs, comparing its performance against MO-GENECI and other consensus strategies. The results show a statistically significant improvement in AUROC and AUPR, demonstrating that biologically guided optimization outperforms primarily mathematical approaches. Additionally, BIO-INSIGHT was applied to gene expression data from patients with fibromyalgia, myalgic encephalomyelitis, and co-diagnosis of both diseases. The inferred networks revealed regulatory interactions specific to each condition, suggesting its clinical utility in biomarker identification and potential therapeutic targets. The robustness and ingenuity of BIO-INSIGHT consolidate its potential as an innovative tool for GRN inference, enabling the generation of more accurate and biologically feasible networks. The source code is hosted in a public GitHub repository under the MIT license: https://***/AdrianSeguraOrtiz/BIO-INSIGHT . Moreover, to facilitate its reproducibility and usage, the software associated with this implementation has been packaged into a Python library available on PyPI: https://***/project/GENECI/3.0.1/ .

关键词： Gene regulatory network Inference Expression data many-objective evolutionary algorithm Consensus

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Efficient parasitic-aware hybrid sizing methodology for analog and RF integrated circuits

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INTEGRATION-THE VLSI JOURNAL 2018年第Jun.期62卷 301-313页

作者： Liao, Tuotian Zhang, Lihong Mem Univ Newfoundland Fac Engn & Appl Sci Dept Elect & Comp Engn St John NF Canada

In this paper, a highly efficient parasitic-aware hybrid sizing methodology is proposed. It involves geometric programming (GP) as the first phase, both single-objective and many-objective evolutionary algorithms (EA) as the second sizing phase. The circuit performance constraints and layout-induced parasitics are considered simultaneously right from the GP sizing phase, while the optimization accuracy is significantly improved in the EA sizing phase. The proposed methodology features an effective integration of layout information into both sizing phases. It has been used to optimize several high-performance analog and RF circuits in different CMOS technologies. The experimental results demonstrate high efficacy of our proposed parasitic-aware hybrid sizing methodology.

关键词： Circuit sizing many-objective evolutionary algorithm Floorplan optimization Geometric programming Parasitic modeling

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A NEW HYPERVOLUME-BASED DIFFERENTIAL EVOLUTION algorithm FOR many-objective OPTIMIZATION

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RAIRO-OPERATIONS RESEARCH 2017年第4期51卷 1301-1315页

作者： Liu, Chao Zhao, Qi Yan, Bai Gao, Yang Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China Res Base Beijing Modern Mfg Dev Beijing 100124 Peoples R China Beijing Univ Technol Inst Laser Engn Beijing 100124 Peoples R China

evolutionary algorithms are successfully used for many-objective optimization. However, solutions are prone to become nondominated from each other with the increase in the number of objectives, which reduces the efficiency of Pareto dominance-based algorithms. In this paper, a new hypervolume-based differential evolution algorithm (MODEhv) is proposed for many-objective optimization problems (MaOPs). In MODEhv, a modified differential evolution paradigm with automatic parameter configuration strategy is used to balance exploration and exploitation of the algorithm. Besides, the hypervolume indicator is incorporated for the selection of solutions to be varied and solutions to be kept in archive respectively. Finally, a threshold technique is employed to improve diversity of solutions in archive. MODEhv is investigated on a set of widely used benchmark problems and compared with five state-of-the-art algorithms. The experimental results show the efficiency of MODEhv for solving MaOPs.

关键词： Differential Evolution Hypervolume indicator many-objective optimization many-objective evolutionary algorithm

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Reference line-based Estimation of Distribution algorithm for many-objective optimization

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KNOWLEDGE-BASED SYSTEMS 2017年 132卷 129-143页

作者： Sun, Yanan Yen, Gary G. Yi, Zhang Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Multi-objective evolutionary algorithms (MOEAs) are preferred in solving multi-objective optimization problems due to their considerable performance giving decision-maker a set of not only convergent but diversified promising solutions. However, the scalability of MOEAs deteriorates in addressing many objective optimization problems which involve more than three conflicting objectives. The principal reason is largely due to the deficiency of the existing genetic operators which cannot generate promising offspring from parents chosen by the Pareto-dominance rule in these MOEAs. Estimation of Distribution algorithms (EDAs) generate offspring with a probabilistic model built from the statistics extracting upon existing solutions to expectedly alleviate the weakness arisen in genetic operators. In this paper, a reference line-based EDA is proposed for effectively solving many-objective optimization problems. Specifically, the estimation model is built based on the reference lines in the decision space to sample solutions with favorable proximity. Then solutions with considerable diversity in Pareto-optimal front are selected. These two phases collectively promote the needed convergence and diversity for the proposed algorithm. To evaluate the performance, extensive experiments are performed against four state-of-the-art many objective evolutionary algorithms and two EDAs over DTLZ and WFG test suites with 5-, 8-, 10-, and 15-objective. Experimental results quantified by the selected performance metrics indicate that the proposed algorithm shows significant competitiveness in tackling many-objective optimization problems. (C) 2017 Elsevier B.V. All rights reserved.

关键词： many-objective evolutionary algorithm evolutionary computation Reference line Estimation of Distribution algorithm

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Stochastic Ranking algorithm for many-objective Optimization Based on Multiple Indicators

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2016年第6期20卷 924-938页

作者： Li, Bingdong Tang, Ke Li, Jinlong Yao, Xin USTC Sch Comp Sci & Technol Univ Sci & Technol China USTC Birmingham Joint Re Hefei 230027 Peoples R China Univ Birmingham Ctr Excellence Res Computat Intelligence & Applic Sch Comp Sci Birmingham B15 2TT W Midlands England

Traditional multiobjective evolutionary algorithms face a great challenge when dealing with many objectives. This is due to a high proportion of nondominated solutions in the population and low selection pressure toward the Pareto front. In order to tackle this issue, a series of indicator-based algorithms have been proposed to guide the search process toward the Pareto front. However, a single indicator might be biased and lead the population to converge to a subregion of the Pareto front. In this paper, a multi-indicator-based algorithm is proposed for many-objective optimization problems. The proposed algorithm, namely stochastic ranking-based multi-indicator algorithm (SRA), adopts the stochastic ranking technique to balance the search biases of different indicators. Empirical studies on a large number (39 in total) of problem instances from two well-defined benchmark sets with 5, 10, and 15 objectives demonstrate that SRA performs well in terms of inverted generational distance and hypervolume metrics when compared with state-of-the-art algorithms. Empirical studies also reveal that, in the case a problem requires the algorithm to have strong convergence ability, the performance of SRA can be further improved by incorporating a direction-based archive to store well-converged solutions and maintain diversity.

关键词： Archive method many-objective evolutionary algorithm multi-indicator multiobjective optimization stochastic ranking

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