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Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems
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作者 Sheng Qi Rui Wang +3 位作者 Tao Zhang Weixiong Huang Fan Yu Ling Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第8期1786-1801,共16页
Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to tr... Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges. 展开更多
关键词 evolutionary algorithms pattern mining sparse large-scale multi-objective problems(SLMOPs) sparse large-scale optimization.
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Design and optimization of diffraction-limited storage ring lattices based on many-objective evolutionary algorithms 被引量:1
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作者 He-Xing Yin Jia-Bao Guan +1 位作者 Shun-Qiang Tian Ji-Ke Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第10期20-35,共16页
Multi-objective evolutionary algorithms(MOEAs) are typically used to optimize two or three objectives in the accelerator field and perform well. However, the performance of these algorithms may severely deteriorate wh... Multi-objective evolutionary algorithms(MOEAs) are typically used to optimize two or three objectives in the accelerator field and perform well. However, the performance of these algorithms may severely deteriorate when the optimization objectives for an accelerator are equal to or greater than four. Recently, many-objective evolutionary algorithms(MaOEAs)that can solve problems with four or more optimization objectives have received extensive attention. In this study, two diffraction-limited storage ring(DLSR) lattices of the Extremely Brilliant Source(ESRF-EBS) type with different energies were designed and optimized using three MaOEAs and a widely used MOEA. The initial population was found to have a significant impact on the performance of the algorithms and was carefully studied. The performances of the four algorithms were compared, and the results demonstrated that the grid-based evolutionary algorithm(GrEA) had the best performance.Ma OEAs were applied in many-objective optimization of DLSR lattices for the first time, and lattices with natural emittances of 116 and 23 pm·rad were obtained at energies of 2 and 6 GeV, respectively, both with reasonable dynamic aperture and local momentum aperture(LMA). This work provides a valuable reference for future many-objective optimization of DLSRs. 展开更多
关键词 Storage ring lattices Many-objective evolutionary algorithms GrEA algorithm NSGA
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Optimal Design of Tapered Roller Bearings Based on Multi⁃Physics Objectives Using Evolutionary Algorithms
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作者 Rajiv Tiwari Rahul M.P.Chandran 《Journal of Harbin Institute of Technology(New Series)》 CAS 2023年第1期73-84,共12页
Rolling element bearing is the most common machine element in rotating machinery.An extended life is among the foremost imperative standards in the optimal design of rolling element bearings,which confide on the fatig... Rolling element bearing is the most common machine element in rotating machinery.An extended life is among the foremost imperative standards in the optimal design of rolling element bearings,which confide on the fatigue failure,wear,and thermal conditions of bearings.To fill the gap,in the current work,all three objectives of a tapered roller bearing have been innovatively considered respectively,which are the dynamic capacity,elasto-hydrodynamic lubrication(EHL)minimum film⁃thickness,and maximum bearing temperature.These objective function formulations are presented,associated design variables are identified,and constraints are discussed.To solve complex non⁃linear constrained optimization formulations,a best⁃practice design procedure was investigated using the Artificial Bee Colony(ABC)algorithms.A sensitivity analysis of several geometric design variables was conducted to observe the difference in all three objectives.An excellent enhancement was found in the bearing designs that have been optimized as compared with bearing standards and previously published works.The present study will definitely add to the present experience based design followed in bearing industries to save time and obtain assessment of bearing performance before manufacturing.To verify the improvement,an experimental investigation is worthwhile conducting. 展开更多
关键词 dynamic capacity evolutionary algorithm optimum design tapered roller bearings TEMPERATURE tolerance analysis
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
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作者 Shehab Abdulhabib Alzaeemi Kim Gaik Tay +2 位作者 Audrey Huong Saratha Sathasivam Majid Khan bin Majahar Ali 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期1163-1184,共22页
Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algor... Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability programming.Inspired by evolutionary algorithms,which can iteratively find the nearoptimal solution,different Evolutionary Algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic Algorithm(GA),Evolution Strategy Algorithm(ES),Differential Evolution Algorithm(DE),and Evolutionary Programming Algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language.With the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms.It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT. 展开更多
关键词 Satisfiability logic programming symbolic radial basis function neural network evolutionary programming algorithm genetic algorithm evolution strategy algorithm differential evolution algorithm
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Time Complexity of Evolutionary Algorithms for Combinatorial Optimization:A Decade of Results 被引量:5
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作者 Pietro S.Oliveto 《International Journal of Automation and computing》 EI 2007年第3期281-293,共13页
Computational time complexity analyzes of evolutionary algorithms (EAs) have been performed since the mid-nineties. The first results were related to very simple algorithms, such as the (1+1)-EA, on toy problems.... Computational time complexity analyzes of evolutionary algorithms (EAs) have been performed since the mid-nineties. The first results were related to very simple algorithms, such as the (1+1)-EA, on toy problems. These efforts produced a deeper understanding of how EAs perform on different kinds of fitness landscapes and general mathematical tools that may be extended to the analysis of more complicated EAs on more realistic problems. In fact, in recent years, it has been possible to analyze the (1+1)-EA on combinatorial optimization problems with practical applications and more realistic population-based EAs on structured toy problems. This paper presents a survey of the results obtained in the last decade along these two research lines. The most common mathematical techniques are introduced, the basic ideas behind them are discussed and their elective applications are highlighted. Solved problems that were still open are enumerated as are those still awaiting for a solution. New questions and problems arisen in the meantime are also considered. 展开更多
关键词 evolutionary algorithms computational complexity combinatorial optimization evolutionary computation theory.
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MULTIOBJECT OPTIMIZATION OF A CENTRIFUGAL IMPELLER USING EVOLUTIONARY ALGORITHMS 被引量:3
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作者 LiJun LiuLijun FengZhenping 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2004年第3期389-393,共5页
Application of the multiobjective evolutionary algorithms to the aerodynamicoptimization design of a centrifugal impeller is presented. The aerodynamic performance of acentrifugal impeller is evaluated by using the th... Application of the multiobjective evolutionary algorithms to the aerodynamicoptimization design of a centrifugal impeller is presented. The aerodynamic performance of acentrifugal impeller is evaluated by using the three-dimensional Navier-Stokes solutions. Thetypical centrifugal impeller is redesigned for maximization of the pressure rise and blade load andminimization of the rotational total pressure loss at the given flow conditions. The Bezier curvesare used to parameterize the three-dimensional impeller blade shape. The present method obtains manyreasonable Pareto optimal designs that outperform the original centrifugal impeller. Detailedobservation of the certain Pareto optimal design demonstrates the feasibility of the presentmultiobjective optimization method tool for turbomachinery design. 展开更多
关键词 Centrifugal impeller Navier-Stokes solver evolutionary algorithms Multiobjective optimization DESIGN
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Two Aspects of Evolutionary Algorithms 被引量:3
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作者 Zbigniew Michalewicz Department of Computer Science, University of North Carolina, Charlotte, NC 28223, USA, and Institute of Computer Science, Polish Academy of Sciences, ul. Ordona 21, 01-237 Warsaw, Poland 《Wuhan University Journal of Natural Sciences》 CAS 2000年第4期413-424,共12页
In this paper we discuss the paradigm of evolutionary algorithms (EAs). We argue about the need for new heuristics in real-world problem solving, discussing reasons why some problems are difficult to solve. After intr... In this paper we discuss the paradigm of evolutionary algorithms (EAs). We argue about the need for new heuristics in real-world problem solving, discussing reasons why some problems are difficult to solve. After introducing the main concepts of evolutionary algorithms, we concentrate on two issues: (1) self-adaptation of the parameters of EA, and (2) handling constraints. 展开更多
关键词 Key words problem solving evolutionary algorithms HEURISTICS CONSTRAINT HANDLING ADAPTATION
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Immune evolutionary algorithms with domain knowledge for simultaneous localization and mapping 被引量:4
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作者 李枚毅 蔡自兴 《Journal of Central South University of Technology》 EI 2006年第5期529-535,共7页
Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were de... Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms. 展开更多
关键词 immune evolutionary algorithms simultaneous localization and mapping domain knowledge
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Selection of optimal land uses for the reclamation of surface mines by using evolutionary algorithms 被引量:2
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作者 Palogos Ioannis Galetakis Michael +1 位作者 Roumpos Christos Pavloudakis Francis 《International Journal of Mining Science and Technology》 SCIE EI CSCD 2017年第3期491-498,共8页
A methodology for the selection of the optimal land uses of the reclamation of mined areas is proposed. It takes into consideration several multi-nature criteria and constraints, including spatial constrains related t... A methodology for the selection of the optimal land uses of the reclamation of mined areas is proposed. It takes into consideration several multi-nature criteria and constraints, including spatial constrains related to the permissible land uses in certain parts of the mined area. The methodology combines desirability functions and evolution searching algorithms for selection of the optimal reclamation scheme. Its application for the reclamation planning of the Amynteon lignite surface mine in Greece indicated that it handles effectively spatial and non-spatial constraints and incorporates easily the decision-makers preferences regarding the reclamation strategy in the optimization procedure. 展开更多
关键词 RECLAMATION Land uses OPTIMIZATION evolutionary algorithms Desirability functions
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Managing Software Testing Technical Debt Using Evolutionary Algorithms 被引量:1
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作者 Muhammad Abid Jamil Mohamed K.Nour 《Computers, Materials & Continua》 SCIE EI 2022年第10期735-747,共13页
Technical debt(TD)happens when project teams carry out technical decisions in favor of a short-term goal(s)in their projects,whether deliberately or unknowingly.TD must be properly managed to guarantee that its negati... Technical debt(TD)happens when project teams carry out technical decisions in favor of a short-term goal(s)in their projects,whether deliberately or unknowingly.TD must be properly managed to guarantee that its negative implications do not outweigh its advantages.A lot of research has been conducted to show that TD has evolved into a common problem with considerable financial burden.Test technical debt is the technical debt aspect of testing(or test debt).Test debt is a relatively new concept that has piqued the curiosity of the software industry in recent years.In this article,we assume that the organization selects the testing artifacts at the start of every sprint.Implementing the latest features in consideration of expected business value and repaying technical debt are among candidate tasks in terms of the testing process(test cases increments).To gain the maximum benefit for the organization in terms of software testing optimization,there is a need to select the artifacts(i.e.,test cases)with maximum feature coverage within the available resources.The management of testing optimization for large projects is complicated and can also be treated as a multi-objective problem that entails a trade-off between the agile software’s short-term and long-term value.In this article,we implement a multi-objective indicatorbased evolutionary algorithm(IBEA)for fixing such optimization issues.The capability of the algorithm is evidenced by adding it to a real case study of a university registration process. 展开更多
关键词 Technical debt software testing optimization large scale agile projects evolutionary algorithms multiobjective optimization indicatorbased evolutionary algorithm(IBEA) pareto front
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AN ANALYSIS ABOUT BEHAVIOR OF EVOLUTIONARY ALGORITHMS:A KIND OF THEORETICAL DESCRIPTION BASED ON GLOBAL RANDOM SEARCH METHODS 被引量:1
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作者 Ding Lixin Kang Lishan +1 位作者 Chen Yupin Zhou Shaoquan 《Wuhan University Journal of Natural Sciences》 CAS 1998年第1期31-31,共1页
Evolutionary computation is a kind of adaptive non--numerical computation method which is designed tosimulate evolution of nature. In this paper, evolutionary algorithm behavior is described in terms of theconstructio... Evolutionary computation is a kind of adaptive non--numerical computation method which is designed tosimulate evolution of nature. In this paper, evolutionary algorithm behavior is described in terms of theconstruction and evolution of the sampling distributions over the space of candidate solutions. Iterativeconstruction of the sampling distributions is based on the idea of the global random search of generationalmethods. Under this frame, propontional selection is characterized as a gobal search operator, and recombination is characerized as the search process that exploits similarities. It is shown-that by properly constraining the search breadth of recombination operators, weak convergence of evolutionary algorithms to aglobal optimum can be ensured. 展开更多
关键词 global random search evolutionary algorithms weak convergence genetic algorithms
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Optimization of Reliability–Redundancy Allocation Problems: A Review of the Evolutionary Algorithms 被引量:1
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作者 Haykel Marouani Omar Al-mutiri 《Computers, Materials & Continua》 SCIE EI 2022年第4期537-571,共35页
The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field.New algorithms are continually being designed on the basis of observations of nature,wildlife,and humani... The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field.New algorithms are continually being designed on the basis of observations of nature,wildlife,and humanity.In this paper,we review eight major evolutionary algorithms that emulate the behavior of civilization,ants,bees,fishes,and birds(i.e.,genetic algorithms,bee colony optimization,simulated annealing,particle swarm optimization,biogeography-based optimization,artificial immune system optimization,cuckoo algorithm and imperialist competitive algorithm).We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation problems.Results from a literature survey show the best results found for series,series–parallel,bridge,and applied case problems(e.g.,overspeeding gas turbine benchmark).Review of literature from recent years indicates an extensive improvement in the algorithm reliability performance.However,this improvement has been difficult to achieve for high-reliability applications.Insights and future challenges in reliability–redundancy allocation problems optimization are also discussed in this paper. 展开更多
关键词 RELIABILITY REDUNDANCY evolutionary algorithms
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Evolutionary Algorithms in Software Defined Networks: Techniques, Applications, and Issues 被引量:1
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作者 LIAO Lingxia Victor C.M.Leung LAI Chin-Feng 《ZTE Communications》 2017年第3期20-36,共17页
A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and o... A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and optimization problems are typicallyvery complex with a huge solution space, large number of variables, and multiple objectives. Heuristic algorithms can solve theseproblems in an acceptable time but are usually limited to some particular problem circumstances. On the other hand, evolutionaryalgorithms(EAs), which are general stochastic algorithms inspired by the natural biological evolution and/or social behavior of species, can theoretically be used to solve any complex optimization problems including those found in SDNs. This paper reviewsfour types of EAs that are widely applied in current SDNs: Genetic Algorithms(GAs), Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO), and Simulated Annealing(SA) by discussing their techniques, summarizing their representative applications, and highlighting their issues and future works. To the best of our knowledge, our work is the first that compares the tech-niques and categorizes the applications of these four EAs in SDNs. 展开更多
关键词 SDN evolutionary algorithms Genetic algorithms Particle Swarm Optimization Ant Colony Optimization Simulated Annealing
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CONVERGENCE RATES FOR A CLASS OF EVOLUTIONARY ALGORITHMS WITH ELITIST STRATEGY
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作者 丁立新 康立山 《Acta Mathematica Scientia》 SCIE CSCD 2001年第4期531-540,共10页
This paper discusses the convergence rates about a class of evolutionary algorithms in general search spaces by means of the ergodic theory in Markov chain and some techniques in Banach algebra. Under certain conditio... This paper discusses the convergence rates about a class of evolutionary algorithms in general search spaces by means of the ergodic theory in Markov chain and some techniques in Banach algebra. Under certain conditions that transition probability functions of Markov chains corresponding to evolutionary algorithms satisfy, the authors obtain the convergence rates of the exponential order. Furthermore, they also analyze the characteristics of the conditions which can be met by genetic operators and selection strategies. 展开更多
关键词 convergence rate Markov chain Banach algebra genetic operator elitist selection evolutionary algorithms
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Hierarchical Evolutionary Algorithms and Its Application in Transonic Airfoil Optimization in Aerodynamics
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作者 王江峰 伍贻兆 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2003年第1期1-6,共6页
Hierarchical evolutionary algorithms based on genetic algorithms (GAs) and Nash strategy of game theory are proposed to accelerate the optimization process and implemented in transonic aerodynamic shape optimization p... Hierarchical evolutionary algorithms based on genetic algorithms (GAs) and Nash strategy of game theory are proposed to accelerate the optimization process and implemented in transonic aerodynamic shape optimization problems Inspired from the natural evolution history that different periods with certain environments have different criteria for the evaluations of individuals’ fitness, a hierarchical fidelity model is introduced to reach high optimization efficiency The shape of an NACA0012 based airfoil is optimized in maximizing the lift coefficient under a given transonic flow condition Optimized results are presented and compared with the single model results and traditional GA 展开更多
关键词 transonic flow aerodynamic optimization finite element method unstructured grid hierarchical fidelity models evolutionary algorithms
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Graphical model construction based on evolutionary algorithms
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作者 Youlong YANG Yan WU Sanyang LIU 《控制理论与应用(英文版)》 EI 2006年第4期349-354,共6页
Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network t... Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network that fits a given dataset is a NP-hard problem, and it also needs consuming mass computational resources. This paper develops a methodology for constructing a graphical model based on Bayesian Dirichlet metric. Our approach is derived from a set of propositions and theorems by researching the local metric relationship of networks matching dataset. This paper presents the algorithm to construct a tree model from a set of potential solutions using above approach. This method is important not only for evolutionary algorithms based on graphical models, but also for machine learning and data mining. The experimental results show that the exact theoretical results and the approximations match very well. 展开更多
关键词 Graphical model evolutionary algorithms Bayesian network Tree models Bayesian Dirichlet metric
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Drift Analysis in Studying the Convergence and Hitting Times of Evolutionary Algorithms: An Overview
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作者 He Jun, Yao Xin1.State Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei,China 2.School of Computer Science, University of Birmingham, Birmingham B15 2TT, England 《Wuhan University Journal of Natural Sciences》 CAS 2003年第S1期143-154,共12页
This paper introduces drift analysis approach in studying the convergence and hitting times of evolutionary algorithms. First the methodology of drift analysis is introduced, which links evolutionary algorithms with M... This paper introduces drift analysis approach in studying the convergence and hitting times of evolutionary algorithms. First the methodology of drift analysis is introduced, which links evolutionary algorithms with Markov chains or supermartingales. Then the drift conditions which guarantee the convergence of evolutionary algorithms are described. And next the drift conditions which are used to estimate the hitting times of evolutionary algorithms are presented. Finally an example is given to show how to analyse hitting times of EAs by drift analysis approach. 展开更多
关键词 evolutionary algorithms CONVERGENCE hitting time drift analysis
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Research on the Mechanical Automation Technology based on Evolutionary Algorithms and Artifi cial Intelligence Theory
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作者 Mindi Duan 《International Journal of Technology Management》 2016年第7期51-53,共3页
In this paper, we conduct research on the mechanical automation technology based on the evolutionary algorithms and artifi cialintelligence theory. Intelligent control theory after 30 years of development has made gra... In this paper, we conduct research on the mechanical automation technology based on the evolutionary algorithms and artifi cialintelligence theory. Intelligent control theory after 30 years of development has made gratifying achievements. But intelligent control has notyet formed a complete and systematic theory, based on the analysis, design, and there are many important problems in the practical application.Intelligent information processing is the use of some of the experience and knowledge of information, and the combination of that upper andlower knowledge information processing method. It is expected to solve the problem of insufficient information of pathology, computationcomplexity and the problem of real-time requirements, using the mathematical model is diffi cult to describe the nonlinear problem, etc. Underthis basis, this paper proposes the new mechanical automation technology based on the evolutionary algorithms and artifi cial intelligence theoryto propose the new perspective of dealing with the related challenges. 展开更多
关键词 Mechanical Automation evolutionary algorithms Artifi cial Intelligence Theory.
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A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts 被引量:24
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作者 Yicun Hua Qiqi Liu +1 位作者 Kuangrong Hao Yaochu Jin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第2期303-318,I0001-I0004,共20页
Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remed... Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remedy this issue,a large body of research has been performed in recent years and many new algorithms have been proposed.This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts.We start with a brief introduction to the basic concepts,followed by a summary of the benchmark test problems with irregular problems,an analysis of the causes of the irregularity,and real-world optimization problems with irregular Pareto fronts.Then,a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses.Finally,open challenges are pointed out and a few promising future directions are suggested. 展开更多
关键词 evolutionary algorithm machine learning multi-objective optimization problems(MOPs) irregular Pareto fronts
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Multi-objective Evolutionary Algorithms for MILP and MINLP in Process Synthesis 被引量:7
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作者 石磊 姚平经 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2001年第2期173-178,共6页
Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitnes... Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis. 展开更多
关键词 multi-objective programming multi-objective evolutionary algorithm steady-state non-dominated sorting genetic algorithm process synthesis
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