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Hyper-heuristic Evolutionary Algorithm Utilizing Q-learning for Addressing the Distributed Flexible Job-Shop Scheduling Problem in the Context of Worker Absenteeism 认领 引用
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作者 Zhiqing Li Yongquan Zhou Qifang Luo 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1932-1969,共38页
The Distributed Flexible Job Shop Scheduling Problem(DFJSP)is critical in modern manufacturing;however,existing research has not sufficiently addressed dynamic disturbances,particularly unexpected worker absences.This... The Distributed Flexible Job Shop Scheduling Problem(DFJSP)is critical in modern manufacturing;however,existing research has not sufficiently addressed dynamic disturbances,particularly unexpected worker absences.This study extends the DFJSPW model to introduce an enhanced framework,DFJSPWA,which optimizes maximum makespan,worker workload,and total energy consumption by integrating worker load factors with random absenteeism.To solve this complex problem,we propose a Q-learning-based Hyper-heuristic Evolutionary Algorithm(QLHHEA).This algorithm features a segmented encoding scheme that implicitly captures absenteeism information,utilizing a decoding process tailored for both standard and rescheduling contexts.Additionally,we construct a pool of twelve efficient Low-Level Heuristics(LLHs)combined with Q-learning to enable the adaptive selection of operators.Furthermore,a Hybrid Rescheduling Method(HRM)is developed,employing three response strategies based on worker status and the urgency of the absenteeism.Comprehensive experiments on 58 benchmark instances demonstrate that QLHHEA significantly outperforms six established algorithms,including MOEA/D and NSGA-II.Statistical validation confirms the superiority of the proposed method.This research provides a robust theoretical and methodological framework for improving scheduling efficiency and resource utilization in distributed production systems facing worker absenteeism. 展开更多
关键词 Worker absenteeism Distributed flexible job shop scheduling problem Hyper-heuristic evolutionary algorithm Dynamic rescheduling Q-learning
A Review of the Evolution of Multi-Objective Evolutionary Algorithms 认领 引用 被引量：1
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作者 Thomas Hanne Mohammad Jahani Moghaddam 《Computers, Materials & Continua》 SCIE EI 2025年第12期4203-4236,共34页
Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review exp... Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential. 展开更多
关键词 Multi-objective optimization evolutionary algorithms Pareto-based selection decomposition-based methods advanced analytics
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Many-objective evolutionary algorithms based on reference-point-selection strategy for application in reactor radiation-shielding design 认领 引用
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作者 Cheng-Wei Liu Ai-Kou Sun +4 位作者 Ji-Chong Lei Hong-Yu Qu Chao Yang Tao Yu Zhen-Ping Chen 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第6期201-215,共15页
In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding struct... In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding structures typically need to be lightweight,miniaturized,and radiation-protected,which is a multi-parameter and multi-objective optimization problem.The conventional multi-objective(two or three objectives)optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters,as well as a deficiency in achieving a global optimal solution,thereby failing to meet the requirements of shielding optimization for newly developed reactors.In this study,genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective(having four or more objectives)optimal design of reactor radiation shielding.To validate the reliability of the methods,an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem.The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance,and they exhibit their reliability in practical engineering problems.The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes.Therefore,the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types. 展开更多
关键词 Many-objective optimization problem Evolutionary algorithm Radiation-shielding design Reference-point-selection strategy
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Evolutionary Algorithm Based on Surrogate and Inverse Surrogate Models for Expensive Multiobjective Optimization 认领 引用
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作者 Qi Deng Qi Kang +4 位作者 MengChu Zhou Xiaoling Wang Shibing Zhao Siqi Wu Mohammadhossein Ghahramani 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第5期961-973,共13页
When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by usin... When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by using designed surrogate models.The generated solutions exhibit excessive randomness,which tends to reduce the likelihood of generating good-quality solutions and cause a long evolution to the optima.To improve SAEAs greatly,this work proposes an evolutionary algorithm based on surrogate and inverse surrogate models by 1)Employing a surrogate model in lieu of expensive(true)function evaluations;and 2)Proposing and using an inverse surrogate model to generate new solutions.By using the same training data but with its inputs and outputs being reversed,the latter is simple to train.It is then used to generate new vectors in objective space,which are mapped into decision space to obtain their corresponding solutions.Using a particular example,this work shows its advantages over existing SAEAs.The results of comparing it with state-of-the-art algorithms on expensive optimization problems show that it is highly competitive in both solution performance and efficiency. 展开更多
关键词 Expensives multi-objective optimization reverse model surrogate-assisted evolutionary algorithms(SAEAs)
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Multi-Firmware Comparison Based on Evolutionary Algorithm and Trusted Base Point 认领 引用
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作者 Wenbing Wang Yongwen Liu 《Computers, Materials & Continua》 SCIE EI 2025年第7期763-790,共28页
Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi... Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi-firmware comparison method based on evolutionary algorithms and trusted base points.We first model the multi-firmware comparison as a multi-sequence matching problem.Then,we propose an adaptation function and a population generation method based on trusted base points.Finally,we apply an evolutionary algorithm to find the optimal result.At the same time,we design the similarity of matching results as an evaluation metric to measure the effect of multi-firmware comparison.The experiments show that the proposed method outperforms Bindiff and the string-based method.Precisely,the similarity between the matching results of the proposed method and Bindiff matching results is 61%,and the similarity between the matching results of the proposed method and the string-based method is 62.8%.By sampling and manual verification,the accuracy of the matching results of the proposed method can be about 66.4%. 展开更多
关键词 Multi-firmware comparison evolutionary algorithm multi-sequence matching binary code comparison
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A Bi-Level Optimization Model and Hybrid Evolutionary Algorithm for Wind Farm Layout with Different Turbine Types 认领 引用
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作者 Erping Song Zipin Yao 《Energy Engineering》 EI 2025年第12期5129-5147,共19页
Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and eco... Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm,where the wake effect,wind speed,types of wind turbines,etc.,have an impact on the output power of the wind farm.To solve the optimization problem of wind farm layout under complex terrain conditions,this paper proposes wind turbine layout optimization using different types of wind turbines,the aim is to reduce the influence of the wake effect and maximize economic benefits.The linear wake model is used for wake flow calculation over complex terrain.Minimizing the unit energy cost is taken as the objective function,considering that the objective function is affected by cost and output power,which influence each other.The cost function includes construction cost,installation cost,maintenance cost,etc.Therefore,a bi-level constrained optimization model is established,in which the upper-level objective function is to minimize the unit energy cost,and the lower-level objective function is to maximize the output power.Then,a hybrid evolutionary algorithm is designed according to the characteristics of the decision variables.The improved genetic algorithm and differential evolution are used to optimize the upper-level and lower-level objective functions,respectively,these evolutionary operations search for the optimal solution as much as possible.Finally,taking the roughness of different terrain,wind farms of different scales and different types of wind turbines as research scenarios,the optimal deployment is solved by using the algorithm in this paper,and four algorithms are compared to verify the effectiveness of the proposed algorithm. 展开更多
关键词 Bi-level optimization genetic algorithm differential evolution hybrid evolutionary algorithm wind farm layout
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Feature Selection Optimisation for Cancer Classification Based on Evolutionary Algorithms:An Extensive Review 认领 引用
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作者 Siti Ramadhani Lestari Handayani +4 位作者 Theam Foo Ng Sumayyah Dzulkifly Roziana Ariffin Haldi Budiman Shir Li Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第6期2711-2765,共55页
In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classificati... In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency. 展开更多
关键词 Feature selection(FS) gene expression profile(GEP) cancer classification evolutionary algorithms(EAs) dynamic-length chromosome
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A new improved Alopex-based evolutionary algorithm and its application to parameter estimation 认领 引用 被引量：1
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作者 桑志祥 李绍军 董跃华 《Journal of Central South University》 SCIE EI CAS 2013年第1期123-133,共11页
In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irratio... In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irrationality of the method that generated candidate solutions at each iteration of AEA, clonal selection algorithm could be applied to improve the method. The performance of the proposed new algorithm was studied by using 22 benchmark functions and was compared with original AEA given the same conditions. The experimental results show that the AEA-C clearly outperforms the original AEA for almost all the 22 benchmark functions with 10, 30, 50 dimensions in success rates, solution quality and stability. Furthermore, AEA-C was applied to estimate 6 kinetics parameters of the fermentation dynamics models. The standard deviation of the objective function calculated by the AEA-C is 41.46 and is far less than that of other literatures' results, and the fitting curves obtained by AEA-C are more in line with the actual fermentation process curves. 展开更多
关键词 Alopex evolutionary algorithm Alopex-based evolutionary algorithm clone selection parameter estimation
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Cell Evolutionary Algorithm:a New Optimization Method on Ground-State Energy of the Atomic 认领 引用
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作者 Liu Lian-jun Xu Jing-wenMao You-dong Li Yuan-xiang 《Wuhan University Journal of Natural Sciences》 EI CAS 2000年第4期425-430,共6页
The purpose of this paper is to present a new general approach to solve ground-state energies of the double-electron systems in a uniform magnetic field,in which the basic element of evolution is the set in the soluti... The purpose of this paper is to present a new general approach to solve ground-state energies of the double-electron systems in a uniform magnetic field,in which the basic element of evolution is the set in the solution space,rather than the point.The paper defines the Cell Evolutionary Algorithm,which implements such a view of the evolution mechanism.First,the optimal set in which the optimal solution may be obtained.Then this approach applies the embedded search method to get the optimal solution.We tested this approach on the atomic structure,and the results show that it can improve not only the efficiency but also the accuracy of the calculations as it relates to this specific problem. 展开更多
关键词 double electron systems ground state energy variational principle cell evolutionary algorithm evolutionary algorithm
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A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts 认领 引用 被引量：53
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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16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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Solving material distribution routing problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm 认领 引用 被引量：7
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作者 高贵兵 张国军 +2 位作者 黄刚 朱海平 顾佩华 《Journal of Central South University》 SCIE EI CAS 2012年第2期433-442,共10页
The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency... The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II. 展开更多
关键词 material distribution routing problem multi-objective optimization evolutionary algorithm local search
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Managing Software Testing Technical Debt Using Evolutionary Algorithms 认领 引用 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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Dose reconstruction with Compton camera during proton therapy via subset-driven origin ensemble and double evolutionary algorithm 认领 引用 被引量：4
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作者 Zhi-Yang Yao Yong-Shun Xiao Ji-Zhong Zhao 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第4期135-148,共14页
Compton camera-based prompt gamma(PG) imaging has been proposed for range verification during proton therapy. However, a deviation between the PG and dose distributions, as well as the difference between the reconstru... Compton camera-based prompt gamma(PG) imaging has been proposed for range verification during proton therapy. However, a deviation between the PG and dose distributions, as well as the difference between the reconstructed PG and exact values, limit the effectiveness of the approach in accurate range monitoring during clinical applications. The aim of the study was to realize a PG-based dose reconstruction with a Compton camera, thereby further improving the prediction accuracy of in vivo range verification and providing a novel method for beam monitoring during proton therapy. In this paper, we present an approach based on a subset-driven origin ensemble with resolution recovery and a double evolutionary algorithm to reconstruct the dose depth profile(DDP) from the gamma events obtained by a cadmium-zinc-telluride Compton camera with limited position and energy resolution. Simulations of proton pencil beams with clinical particle rate irradiating phantoms made of different materials and the CT-based thoracic phantom were used to evaluate the feasibility of the proposed method. The results show that for the monoenergetic proton pencil beam irradiating homogeneous-material box phantom,the accuracy of the reconstructed DDP was within 0.3 mm for range prediction and within 5.2% for dose prediction. In particular, for 1.6-Gy irradiation in the therapy simulation of thoracic tumors, the range deviation of the reconstructed spreadout Bragg peak was within 0.8 mm, and the relative dose deviation in the peak area was less than 7% compared to the exact values. The results demonstrate the potential and feasibility of the proposed method in future Compton-based accurate dose reconstruction and range verification during proton therapy. 展开更多
关键词 Prompt gamma imaging Dose reconstruction Range verification Origin ensemble Compton camera Evolutionary algorithm
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Multi-objective Evolutionary Algorithms for MILP and MINLP in Process Synthesis 认领 引用 被引量：6
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作者 石磊 姚平经 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 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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Fuzzy traffic signal control with DNA evolutionary algorithm 认领 引用 被引量：2
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作者 毕云蕊 路小波 +1 位作者 孙哲 曾唯理 《Journal of Southeast University(English Edition)》 EI CAS 2013年第2期207-210,共4页
In order to optimize the signal control system, this paper proposes a method to design an optimized fuzzy logic controller （FLC） with the DNA evolutionary algorithm. Inspired by the DNA molecular operation character... In order to optimize the signal control system, this paper proposes a method to design an optimized fuzzy logic controller （FLC） with the DNA evolutionary algorithm. Inspired by the DNA molecular operation characteristics, the DNA evolutionary algorithm modifies the corresponding genetic operators. Compared with the traditional genetic algorithm （GA）, the DNA evolutionary algorithm can overcome weak local search capability and premature convergence. The parameters of membership functions are optimized by adopting the quaternary encoding method and performing corresponding DNA genetic operators. The relevant optimized parameters are combined with the FLC for single intersection traffic signal control. Simulation experiments shows the better performance of the FLC with the DNA evolutionary algorithm optimization. The experimental results demonstrate the efficiency of the nrotmsed method. 展开更多
关键词 DNA evolutionary algorithm genetic algorithm（GA） fuzzy control traffic signal control
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Application of Particle Swarm Algorithm in the Optimal Allocation of Regional Water Resources Based on Immune Evolutionary Algorithm 认领 引用 被引量：6
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作者 屈国栋 楼章华 《Journal of Shanghai Jiaotong university(Science)》 EI 2013年第5期634-640,共7页
The optimal allocation model of regional water resources is built with the purpose of maximizing the comprehensive economic,social and environmental benefits of regional water consumption.In order to solve the problem... The optimal allocation model of regional water resources is built with the purpose of maximizing the comprehensive economic,social and environmental benefits of regional water consumption.In order to solve the problems that easily appear during the model solution of regional water resource optimal allocation with multiple water sources,multiple users and multiple objectives like"curse of dimensionality"or sinking into local optimum,this paper proposes a particle swarm optimization(PSO)algorithm based on immune evolutionary algorithm(IEA).This algorithm introduces immunology principle into particle swarm algorithm.Its immune memorizing and self-adjusting mechanism is utilized to keep the particles in the fitness level at a certain concentration and guarantee the diversity of population.Also,the global search characteristics of IEA and the local search capacity of particle swarm algorithm have been fully utilized to overcome the dependence of PSO on initial swarm and the deficiency of vulnerability to local optimum.After applying this model to the allocation of water resources in Zhoukou,we obtain the scheme for optimization allocation of water resources in the planning level years,i.e.2015and 2025 under the guarantee rate of 50%.The calculation results indicate that the application of this algorithm to solve the issue of optimal allocation of regional water resources is reliable and reasonable.Thus it ofers a new idea for solving the issue of optimal allocation of water resources. 展开更多
关键词 immune evolutionary algorithm(IEA) particle swarm optimization(PSO) water resources optimal allocation
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MULTIOBJECT OPTIMIZATION OF A CENTRIFUGAL IMPELLER USING EVOLUTIONARY ALGORITHMS 认领 引用 被引量：3
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作者 LiJun LiuLijun FengZhenping 《Chinese Journal of Mechanical Engineering》 EI CAS 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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Hybrid evolutionary algorithm for no-wait flow shops to minimize makespan and total flowtime 认领 引用 被引量：2
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作者 廖小平 刘有根 李小平 《Journal of Southeast University(English Edition)》 EI CAS 2008年第4期450-454,共5页
The NP-hard no-wait flow shop scheduling problems with makespan and total flowtime minimization are considered. Objective increment properties of the problems are analyzed. A non-dominated classification method is int... The NP-hard no-wait flow shop scheduling problems with makespan and total flowtime minimization are considered. Objective increment properties of the problems are analyzed. A non-dominated classification method is introduced to class population individuals into Pareto fronts to improve searching efficiency. Besides investigating the crowding distance and the elitist solution strategy, two effective bi-criteria local search procedures based on objective increments are presented to improve searching effectiveness. Based on the properties and methods, a hybrid evolutionary algorithm is proposed for the considered problems and compared with the best existing algorithms. Experimental results show that the proposed algorithm is effective with high efficiency. 展开更多
关键词 no-wait flow shop objective increment makespan total flowtime evolutionary algorithm
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Lake Eutrophic Evaluation Based on Bee Immune Evolutionary Algorithm 认领 引用 被引量：4
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作者 党媛 李祚泳 邹艳玲 《Agricultural Science & Technology》 CAS 2010年第4期156-158,188,共3页
In order to establish the lake eutrophic evaluation model for multiple indices,based on the gauge transformation,an index formula in the form of a logarithmic power function was proposed to design an eutrophic evaluat... In order to establish the lake eutrophic evaluation model for multiple indices,based on the gauge transformation,an index formula in the form of a logarithmic power function was proposed to design an eutrophic evaluation model for the ＂ normalized values＂ of multi-indexes.The parameters in the formula were also optimized by bee immune evolutionary algorithm（BEIEA）.The universal index formula was suitable to multiindices items for eutrophic evaluation.At the same time,the formula was applied to practical eutrophic evaluations in 10 regions of Dong Lake.The evaluation results were coincident with those obtained from the power function of weighted sums and also with actual conditions.It was shown that the bee immune evolutionary algorithm was suitable to the parameter optimization in the eutrophic evaluation model. 展开更多
关键词 Lake Eutrophic evaluation Bee algorithm Bee immune evolutionary algorithm Parameter optimization
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Adaptive Immune Evolutionary Algorithms Based on Immune Network Regulatory Mechanism 认领 引用 被引量：3
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作者 何宏 钱锋 《Journal of Donghua University(English Edition)》 EI CAS 2007年第1期141-145,共5页
Based on immune network regulatory mechanism, a new adaptive immune evolutionary algorithm (AIEA) is proposed to improve the performance of genetic algorithms (GA) in this paper. AIEA adopts novel selection operation ... Based on immune network regulatory mechanism, a new adaptive immune evolutionary algorithm (AIEA) is proposed to improve the performance of genetic algorithms (GA) in this paper. AIEA adopts novel selection operation according to the stimulation level of each antibody. A memory base for good antibodies is devised simultaneously to raise the convergent rapidity of the algorithm and adaptive adjusting strategy of antibody population is used for preventing the loss of the population adversity. The experiments show AIEA has better convergence performance than standard genetic algorithm and is capable of maintaining the adversity of the population and solving function optimization problems in an efficient and reliable way. 展开更多
关键词 evolutionary algorithm immune network adaptation stimulation level.
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