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Theory Evolution Optimization:A Metaheuristic Algorithm BaSed on Evolution Process of Theory 认领 引用
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作者 Jiacong Liu Jiaze Tu +5 位作者 Chunguang Bi Huiling Chen Ali Asghar Heidari Hao Xie Lei Liu Yi Chen 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1015-1060,共46页
Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods.To advance the field’s focus on algorithmic performance,this study introduces the T... Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods.To advance the field’s focus on algorithmic performance,this study introduces the Theory Evolution Optimization(TEO)–an efficient metaheuristic inspired by the evolution of scientific theory.TEO simulates the competitive,accumulative,and replacement processes among scientific hypotheses,mirroring the evolution from a hypothesis to an established scientific theory.The performance of TEO is validated through extensive experimental simulations and benchmarked against 28 popular algorithms,including highly competitive champions such as EBOwithCMAR,LSHADE_cnEpSi,and LSHADE.Pairwise comparisons between TEO and the latest algorithms are conducted using the Wilcoxon signed-rank test,with multiple comparisons managed by the Friedman test.Initially,TEO is tested on the classical IEEE CEC2017 and the latest IEEE CEC2022 benchmark functions.TEO successfully addresses four prominent engineering design problems in constrained continuous space for practical applications.Additionally,a binary TEO(BTEO)variant is introduced and applied to feature selection tasks in discrete space.Experimental results consistently demonstrate that TEO proposes highly competitive outcomes in optimization problems.The source codes for this research are accessible to the public at http://gffzze767f4cc5ce545d8s99nkp5pfou9c60bo.ffgz.tsg.suse.edu.cn/TEO.html. 展开更多
关键词 Optimization,metaheuristic algorithms Evolution of scientific theory Theory evolution optimizers Engineering design optimization Feature selection
Collaborative optimization of well operations and adjustment strategies in waterflooding reservoirs using an enhanced adaptive differential evolution algorithm 认领 引用
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作者 Xian-Min Zhang Jian-Gang Yang +2 位作者 Qi-Hong Feng Ya-Wei Hou Lei Zhang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2735-2757,共23页
Efficient optimization of well operations andadjustment strategies in large-scale waterflooding reservoirs is a high-dimensional and complex challenge due to strong decision coupling and reservoir heterogeneity.This s... Efficient optimization of well operations andadjustment strategies in large-scale waterflooding reservoirs is a high-dimensional and complex challenge due to strong decision coupling and reservoir heterogeneity.This study proposes a collaborative optimization framework that integrates multiple adjustment strategies,includinginfillwell drilling,shut-in of low-efficiency wells,and injectionproduction well conversion.A penalty mechanism is introduced tobalance cumulative oil production maximization with minimum production constraints for infill wells.The core contribution is the development of a multi-strategy enhancedadaptive differential evolution algorithm(E-ADE),which incorporates the follower update mechanism of the SparrowSearch Algorithm(SSA)and the logarithmic spiral search strategy of the Whale Optimization Algorithm(WOA)into the differential evolution(DE)framework.By dynamically adjusting differential evolution vectors and adaptively regulating population size across optimization stages,E-ADE effectively balances global exploration and local exploitation,leading to significantlyimproved convergence speed and optimization accuracy.Benchmark tests on nine multimodalfunctions demonstrate that E-ADEconsistently outperforms classical algorithms,includingDE,GA,PSO,WOA,and SSA.The method is further applied to the PUNQ-S3 reservoir model and the S4 block of the W12-2 oilfield under high water-cut conditions.The results indicate that E-ADE enables adaptive optimization of infillwell placement,shut-in schemes,and welltype conversions,achieving coordinated improvements in both field-scale production andsingle-well performance,and substantially enhancing the efficiency of waterflooding development. 展开更多
关键词 Waterflooding reservoir Collaborative optimization Enhanced adaptive differential evolution algorithm Logarithmic spiral search Shannon entropy
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Prediction of laser welding deformation using a deep learning model optimized by a differential evolution algorithm 认领 引用
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作者 Lihong Cheng Yue Li +2 位作者 Jianfeng Wang Chao Ma Xiaohong Zhan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期236-248,共13页
Welding deformation adversely affects the quality and precision of structural components,and traditional methods require significant material resources and time.Machine learning has demonstrated exceptional ac-curacy ... Welding deformation adversely affects the quality and precision of structural components,and traditional methods require significant material resources and time.Machine learning has demonstrated exceptional ac-curacy and efficiency in solving complex problems.Thus,the use of machine learning to predict welding de-formations is a novel approach.In this study,laser welding experiments were conducted on a TC4 titanium alloy to establish a welding deformation dataset.The deep neural network(DNN)and convolutional neural network(CNN)models were designed and constructed,with average prediction errors of 0.85 mm and 0.94 mm on the validation set,respectively.To further optimize the network parameters,a differential evolution algorithm was employed through mutation,crossover,and selection.The results indicated that after optimization,the pre-diction errors of the DNN and CNN models reduced to 0.75 mm and 0.85 mm,respectively.These represent accuracy improvements of 14.8%and 9.6%,respectively.The optimized models exhibited superior predictive performances for the validation set. 展开更多
关键词 Deep learning Differential evolution algorithm Laser welding deformation Ti6Al4V alloy
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Structural Reliability Analysis Based on Differential Evolution Algorithm and Hypersphere Integration 认领 引用
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作者 CHEN Zhenzhong HAN Zhuo +4 位作者 WANG Peiyu PAN Qianghua LI Xiaoke GAN Xuehui CHEN Ge 《Journal of Donghua University(English Edition)》 CAS 2026年第1期118-130,共13页
In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order relia... In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order reliability methods exhibit limited accuracy in highly nonlinear scenarios.To overcome these challenges,a novel reliability analysis strategy based on a multimodal differential evolution algorithm and a hypersphere integration method is proposed.Initially,the penalty function method is employed to reformulate the MPP search problem as a conditionally constrained optimization task.Subsequently,a differential evolution algorithm incorporating a population delineation strategy is utilized to identify all MPPs.Finally,a paraboloid equation is constructed based on the curvature of the limit-state function at the MPPs,and the failure probability of the structure is calculated by using the hypersphere integration method.The localization effectiveness of the MPPs is compared through multiple numerical cases and two engineering examples,with accuracy comparisons of failure probabilities against the first-order reliability method(FORM)and the secondorder reliability method(SORM).The results indicate that the method effectively identifies existing MPPs and achieves higher solution precision. 展开更多
关键词 reliability analysis design point positioning differential evolution algorithm hypersphere integration
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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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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering 认领 引用
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作者 Jeng-Shyang Pan Mengfei Zhang +2 位作者 Shu-Chuan Chu Xingsi Xue Václav Snášel 《Computers, Materials & Continua》 SCIE EI 2025年第4期475-496,共22页
Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their sim... Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their simplicity and efficiency.This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering performance.The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)algorithm.Firstly,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population diversity.Secondly,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence speed.Finally,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation effectively.The performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic algorithms.To further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven datasets.Experimental results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering approaches.This study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks. 展开更多
关键词 Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm
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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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Surrogate-assisted differential evolution using manifold learning-based sampling for highdimensional expensive constrained optimization problems 认领 引用 被引量：2
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作者 Teng LONG Nianhui YE +2 位作者 Rong CHEN Renhe SHI Baoshou ZHANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第7期252-270,共19页
To address the challenges of high-dimensional constrained optimization problems with expensive simulation models,a Surrogate-Assisted Differential Evolution using Manifold Learning-based Sampling(SADE-MLS)is proposed.... To address the challenges of high-dimensional constrained optimization problems with expensive simulation models,a Surrogate-Assisted Differential Evolution using Manifold Learning-based Sampling(SADE-MLS)is proposed.In SADE-MLS,differential evolution operators are executed to generate numerous high-dimensional candidate points.To alleviate the curse of dimensionality,a Manifold Learning-based Sampling(MLS)mechanism is developed to explore the high-dimensional design space effectively.In MLS,the intrinsic dimensionality of the candidate points is determined by a maximum likelihood estimator.Then,the candidate points are mapped into a low-dimensional space using the dimensionality reduction technique,which can avoid significant information loss during dimensionality reduction.Thus,Kriging surrogates are constructed in the low-dimensional space to predict the responses of the mapped candidate points.The candidate points with high constrained expected improvement values are selected for global exploration.Moreover,the local search process assisted by radial basis function and differential evolution is performed to exploit the design space efficiently.Several numerical benchmarks are tested to compare SADE-MLS with other algorithms.Finally,SADE-MLS is successfully applied to a solid rocket motor multidisciplinary optimization problem and a re-entry vehicle aerodynamic optimization problem,with the total impulse and lift to drag ratio being increased by 32.7%and 35.5%,respec-tively.The optimization results demonstrate the practicality and effectiveness of the proposed method in real engineering practices. 展开更多
关键词 Surrogate-assisted differential evolution Dimensionality reduction Solid rocket motor Re-entry vehicle Expensive constrained optimization
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Novel State of Health Estimation for Lithium-Ion Battery Based on Differential Evolution Algorithm-Extreme Learning Machine 认领 引用
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作者 LI Qingwei FU Can +2 位作者 XUE Wenli WEI Yongqiang SHEN Zhiwen 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第2期252-261,共10页
To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating t... To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine(ELM).Input weights and hidden layer biases were generated randomly in traditional ELM.To improve the estimation accuracy of ELM,the differential evolution algorithm was used to optimize these parameters in feasible solution spaces.First,incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests.Then,the ELM based on differential evolution algorithm(DE-ELM model)was used for a lithium-ion battery SOH estimation.At last,four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model.Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability. 展开更多
关键词 lithium-ion battery state of health(SOH) extreme learning machine(ELM) differential evolution(DE)algorithm
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A Surrogate-Assisted Multi-Objective Evolutionary Algorithm with Discontinuity-Inducing Variables 认领 引用
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作者 Chunlin He Guangsong Guo +2 位作者 Yong Zhang Xianfang Song Xinfang Ji 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2026年第1期349-378,共30页
In practice,some multi-objective optimization problems exhibit expensive calculation characteristic,called expensive multi-objective optimization problems(EMOPs).Most of the existing expensive multi-objective evolutio... In practice,some multi-objective optimization problems exhibit expensive calculation characteristic,called expensive multi-objective optimization problems(EMOPs).Most of the existing expensive multi-objective evolutionary algorithms do not consider the discontinuity of Pareto front(PF),their performance is not ideal when solving EMOPs with discontinuous PF.In view of this,this paper proposes a surrogate-assisted multi-objective evolutionary algorithm with discontinuity-inducing variables(VD-SAMOEA).First,a method combining density clustering and random sampling is developed to identify variables that significantly impact the discontinuity of PF.And then,based on the identified discontinuity-inducing variables,a local exploitation strategy with varying search intervals is presented to improve the algorithm’s search performance for the sparse PF segments.Further,a local surrogate model construction method based on geodesic flow kernel multi-source transfer learning is proposed to improve the accuracy of the local surrogate model on the exploited region.Moreover,a convergence-indicator-guided hybrid global search strategy is proposed to balance the diversity and convergence of the population.Finally,experimental results on 20 test functions and the carbon fiber spinning problem indicate that,compared to 11 typical algorithms,the proposed algorithm can obtain high-quality Pareto optimal solutions with a lower computational cost. 展开更多
关键词 evolutionary computation multi-objective optimization surrogate-assisted evolutionary algorithm particle swarm optimization
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Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval 认领 引用 被引量：9
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作者 Zhang Xuejun Guan Xiangmin +1 位作者 Zhu Yanbo Lei Jiaxing 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2015年第2期556-563,共8页
The continuous growth of air traffic has led to acute airspace congestion and severe delays, which threatens operation safety and cause enormous economic loss. Flight assignment is an economical and effective strategi... The continuous growth of air traffic has led to acute airspace congestion and severe delays, which threatens operation safety and cause enormous economic loss. Flight assignment is an economical and effective strategic plan to reduce the flight delay and airspace congestion by rea- sonably regulating the air traffic flow of China. However, it is a large-scale combinatorial optimiza- tion problem which is difficult to solve. In order to improve the quality of solutions, an effective multi-objective parallel evolution algorithm （MPEA） framework with dynamic migration interval strategy is presented in this work. Firstly, multiple evolution populations are constructed to solve the problem simultaneously to enhance the optimization capability. Then a new strategy is pro- posed to dynamically change the migration interval among different evolution populations to improve the efficiency of the cooperation of populations. Finally, the cooperative co-evolution （CC） algorithm combined with non-dominated sorting genetic algorithm II （NSGA-II） is intro- duced for each population. Empirical studies using the real air traffic data of the Chinese air route network and daily flight plans show that our method outperforms the existing approaches, multi- objective genetic algorithm （MOGA）, multi-objective evolutionary algorithm based on decom- position （MOEA/D）, CC-based multi-objective algorithm （CCMA） as well as other two MPEAs with different migration interval strategies. 展开更多
关键词 Air traffic flow management Cooperative co-evolution Dynamic migration intervalstrategy Flight assignment Parallel evolution algorithm
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Enhancing Urban Rail Transit Train Routes Planning Using Surrogate-Assisted Fish Migration Optimization 认领 引用
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作者 Zhigang Du Jengshyang Pan +2 位作者 Xiaoyang Wang Shuchuan Chu Shaoquan Ni 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第4期1702-1716,共15页
Meta-heuristic evolutionary algorithms have become widely used for solving complex optimization problems.However,their effectiveness in real-world applications is often limited by the need for many evaluations,which c... Meta-heuristic evolutionary algorithms have become widely used for solving complex optimization problems.However,their effectiveness in real-world applications is often limited by the need for many evaluations,which can be both costly and time-consuming.This is especially true for large-scale transportation networks,where the size of the problem and the high computational cost can hinder the algorithm’s performance.To address these challenges,recent research has focused on using surrogate-assisted models.These models aim to reduce the number of expensive evaluations and improve the efficiency of solving time-consuming optimization problems.This paper presents a new two-layer Surrogate-Assisted Fish Migration Optimization(SA-FMO)algorithm designed to tackle high-dimensional and computationally heavy problems.The global surrogate model offers a good approximation of the entire problem space,while the local surrogate model focuses on refining the solution near the current best option,improving local optimization.To test the effectiveness of the SA-FMO algorithm,we first conduct experiments using six benchmark functions in a 50-dimensional space.We then apply the algorithm to optimize urban rail transit routes,focusing on the Train Routing Optimization problem.This aims to improve operational efficiency and vehicle turnover in situations with uneven passenger flow during transit disruptions.The results show that SA-FMO can effectively improve optimization outcomes in complex transportation scenarios. 展开更多
关键词 Train routing optimization Surrogate-assisted Fish migration optimization Meta-heuristic evolutionary algorithm
Optimization of air quantity regulation in mine ventilation networks using the improved differential evolution algorithm and critical path method 认领 引用 被引量：21
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作者 Chen Kaiyan Si Junhong +3 位作者 Zhou Fubao Zhang Renwei Shao He Zhao Hongmei 《International Journal of Mining Science and Technology》 EI CAS CSCD 2015年第1期79-84,共6页
In mine ventilation networks,the reasonable airflow distribution is very important for the production safety and economy.Three basic problems of the natural,full-controlled and semi-controlled splitting were reviewed ... In mine ventilation networks,the reasonable airflow distribution is very important for the production safety and economy.Three basic problems of the natural,full-controlled and semi-controlled splitting were reviewed in the paper.Aiming at the high difficulty semi-controlled splitting problem,the general nonlinear multi-objectives optimization mathematical model with constraints was established based on the theory of mine ventilation networks.A new algorithm,which combined the improved differential evaluation and the critical path method(CPM)based on the multivariable separate solution strategy,was put forward to search for the global optimal solution more efficiently.In each step of evolution,the feasible solutions of air quantity distribution are firstly produced by the improved differential evolu-tion algorithm,and then the optimal solutions of regulator pressure drop are obtained by the CPM.Through finite steps iterations,the optimal solution can be given.In this new algorithm,the population of feasible solutions were sorted and grouped for enhancing the global search ability and the individuals in general group were randomly initialized for keeping diversity.Meanwhile,the individual neighbor-hood in the fine group which may be closely to the optimal solutions were searched locally and slightly for achieving a balance between global searching and local searching,thus improving the convergence rate.The computer program was developed based on this method.Finally,the two ventilation networks with single-fan and multi-fans were solved.The results show that this algorithm has advantages of high effectiveness,fast convergence,good robustness and flexibility.This computer program could be used to solve lar^e-scale~eneralized ventilation networks o^timization problem in the future. 展开更多
关键词 Mine ventilation network Differential evolution algorithm Critical path method Population group and neighborhood search Multivariable separate solution
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Chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with Alopex 认领 引用 被引量：6
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作者 范勤勤 吕照民 +1 位作者 颜学峰 郭美锦 《Journal of Central South University》 SCIE EI CAS 2013年第4期950-959,共10页
To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individua... To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained. 展开更多
关键词 evolutionary computation dynamic optimization differential evolution algorithm Alopex algorithm self-adaptivity
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Multi-objective Optimization of a Parallel Ankle Rehabilitation Robot Using Modified Differential Evolution Algorithm 认领 引用 被引量：18
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作者 WANG Congzhe FANG Yuefa GUO Sheng 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2015年第4期702-715,共14页
Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitati... Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution（DE） algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements. 展开更多
关键词 ankle rehabilitation parallel robot multi-objective optimization differential evolution algorithm
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Novel Control Vector Parameterization Method with Differential Evolution Algorithm and Its Application in Dynamic Optimization of Chemical Processes 认领 引用 被引量：7
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作者 孙帆 钟伟民 +1 位作者 程辉 钱锋 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2013年第1期64-71,共8页
Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been w... Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution （DE） algorithm and control vector parameteri- zation （CVP） is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the oroposed methods. 展开更多
关键词 control vector pararneterization differential evolution algorithm dynamic optimization chemical processes
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A comparative study of differential evolution and genetic algorithms for optimizing the design of water distribution systems 认领 引用 被引量：5
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作者 Xiao-lei DONG Sui-qing LIU +2 位作者 Tao TAO Shu-ping LI Kun-lun XIN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2012年第9期674-686,共13页
The differential evolution （DE） algorithm has been received increasing attention in terms of optimizing the design for the water distribution systems （WDSs）. This paper aims to carry out a comprehensive performari... The differential evolution （DE） algorithm has been received increasing attention in terms of optimizing the design for the water distribution systems （WDSs）. This paper aims to carry out a comprehensive performarice comparison between the new emerged DE algorithm and the most popular algorithm-the genetic algorithm （GA）. A total of six benchmark WDS case studies were used with the number of decision variables ranging from 8 to 454. A preliminary sensitivity analysis was performed to select the most effective parameter values for both algorithms to enable the fair comparison. It is observed from the results that the DE algorithm consistently outperforms the GA in terms of both efficiency and the solution quality for each case study. Additionally, the DE algorithm was also compared with the previously published optimization algorithms based on the results for those six case studies, indicating that the DE exhibits comparable performance with other algorithms. It can be concluded that the DE is a newly promising optimization algorithm in the design of WDSs. 展开更多
关键词 Differential evolution （DE） Genetic algorithms （GAs） Optimization Water distribution systems （WDSs）
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Improved differential evolution algorithm for resource-constrained project scheduling problem 认领 引用 被引量：4
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作者 Lianghong Wu Yaonan Wang Shaowu Zhou 《Journal of Systems Engineering and Electronics》 SCIE EI 2010年第5期798-805,共8页
An improved differential evolution（IDE）algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem（RCPSP）with the obj... An improved differential evolution（IDE）algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem（RCPSP）with the objective of minimizing project duration Activities priorities for scheduling are represented by individual vectors and a senal scheme is utilized to transform the individual-represented priorities to a feasible schedule according to the precedence and resource constraints so as to be evaluated.To investigate the performance of the IDE-based approach for the RCPSP,it is compared against the meta-heuristic methods of hybrid genetic algorithm（HGA）,particle swarm optimization（PSO） and several well selected heuristics.The results show that the proposed scheduling method is better than general heuristic rules and is able to obtain the same optimal result as the HGA and PSO approaches but more efficient than the two algorithms. 展开更多
关键词 differential evolution algorithm project soheduling resource constraint priority-based scheduling.
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Fault Reconfiguration of Shipboard Power System Based on Triple Quantum Differential Evolution Algorithm 认领 引用 被引量：5
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作者 王丛佼 王锡淮 +2 位作者 肖健梅 陈晶 张思全 《Journal of Shanghai Jiaotong university(Science)》 EI 2016年第4期433-442,共10页
Fault reconfiguration of shipboard power system is viewed as a typical nonlinear and multi-objective combinatorial optimization problem. A comprehensive reconfiguration model is presented in this paper, in which the r... Fault reconfiguration of shipboard power system is viewed as a typical nonlinear and multi-objective combinatorial optimization problem. A comprehensive reconfiguration model is presented in this paper, in which the restored loads, switch frequency and generator efficiency are taken into account. In this model, analytic hierarchy process(AHP) is proposed to determine the coefficients of these objective functions. Meanwhile, a quantum differential evolution algorithm with triple quantum bit code is proposed. This algorithm aiming at the characteristics of shipboard power system is different from the normal quantum bit representation. The individual polymorphic expression is realized, and the convergence performance can be further enhanced in combination with the global parallel search capacity of differential evolution algorithm and the superposition properties of quantum theory. The local optimum can be avoided by dynamic rotation gate. The validity of algorithm and model is verified by the simulation examples. 展开更多
关键词 quantum differential evolution algorithm ternary coding dynamic rotation gate shipboard power system fault reconfiguration
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Vector Dominating Multi-objective Evolution Algorithm for Aerodynamic-Structure Integrative Design of Wind Turbine Blade 认领 引用 被引量：1
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作者 Wang Long Wang Tongguang +1 位作者 Wu Jianghai Ke Shitang 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2016年第1期1-8,共8页
A novel multi-objective optimization algorithm incorporating vector method and evolution strategies,referred as vector dominant multi-objective evolutionary algorithm(VD-MOEA),is developed and applied to the aerodynam... A novel multi-objective optimization algorithm incorporating vector method and evolution strategies,referred as vector dominant multi-objective evolutionary algorithm(VD-MOEA),is developed and applied to the aerodynamic-structural integrative design of wind turbine blades.A set of virtual vectors are elaborately constructed,guiding population to fast move forward to the Pareto optimal front and dominating the distribution uniformity with high efficiency.In comparison to conventional evolution algorithms,VD-MOEA displays dramatic improvement of algorithm performance in both convergence and diversity preservation when handling complex problems of multi-variables,multi-objectives and multi-constraints.As an example,a 1.5 MW wind turbine blade is subsequently designed taking the maximum annual energy production,the minimum blade mass,and the minimum blade root thrust as the optimization objectives.The results show that the Pareto optimal set can be obtained in one single simulation run and that the obtained solutions in the optimal set are distributed quite uniformly,maximally maintaining the population diversity.The efficiency of VD-MOEA has been elevated by two orders of magnitude compared with the classical NSGA-II.This provides a reliable high-performance optimization approach for the aerodynamic-structural integrative design of wind turbine blade. 展开更多
关键词 wind turbine multi-objective optimization vector method evolution algorithm
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