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An evolutionary Algorithm to Optimization of Discrete Problem Based on Pheromone

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Wuhan University Journal of Natural Sciences 2001年第Z1期6卷 606-610页

作者： Huang Zhang can 1, Wu Fang cai 2, Chen Si duo 2 1.Department of Mathematics,Eastern Campus Wuhan University of Technology, Wuhan 430070, China 2.Department of Automation, Eastern Campus Wuhan University of Technology, Wuhan 430070,China Department of Mathematics Eastern Campus Wuhan University of Technology Wuhan China Department of Automation Eastern Campus Wuhan University of Technology Wuhan China

The pheromone based positive feedback approach of ant algorithm is introduced in evolutionary computation of discrete problem, so as to accomplish the optimization of each allele. It ensures stable converge of the algorithm into global optimum. The optimal cutting problem is studied as an example to analyze the performance of the algorithm. The experimental results show the novel performance of the algorithm in the optimization of discrete problem.

关键词： pheromone Ant System evolutionary computation

An evolutionary Real-Time 3D Route Planner for Aircraft

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Journal of Systems Engineering and Electronics 2003年第1期14卷 47-53页

作者：郑昌文丁明跃周成平 Institute of Pattern Recognition and Artificial Intelligence State Education Commission Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 P. R. China

A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not required. By using problem-specific chromosome structure and genetic operators, the routes are generated in real time, with different mission constraints such as minimum route leg length and flying altitude, maximum turning angle, maximum climbing/diving angle and route distance constraint taken into account.

关键词： evolutionary computation Route planning Route constraints Real time Aircraft.

computation model and improved ACO algorithm for p//T

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Journal of Systems Engineering and Electronics 2009年第6期20卷 1336-1343页

作者： Yi Yang Lai Jieling School of Information Science & Technology Sun Yat-sen Univ. Guangzhou 510275 P. R. China

Scheduling jobs on parallel machines to minimize the total tardiness（p//T） is proved to be NP hard.A new ant colony algorithm to deal with p//T（p//T ACO） is addressed, and the computing model of mapping p//T to the ant colony optimization environment is ***, based on the academic researches on p//T, some new properties used in the evolutionary computation are analyzed and *** theoretical analysis and comparative experiments demonstrate that p//T ACO has much better performance and can be used to solve practical large scale problems efficiently.

关键词： scheduling evolutionary computation ant colony optimization.

Zoning Search With Adaptive Resource Allocating Method for Balanced and Imbalanced Multimodal Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1163-1176页

作者： Qinqin Fan Okan K.Ersoy Logistics Research Center Shanghai Maritime UniversityShanghai 201306China School of Electronic and Computer Engineering Purdue UniversityWest LafayetteIN 47906 USA

Maintaining population diversity is an important task in the multimodal multi-objective *** the zoning search(ZS)can improve the diversity in the decision space,assigning the same computational costs to each search subspace may be wasteful when computational resources are limited,especially on imbalanced *** alleviate the above-mentioned issue,a zoning search with adaptive resource allocating(ZS-ARA)method is proposed in the current *** the proposed ZS-ARA,the entire search space is divided into many subspaces to preserve the diversity in the decision space and to reduce the problem ***,the computational resources can be automatically allocated among all the *** ZS-ARA is compared with seven algorithms on two different types of multimodal multi-objective problems(MMOPs),namely,balanced and imbalanced *** results indicate that,similarly to the ZS,the ZS-ARA achieves high performance with the balanced ***,it can greatly assist a“regular”algorithm in improving its performance on the imbalanced MMOPs,and is capable of allocating the limited computational resources dynamically.

关键词： computational resource allocation decision space decomposition evolutionary computation multimodal multi-objective optimization

Exploiting Bivariate Dependencies to Speedup Structure Learning in Bayesian Optimization Algorithm

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Journal of Computer Science & Technology 2012年第5期27卷 1077-1090页

作者： Amin Nikanjam Adel Rahmani School of Computer Engineering Iran University of Science and Technology

Bayesian optimization algorithm （BOA） is one of the successful and widely used estimation of distribution algorithms （EDAs） which have been employed to solve different optimization problems. In EDAs, a model is learned from the selected population that encodes interactions among problem variables. New individuals are generated by sampling the model and incorporated into the population. Different probabilistic models have been used in EDAs to learn interactions. Bayesian network （BN） is a well-known graphical model which is used in BOA. Learning a propel model in EDAs and particularly in BOA is distinguished as a computationally expensive task. Different methods have been proposed in the literature to improve the complexity of model building in EDAs. This paper employs bivariate dependencies to learn accurate BNs in BOA efficiently. The proposed approach extracts the bivariate dependencies using an appropriate pairwise interaction-detection metric. Due to the static structure of the underlying problems, these dependencies are used in each generation of BOA to learn an accurate network. By using this approach, the computational cost of model building is reduced dramatically. Various optimization problems are selected to be solved by the algorithm. The experimental results show that the proposed approach successfully finds the optimum in problems with different types of interactions efficiently. Significant speedups are observed in the model building procedure as well.

关键词： evolutionary computation Bayesian optimization algorithm Bayesian network model building bivariate interaction

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进化计算的一种变异概率自适应方法

计算机科学 2002年第Z1期29卷 144-145,114页

作者：李枚毅蔡自兴石跃祥孙国荣蒙祖强中南大学信息科学与工程学院长沙410083

　　1引言　　进化计算的不足之一是可能陷入局部极小,变异操作正是为解决局部极小问题而使用的.若变异概率pm取值较大的话,能够产生出较多的新个体,搜索空间相应也大,进化过程中产生全局次优解(near-optimality)的机会也就会大,但也有... 详细信息

　　1引言　　进化计算的不足之一是可能陷入局部极小,变异操作正是为解决局部极小问题而使用的.若变异概率pm取值较大的话,能够产生出较多的新个体,搜索空间相应也大,进化过程中产生全局次优解(near-optimality)的机会也就会大,但也有可能破坏掉很多较好的模式,变异操作的概率过大会引起进化过程不稳定,使得遗传算法的性能近似于随机搜索算法的性能;若变异概率pm取值太小的话,则变异操作产生新个体的能力和抑制早熟现象的能力就会较差.……

关键词：进化计算变异概率自适应方法 evolutionary computation Mutation Probability 变异操作算法的性能进化过程早熟现象随机搜索搜索空间取值能力局部极小问题个体次优解遗传模式

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1168-1180页

作者： Zhenyu Lei Shangce Gao Zhiming Zhang Haichuan Yang Haotian Li The authors are with the Faculty of Engineering University of ToyamaToyama 930-8555Japan

Wind energy has been widely applied in power generation to alleviate climate *** wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in *** farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind ***,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion *** heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind ***,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO *** is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art *** experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and *** be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search *** improves the solution *** parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

关键词： Chaotic local search(CLS) evolutionary computation genetic learning particle swarm optimization(PSO) wake effect wind farm layout optimization(WFLO)

Parallel exploration via negatively correlated search

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Frontiers of Computer Science 2021年第5期15卷 123-135页

作者： Peng YANG Qi YANG Ke TANG Xin YAO Guangdong Provincial Key Laboratory of Brain-inspired Intelligent computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

Effective exploration is key to a successful search *** recently proposed negatively correlated search(NCS)tries to achieve this by coordinated parallel exploration,where a set of search processes are driven to be negatively correlated so that different promising areas of the search space can be visited *** successful applications of NCS,the negatively correlated search behaviors were mostly devised by intuition,while deeper(e.g.,mathematical)understanding is *** this paper,a more principled NCS,namely NCNES,is presented,showing that the parallel exploration is equivalent to a process of seeking probabilistic models that both lead to solutions of high quality and are distant from previous obtained probabilistic *** learning,for which exploration is of particular importance,are considered for empirical *** proposed NCNES is applied to directly train a deep convolution network with 1.7 million connection weights for playing Atari *** results show that the significant advantages of NCNES,especially on games with uncertain and delayed rewards,can be highly owed to the effective parallel exploration ability.

关键词： evolutionary computation reinforcement learning exploration

A Differential Evolution Approach for Protein Folding Using a Lattice Model

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Journal of Computer Science & Technology 2007年第6期22卷 904-908页

作者： Heitor Silverio Lopes Reginaldo Bitello Bioinformatics Laboratory Federal University of Technology-ParanáAv.7 de setembro3165 80230-901 CuritibaBrazil

Protein folding is a relevant computational problem in Bioinformatics, for which many heuristic algorithms have been proposed. This work presents a methodology for the application of differential evolution （DE） to the problem of protein folding, using the bi-dimensional hydrophobic-polar model. DE is a relatively recent evolutionary algorithm, and has been used successfully in several engineering optimization problems, usually with continuous variables. We introduce the concept of genotype-phenotype mapping in DE in order to provide a mapping between the real-valued vector and an actual folding. The methodology is detailed and several experiments with benchmarks are done. We compared the results with other similar implementations. The proposed DE has shown to be competitive, statistically consistent and very promising.

关键词： bioinformatics differential evolution evolutionary computation protein folding