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检索条件"主题词=Cultural Algorithm"

共 209 条记录，以下是51-60 订阅

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A New Simultaneous Coordinated Design of STATCOM Controller and Power System Stabilizer for Power Systems Using cultural algorithm

A New Simultaneous Coordinated Design of STATCOM Controller ...

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IEEE International Energy Conference (ENERGYCON)

作者： Tavakoli, Mohamad Reza Rasouli, Vahid Nasajpour, Hamid Reza Shaarbafehizadeh, Majid Univ Isfahan Dept Elect Engn Esfahan Iran

ISBN: (纸本)9781479924493

The shunt flexible AC transmission system (FACTS) devices, such as STATCOM, are being used in transmission networks to enhance damping of a power system. However, uncoordinated design of these devices with power system stabilizers (PSSs) may deteriorate the power system performance. Furthermore, the characteristics of the power system elements are non-linear, and the controllers that are designed based on linear control theories may not be capable of providing the desirable performance, specifically when there are large disturbances acting on the system. This paper presents a new technique named cultural algorithm (CA) to design the controllers of STATCOM and PSS coordinately. This technique is simple, computationally efficient and can guarantee that the overall system performance is desirable. Simulations are carried out on both single-machine infinite-bus and multi-machine electric power systems. Comparing the results of the proposed control strategy with other intelligent methods, BFA and GA clearly verifies that the proposed method can provide a better performance.

关键词： Power system stabilizer STATCOM Power Systems Optimization cultural algorithm

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Integrated Intelligent Optimization Based on cultural algorithm

Integrated Intelligent Optimization Based on Cultural Algori...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Liu, Zhuoqian Gu, Xingsheng E China Univ Sci & Technol Res Inst Automat Shanghai 200237 Peoples R China

ISBN: (纸本)9781424467129

This paper presents an Integrated Intelligent Optimization based on cultural algorithm (IIOBCA) to solve Optimization problems, Unlike the traditional CA, the IIOBCA consists of two belief spaces with different directions and a population space, The multi-algorithms are proposed to be used in the population space, There are two groups in the belief spaces, i,e, the "good" belief space and the "bad" belief space, From the population space, the "good" belief space only takes the individuals of the population with best fitness values, and the "bad" belief space takes the individuals with the rest "bad" fitness values, The evolution of the "good" belief space is adopted by Particle Swarm Optimization (PSO) and the "bad" belief space is adopted by Genetic algorithm (GA) separately while the population are evolving at generation. The individuals in the population space are divided into three sub-groups, and the three groups are searching global optima via GA, PSO and COA respectively. To avoiding premature convergence, the strategy of exchanging the individuals between "good" belief space and "bad" belief space is presented, Regarding several large-dimension special unimodal and multimodal functions, compared with the basic GA, PSO and COA, simulation results show that the new algorithm can not only strengthen the global searching ability, but also overcome the disadvantage of falling into the local Optimization

关键词： Intelligent Optimization cultural algorithm integration

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A Parallel Framework for Multi-Population cultural algorithm and Its Applications in TSP 3rd

A Parallel Framework for Multi-Population Cultural Algorithm...

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3rd International Conference on Machine Learning, Optimization, and Data Science (MOD)

作者： Unold, Olgierd Tarnawski, Radoslaw Wroclaw Univ Sci & Technol Dept Comp Engn Fac Elect Wyb Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (纸本)9783319729268;9783319729251

This paper presents a novel parallel framework based on the Multi-Population cultural algorithm (MPCA) scheme for optimization problems. Contrary to the existing variants of cultural algorithm (CA), the proposed parallel framework for MPCA (PFMPCA) allows the use of any implemented metaheuristic both in a belief, and in a population space. Furthermore, the proposed approach permits CA to evolve simultaneously multiple population and belief sub-spaces, leveraging the dual inheritance mechanism and utilizing multi-population approach. Moreover, each sub-population (in population or belief space) is able to communicate between each other. PFMPCA has been implemented on Graphics Processing Units (GPUs) using CUDA programming model. The performance of the developed framework was evaluated using asymmetric Travelling Salesman Problem (ATSP). The MPCA for TSP implemented by means of the parallel framework proves to have an extensible architecture designed to accommodate changes and good performances.

关键词： cultural algorithm Multi-Population GPU computing CUDA architecture Travelling Salesman Problem Ant Colony Optimization Genetic algorithm

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Path Planning Method for Robots in Complex Ground Environment Based on cultural algorithm 09

Path Planning Method for Robots in Complex Ground Environmen...

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World Summit on Genetic and Evolutionary Computation (GEC 09)

作者： Guo, Yi-nan Yang, Mei Cheng, Jian China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Peoples R China

ISBN: (纸本)9781605583266

In complex ground environment, different regions have different road conditions. Path planning for robots in such environment is an open problem, which lacks effective methods. A novel global path planning method based on common sense and evolution knowledge is proposed by adopting dual evolution structure in culture algorithms. Common sense describes ground information and feasibility of environment, which is used to evaluate and select the paths. Evolution knowledge describes the angle relationship between the path and the obstacles, or the common segments of paths, which is used to judge and repair infeasible individuals. Taken two types of environments with different obstacles and road conditions as examples, simulation results indicate that the algorithm can effectively solve path planning problem in complex ground environment and decrease the computation complexity for judgment and repair of infeasible individuals. It also can improve the convergence speed and have better computation stability.

关键词： path planning complex ground environment common sense evolution knowledge genetic algorithm cultural algorithm

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A First Study of Fuzzy Cognitive Maps Learning Using cultural algorithm 9

A First Study of Fuzzy Cognitive Maps Learning Using Cultura...

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9th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Ahmadi, Sadra Forouzideh, Nafiseh Yeh, Chung-Hsing Martin, Rodney Papageorgiou, Elpiniki Monash Univ Fac Informat Technol Melbourne Vic 3004 Australia Technol Educ Inst Cent Greece Dept Comp Engn Lamia Greece

ISBN: (纸本)9781479943159

In this paper a new algorithm for Fuzzy Cognitive Maps learning is introduced. The proposed approach is based on the cultural algorithm and it is used to build the weight matrices that allow the Fuzzy Cognitive Map algorithm to find the final steady states. A Fuzzy Cognitive Map (FCM) is a fuzzy signed directed graph with feedback and models complex systems as a collection of concepts and causal relations between concepts. An FCM can be constructed by using experts' knowledge or historical data. In this paper we have developed an automated method FCM learning which uses a type of evolutionary algorithm known as a cultural algorithm. We explain the algorithm and demonstrate its performance advantages.

关键词： Fuzzy cognitive maps (FCM) FCM learning cultural algorithm

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A cultural algorithm for Determining Similarity Values Between Users in Recommender Systems 1

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22nd International Conference on Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Selvarajah, Kalyani Kobti, Ziad Kargar, Mehdi Univ Windsor Sch Comp Sci Windsor ON Canada Ryerson Univ Ted Rogers Sch Informat Technol Management Toronto ON Canada

ISBN: (数字)9783030166922

ISBN: (纸本)9783030166915;9783030166922

Recommendation systems for online marketing often rely on users' ratings to evaluate the similarity between users in Collaborative Filtering (CF) recommender systems. This paper applies knowledge-based evolutionary optimization algorithms called cultural algorithms (CA) to evaluate the similarity between users. To deal with the sparsity of data, we combine CF with a trust network between users. The trust network is then clustered using Singular Value Decomposition (SVD) which helps to discover the top neighbors' trust value. By incorporating trust relationships with CF, we predict the rating by each user on a given item. This study uses the Epinions dataset in order to train and test the accuracy of the results of the approach. The results are then compared against those produced by Genetic algorithms (GA), Cosine, and Pearson Correlation Coefficient (PCC) methods. The comparison of the results suggests that the proposed algorithm outperforms the other similarity functions.

关键词： cultural algorithm Recommender system Collaborative Filtering Trust aware

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A Concurrent-Hybrid Evolutionary algorithms with Multi-child Differential Evolution and Guotao algorithm Based on cultural algorithm Framework

A Concurrent-Hybrid Evolutionary Algorithms with Multi-child...

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5th International Symposium on Intelligence Computation and Applications

作者： Li, Xia Liu, Kunqi Ma, Lixiao Li, Huanzhe Shijiazhuang Univ Econ Dept Comp Sci Shijiazhuang 050031 Peoples R China

ISBN: (纸本)9783642164927

This paper proposes a multi-child differential evolutionary agorithm(MCDE), and forms a concurrent-hybrid evolutionary algorithm by integrating the MCDE algorithm and Guotao algorithm based on variable searching subspace(VSSGT) into the culture algorithm framework. Numerical experiment results indicate that the performance of the proposed algorithm is better than that of MCDE, Differential Evolution algorithm(DE) and VSSGT, and better than that of the DE with double trial vectors based on Boltzmann mechanisin.

关键词： Differential evolutionary algorithm Multi-child Differential evolutionary algorithm Guotao algorithm based on variable searching subspace cultural algorithm Concurrent-Hybrid evolutionary algorithm

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Non-Convex Optimization of Combined Enviromental Economic Dispatch through the Third Version of the cultural algorithm (CA3)

Non-Convex Optimization of Combined Enviromental Economic Di...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Goudarzi, Arman Swanson, Andrew G. Tooryan, Fatemeh Ahmadi, Afshin Univ KwaZulu Natal Sch Elect Elect & Comp Engn Durban South Africa Clemson Univ Sch Elect Elect & Comp Engn Clemson SC USA

ISBN: (纸本)9781509066179

Economic load dispatch (ELD) is conventionally defined as the process of allocating generation among the running generating units such that the demand is satisfied and the fuel cost is minimized. Due to increased environmental regulation, emission constraints are being added to redefine the conventional ELD problem. It is important to minimize both the operating fuel cost and emission levels simultaneously while satisfying the load demand;this process is known as combined environmental economic dispatch (CEED). The resultant CEED becomes a challenging non-convex optimization problem, which is difficult to solve using the traditional methods such as lambda iteration and gradient method. To tackle this barrier the study has proposed a novel heuristic search technique based on a modified version of the cultural algorithm (CA3) to solve the CEED problem whilst considering transmission line losses and several physical constraints of generating units. In order to validate the aims of this study, the proposed approach has been applied in two different test systems;5 generating units and 20 generating units systems. The results of the simulations show that the proposed method of the study provides a lower generation cost for the CEED problem in comparison to the other studied techniques, it is also observed that the convergence of the CA3 remains considerably fast as the system complexity increases.

关键词： Combined environmental economic dispatch cultural algorithm prohibited operating zones ramp-rate limits valve-point effect

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A Novel Multi-objective cultural algorithm Embedding Five-Element Cycle Optimization

A Novel Multi-objective Cultural Algorithm Embedding Five-El...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Mao, Zhengyan Xiang, Yue Zhang, Yijie Liu, Mandan East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781728169293

The cultural algorithm, as a dual-inheritance framework designed for optimization problems, can incorporate any population-adopted evolutionary computation technique in its population space. On the other hand, based on the Five-Elements Cycle Model derived from the ancient Chinese Five Elements (metal, wood, water, fire, earth) theory, the five-elements cycle optimization algorithm was proved to be effective in solving continuous function optimization problems. In this work, we propose a multi-objective cultural algorithm with a five-elements-cycle-optimization-based population space, where the five-element cycle model is adopted as the evolution scheme in the population space of the cultural algorithm framework. Simulation results on 12 classic benchmark problems show that the proposed algorithm can effectively solve continuous optimization functions and obtains satisfactory non-dominated solutions compared with 8 representative multi-objective algorithms.

关键词： multi-objective evolutionary optimization cultural algorithm five-elements cycle model

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The Coverage Optimization for Wireless Sensor Networks Based on Quantum-Inspired cultural algorithm 1

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Chinese Intelligent Automation Conference (CIAC)

作者： Guo, Yi-nan Liu, Dandan Liu, Yun Chen, Meirong China Univ Min & Technol Coll Informat & Elect Engn Xuzhou 221116 Jiangsu Peoples R China

ISBN: (数字)9783642385247

ISBN: (纸本)9783642385247;9783642385230

In order to properly distribute sensor nodes in wireless sensor networks, a coverage model considering maximum coverage ratio and minimum redundancy is given and the optimization strategy based on quantum-inspired cultural algorithm is proposed. In population space, quantum-inspired evolutionary algorithm is used to increase the observed probability. In belief space, the implicit knowledge embodied in the evolution is extracted from the better individuals chosen from the population. It is used to guide the search direction of evolutionary population and influence the update of quantum individuals. Simulation results indicate that the algorithm is superior to other algorithms in coverage optimization and effectively improve the coverage performance of wireless sensor networks.

关键词： Wireless sensor networks Quantum-inspired algorithm cultural algorithm Coverage optimization

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