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Cuckoo Search for Solving Economic Dispatch Load Problem

Intelligent Control and Automation 2013年第4期4卷 385-390页

作者： Adriane B.S.Serapiao Univ Estadual Paulista(UNESP/IGCE/DEMAC) Rio ClaroBrazil

Economic Load Dispatch (ELD) is a process of scheduling the required load demand among available generation units such that the fuel cost of operation is minimized. The ELD problem is formulated as a nonlinear constrained optimization problem with both equality and inequality constraints. In this paper, two test systems of the ELD problems are solved by adopting the Cuckoo Search (CS) Algorithm. A comparison of obtained simulation results by using the CS is carried out against six other swarm intelligence algorithms: Particle swarm Optimization, Shuffled Frog Leaping Algorithm, Bacterial Foraging Optimization, Artificial Bee Colony, Harmony Search and Firefly Algorithm. The effectiveness of each swarm intelligence algorithm is demonstrated on a test system comprising three-generators and other containing six-generators. Results denote superiority of the Cuckoo Search Algorithm and confirm its potential to solve the ELD problem.

关键词： Economic Dispatch Load Cuckoo Search Algorithm swarm intelligence Optimization

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Optimal Power Flow Solution Using Particle swarm Optimization Technique with Global-Local Best Parameters

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Journal of Energy and Power Engineering 2010年第2期4卷 46-51页

作者： P. Umapathy C. Venkatasehsiah M. Senthil Arumugam Faculty of Engineering and Technology Multimedia University Melaka 75450 Malaysia

This paper proposes an efficient method for optimal power flow solution （OPF） using particle swarm optimization （PSO） technique. The objective of the proposed method is to find the steady state operation point in a power system which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow limits and voltage limits. In order to improvise the performance of the conventional PSO （cPSO）, the fine tuning parameters- the inertia weight and acceleration coefficients are formulated in terms of global-local best values of the objective function. These global-local best inertia weight （GLBestlW） and global-local best acceleration coefficient （GLBestAC） are incorporated into PSO in order to compute the optimal power flow solution. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The results are compared with those obtained through cPSO. It is observed that the proposed algorithm is computationally faster, in terms of the number of load flows executed and provides better results than the conventional heuristic techniques.

关键词： Particle swarm optimization swarm intelligence optimal power flow solution inertia weight acceleration coefficient.

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Artificial Bee Colony Algorithm Based on Von Neumann Topology Structure

Artificial Bee Colony Algorithm Based on Von Neumann Topolog...

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2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Wenping Zou Yunlong Zhu Hanning Chen Hai Shen Key Laboratory of Industrial Informatics Shenyang Institute of AutomationChinese Academy of Sciences Graduate School of the Chinese Academy of Sciences

Article Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper proposes a new variant of the ABC algorithm based on Von Neumann topology structure for solving global optimization problems, namely Von Neumann Neighborhood Article Bee Colony (VABC). VABC significantly improves the original ABC in solving complex optimization problems. In this work, VABC algorithm is used for optimizing a set of widely-used benchmark functions and the comparative results produced by ABC. The simulation results showe that the proposed VABC outperforms the original ABC algorithms in terms of accuracy, robustness and convergence speed.

关键词： component Article Bee Colony Von Neumann topology swarm intelligence

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A Kind of Improved Monkey Algorithm

A Kind of Improved Monkey Algorithm

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第30届中国控制与决策会议

作者： Xiaoping Xu Shan Zhang Fucai Qian Long Liu Bin Wang Kangkang Jing Xiya Zhang School of Sciences Xi'an University of Technology School of Automation and Information Engineering Xi'an University of Technology State Key Laboratory of Astronautic Dynamics Xi'an Satellite Control Center

swarm intelligence is one of the most promising area for the researchers in the field of numerical *** algorithm is a new swarm intelligent optimization *** algorithm can effectively solve the optimization problems of linear,nonlinear,nonconvex and complex high dimensional ***,it has been widely studied and concerned by many *** order to further improve the solution accuracy of algorithm,this paper proposes a kind of improved monkey *** basic idea of the proposed monkey algorithm is that an inducing factor is adopted in the climbing process of the basic monkey algorithm to induce it to crawl upward,which can escape from local optimal solution and find the global optimal ***,experimental results show that the algorithm significantly improves the search ability of monkey algorithm.

关键词： swarm intelligence Optimization Monkey Algorithm Inducing Factor

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Research in Capacitated Vehicle Routing Problem Based on Modified Hybrid Particle swarm Optimization

Research in Capacitated Vehicle Routing Problem Based on Mod...

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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Zhengchu Wang School of Mechanical Engineering Taizhou College Taizhou Zhejiang 318000 P.R.China Muxun Zhou School of Physics&Electronic Engineering Taizhou College Taizhou Zhejiang 318000 P.R.China Jun Li School of Mechanical Engineering Taizhou College Taizhou Zhejiang 318000 P.R.China Jian Fan School of Mechanical Engineering Taizhou College Taizhou Zhejiang 318000 P.R.China

Capacitated vehicle routing problem （CVRP） is an NP hard problem and it has important practical *** large-scale problems,it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational *** this paper,it is analyzed that the insufficiency of the traditional particle swarm optimization （PSO） to the discrete and combination optimization problem;and hybrid particle swarm optimization （HPSO） is developed in this work to *** the large lost in swarm diversity during the evolution,diversity-measure is introduced into *** hybrid particle swarm optimization （MHPSO） is proposed and the detailed strategy of solving CVRP is also *** order to utilize the ergodicity,stochastic property and regularity of capability of chaos,it also constructs the initial solution with *** effectiveness and better performance of the proposed algorithm is demonstrated by comparing with other particle swarm optimization algorithms （PSO） and genetic algorithms （GA）.Series of numerical examples show that MHPSO is a feasible and effective approach for capacitated vehicle routing problem.

关键词： vehicle routing problem particle swarm optimization swarm intelligence optimization

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A Self-organized Shape Formation Method For swarm Controlling

A Self-organized Shape Formation Method For Swarm Controllin...

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第37届中国控制会议

作者： Qian Bi Yong Huang The 29th Research Institude Of China Electronics Technology Group Corporation

This paper presents a new approach for the shape formation based on the artificial method. It refers to the basic concept in the swarm intelligence: complex behaviors of the swarm can be formed with simple rules designed in the agents. In the framework, the distance image is used to generate not only an attraction field to keep all the agents in the given shape, but also repulsive force field among the agents to make them distribute uniformly. Compared to the traditional methods based on centralized control, the algorithm has properties of distributed and simple computation, convergence and robustness, which is very suitable for the swarm robots in the real world considering the limitation of communication, collision avoidance and calculation problems. We also show that some initial sensitive method can be improved in the similar way. The simulation results prove the proposed approach is suitable for convex, non-convex and line shapes.

关键词： shape formation artificial potential fields distributed calculation swarm intelligence

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UAV Path Planning Method Based on Ant Colony Optimization

UAV Path Planning Method Based on Ant Colony Optimization

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2010 Chinese Control and Decision Conference

作者： Chao Zhang,Ziyang Zhen,Daobo Wang,Meng Li College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing,210016

＜正＞A new UAV path planning method based on ant colony optimization（ACO）is *** target position is considered as the food source which the ants are going to *** enemy defense region is considered as the searching area of the ants and is divided into equally spaced *** ants move to the destination node through several nodes on the grid *** visibility function of ACO algorithm considers the enemy threats intensity on the paths and the distance to the destination *** weighted sums of the flight path length,the threat cost and the maximum restriction of the yaw angle are considered as the evaluation function of ACO *** pheromone amounts on the paths are updated according to the evaluation function ***,the UAV optimal flight path is expressed by a group of node number,which is obtained by the ants finding the optimal route to the food *** ACO algorithm based UAV path planning method is characterized as simple coding and good optimization guidance,and the simulation results also show its effectiveness.

关键词： Unmanned Aerial Vehicle Path Planning Ant Colony Optimization swarm intelligence

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Mathematical Analysis on a swarm of Robotic System

Mathematical Analysis on a Swarm of Robotic System

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首届亚洲机械电子学学术会议

作者： Yangmin Li Xin Chen Department of Electromechanical Engineering,Faculty of Science and Technology,University of Macau,Av.Padre Tomás Pereira S.J.,Taipa,Macau SAR,P.R.China,

As a benchmark for swarm intelligence,the issue of formation task is to investigate how a group of homogeneous robots form a single *** propose a design method for swarm systems to accomplish formation task,and establishes a mathematic model to describe the emergent *** on the model,we analyze some properties of the swarm system such as the attachment rate by computer *** demonstrates that this macroscopic model provides a good mathematic approach to test the performance of multiple robots constructed swarm system.

关键词： swarm intelligence Macroscopic Model Formation Task.

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An improved particle swarm optimization for the constrained portfolio selection problem

An improved particle swarm optimization for the constrained ...

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2009 International Conference on Computational intelligence and Natural Computing(CINC 2009)

作者： Jianwei Gao School of Business Administration North China Electric Power University Beijing China Zhonghua Chu School of Business Administration North China Electric Power University Beijing China

We focus on a constrained portfolio selection model with transaction costs and quantity limit. Due to these complex constraints, the process becomes a high-dimensional constrained optimization problem. Traditional optimization algorithms fail to work efficiently and heuristic algorithms with effective searching ability can be the best choose for the problem, and then we design an improved particle swarm（ISPO） optimization to solve this question. In order to prevent premature convergence to local minima, we design a new definition for global point. Finally, a numerical example of a portfolio selection problem is given to illustrate our proposed method;the simulation results demonstrate good performance of the IPSO in solving the complex constrained portfolio selection problem.

关键词： swarm intelligence Particle swarm Optimization Portfolio selection Algorithm Optimization.

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Human-Inspired Algorithms for Continuous Function Optimization

Human-Inspired Algorithms for Continuous Function Optimizati...

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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Luna Mingyi Zhang Cheyenne Dahlmann The Center for Advanced Studies in Science Math and Technology of Joseph Wheeler High School 375 Holt Road MariettaGeorgia 30068USA

The Human-Inspired Algorithm（HIA） is a new algorithm that uses a given population（a group of candidate solutions） to improve the search for optimal solutions to continuous functions in different optimization applications such as non-linear *** imitates the intelligent search strategies of mountain climbers who use modern techniques（such as binoculars and cell phones） to effectively find the highest mountain in a given *** from Genetic Algorithms（GAs） and Bees Algorithms（BAs）,HIA divides a whole search space into multiple equal subspaces,evenly assigns the population in the subspaces,finds an elite subspace with the largest sum of function values,and uses more climbers（candidate solutions） to explore the elite subspace and fewer ones to explore the rest of the whole search *** use random search in local neighborhood search,whereas HIA uses GAs in local neighborhood search to obtain better *** locates a point with the largest function value among the elite sites and creates a hypercube with the point as its *** assigned climbers in the hypercube and the elite subspace continue to search for the optimal solution *** each loop,the hypercube and the elite subspace become smaller to have a larger chance to pinpoint the optimal *** results for three continuous functions with constraints and three continuous functions with box constraints can indicate that HIA is more efficient than GAs and ***,conclusions and future works are given.

关键词： Human-Inspired Algorithms swarm intelligence Bees Algorithms Genetic Algorithms Optimization

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