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Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年第0期36卷 148-163页

作者： Imanian, Nafiseh Shiri, Mohammad Ebrahim Moradi, Parham Amirkabir Univ Technol Dept Math & Comp Sci Tehran Iran Univ Kurdistan Dept Comp Engn Sanandaj Iran

Artificial bee colony (ABC) is a swarm optimization algorithm which has been shown to be more effective than the other population based algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). Since it was invented, it has received significant interest from researchers studying in different fields because of having fewer control parameters, high global search ability and ease of implementation. Although ABC is good at exploration, the main drawback is its poor exploitation which results in an issue on convergence speed in some cases. Inspired by particle swarm optimization, we propose a modified ABC algorithm called VABC, to overcome this insufficiency by applying a new search equation in the onlooker phase, which uses the PSO search strategy to guide the search for candidate solutions. The experimental results tested on numerical benchmark functions show that the VABC has good performance compared with PSO and ABC. Moreover, the performance of the proposed algorithm is also compared with those of state-of-the-art hybrid methods and the results demonstrate that the proposed method has a higher convergence speed and better search ability for almost all functions. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Particle swarm optimization Artificial bee colony Continuous optimization hybrid optimization algorithm

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Improved performances of a PMSM with reduced torque ripples, optimized based on hybrid algorithm, dedicated for light EV

Improved performances of a PMSM with reduced torque ripples,...

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IEEE Vehicle Power and Propulsion Conference

作者： Daniel FODOREAN Lhassane IDOUMGHAR Technical University of Cluj-Napoca Universite de Haute-Alsace

ISBN: (纸本)9781467376389

This paper deals with the optimum design of a permanent magnet synchronous machine (PMSM) used for the propulsion of a light electric vehicle. The optimization is employed based on evolutionary algorithm by combining the simulated annealing with the particle swarm approach. The optimized solution is validated numerically through finite element method (FEM). Furthermore, a prototype of the optimized machine is constructed and the control of the motor operation is tested, showing that the obtained results are validating the employed approach and the prototype corresponds to the application needs.

关键词： Light electric vehicle PMSM hybrid optimization algorithm FEM validation Measurements

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Multi-state Markov process Based Maintenance optimization for Power Distribution Transformer

Multi-state Markov process Based Maintenance Optimization fo...

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International Conference on Power System Technology (PowerCon)

作者： Liang Hai-feng Zhang Jing Gao Ya-jing North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China

ISBN: (纸本)9781479950324

Pointing at optimization of the distribution transformer's maintenance based on risk, firstly this paper proposed an equipment failure rate model based on Multi-state Markov process that is suitable for Condition Based Maintenance. And then an age reduction factor is used to modify the failure rate model. A Distribution Transformer Maintenance optimization Model whose objective function is the minimum of the risk of grid operation is established basically. The model is established from the perspective of maintenance accounting the maintenance risk and failure risk. And a hybrid optimization algorithm is proposed to solve the optimized problem. Finally using a case validates the effectiveness of this proposed method.

关键词： Maintenance optimization Markova Process Age reduction factor hybrid optimization algorithm

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An Efficient hybrid optimization algorithm Based on Lagged-start and Parallel Operation

An Efficient Hybrid Optimization Algorithm Based on Lagged-s...

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International Conference on Web Information Systems and Mining

作者： Yu Guofang Zhang Yujie China Univ Min & Technol Coll Informat & Elect Engn Xuzhou 221116 Jiangsu Prov Peoples R China Xuzhou AF Coll Dept Logist Command Xuzhou Peoples R China

ISBN: (纸本)9780769538174

Fast convergence-rate, low computation-complexity and good stability are important goals in the researching area of neural network learning algorithm. A kind of parallel computing lagged-start hybrid optimization algorithm is studied, it not only integrates the basic gradient-method and the unconstrained optimization algorithm to realize the supplement of their advantages, but also makes full use of the high-performance computer's parallel computing features to complete the algorithm switching from one to another on time, which improves the efficiency of algorithm learning and meets the neural network system's online learning or real-time control. Combined a typical test function, a Microsoft Visual C# program is edit for the performance testing and validation of the proposed algorithm, the results is satisfied as expected.

关键词： neural network hybrid optimization algorithm lagged-start parallel Operation

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The Design Study on A Novel MW Wind Power Generation Speed-up Machine

The Design Study on A Novel MW Wind Power Generation Speed-u...

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3rd International Conference on Materials Science and Engineering (ICMSE 2014)

作者： Han, Lifen Ding, Weifeng Zhong, Shouyan Liao, Zilong Dongguan Univ Technol Coll Mech Engn Dongguan Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou Peoples R China

ISBN: (纸本)9783037859520

A novel efficient transmission, toroidal drive, was introduced into the field of wind power. A novel MW (Million Watt) wind power generation speed-up machine (WPGSM) was designed to replace the gear speed-up machine. The structure scheme of the MW WPGSM was designed and the design platform for the MW WPGSM was developed by UG/OPEN, VC++ and hybrid optimization algorithm based on the unigraphics (UG) software.

关键词： MW WPGSM toroidal drive parametric design and model parameter optimization hybrid optimization algorithm

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Optimal Power Flow Using hybrid PSOGSA algorithm 55

Optimal Power Flow Using Hybrid PSOGSA Algorithm

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55th International Scientific Conference on Power and Electrical Engineering of Riga-Technical-University (RTUCON)

作者： Radosavljevic, Jordan Arsic, Nebojsa Jevtic, Miroljub Univ Pristina Fac Tech Sci Kosovska Mitrovica Serbia

ISBN: (纸本)9781479974627

This paper presents a new hybrid algorithm based on the particle swarm optimization (PSO) and the gravitational search algorithm (GSA) for solving the optimal power flow (OPF) in power systems. Performance of this approach for the OPF problem is studied and evaluated on the standard IEEE 30-bus test system with different objective functions. Simulation results on the OPF problem show that the hybrid PSOGSA algorithm provides effective and robust high-quality solution.

关键词： Optimal power flow particle swarm optimization gravitational search algorithm hybrid optimization algorithm

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A Novel hybrid optimization Method of Shuffled Frog Leaping algorithm and Particle Swarm optimization

A Novel Hybrid Optimization Method of Shuffled Frog Leaping ...

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2013 2nd International Conference on Key Engineering Materials and Computer Science(KEMCS 2013)

作者： LIN Meijin LUO Fei XU Yuge LUO long Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Technology South China University of Technology Foshan university

Shuffled frog leaping algorithm(SFLA) is a meta-heuristic algorithm, which combines the social behavior technique and the global information exchange of memetic algorithms. But the SFLA has the shortcoming of low convergence speed while solving complex optimization problems. Particle swarm optimization(PSO) is a fast searching algorithms, but easily falls into the local optimum for the diversity scarcity of particles. In the paper, a new hybrid optimization called SFLA-PSO is proposed, which introduced PSO to SFLA by combining the fast search strategy of PSO and global search strategy of SFLA. Six benchmark functions are selected to compare the performance of SFLA-PSO, basic PSO, w PSO and SFLA. The simulation results show that the proposed algorithm SFLA-PSO possesses outstanding performance in the convergence speed and the precision of the global optimum solution.

关键词： Shuffled frog leaping algorithm(SFLA) Particle swarm optimization(PSO) hybrid optimization algorithm Benchmark functions

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hybrid artificial immune system in identification of room acoustic properties

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2013年第6期21卷 957-967页

作者： Poteralski, A. Szczepanik, M. Ptaszny, J. Kus, W. Burczynski, T. Silesian Tech Univ Inst Computat Mech & Engn Gliwice Poland Cracow Univ Technol Inst Comp Sci Krakow Poland

The paper deals with an application of a hybrid artificial immune system (HAIS) to the identification problems. The HAIS is applied to identify complex impedances of room walls. This approach is based on the mechanism discovered in biological immune systems. The numerical example demonstrates that the method based on immune computation is an effective technique for solving computer aided in identification problem.

关键词： artificial immune system acoustics identification method of fundamental solutions hybrid optimization algorithm

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STRATEGIC INIITIALIZATION OF A hybrid PARTICLE SWARM optimization-SIMULLATED ANNEALING algorithm (HPSOSA) FOR PID CONTROLLER DESIGN FOR A NONLINEAR SYSTEM

STRATEGIC INIITIALIZATION OF A HYBRID PARTICLE SWARM OPTIMIZ...

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25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Tharmalingam, Mathiruban Raahemifar, Kaamran Ryerson Univ Dept Elect Engn & Comp Engg Toronto ON Canada

ISBN: (纸本)9781467314336

There exist some variations of the particle swarm optimization - simulated annealing optimization technique (PSOSA) hybrid algorithm for solving the PID control design problem, however most of these algorithms use the simulated annealing as a tool to escape local minimums that the PSO algorithm may get trapped in and also these algorithms initialize the particles within the solution space randomly. In this paper, the effects of initializing the particles strategically within the solution space along with the application of the SA algorithm to the hybrid algorithm at each iteration are explored. To test the effectiveness of the proposed modifications the algorithms are compared on common benchmark functions before the modified hybrid algorithm (MPSOSA) is used to design a PID controller for the inverted Pendulum problem.

关键词： Nonlinear systems PID Controller hybrid optimization algorithm Particle Swarm optimization (PSO) Simulated Annealing (SA)

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TWT magnetic focusing structure optimization by parallel evolutionary algorithm

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2012年第5期31卷 1338-1346页

作者： Coco, Salvatore Laudani, Antonino Pollicino, Giuseppe Pulcini, Giuseppe Fulginei, Francesco Riganti Salvini, Alessandro Univ RomaTre Dipartimento Elettron Applicata Rome Italy Univ Catania Dipartimento Ingn Elettr Elettron & Informat Catania Italy Univ RomaTre Res Unit Electrotech Rome Italy

Purpose - The purpose of this paper is to present the application of a novel hybrid algorithm, called MeTEO (Metric-Topological-Evolutionary-optimization), based on the combination of three heuristics inspired by artificial life to the solution of optimization problems of a real electronic vacuum device. Design/methodology/approach - The Particle Swarm optimization (PSO), the Flock-of-Starlings optimization (FSO) and the Bacterial Chemotaxis algorithm (BCA) were adapted to implement a novel meta-heuristic MeTEO the FSO has been powerfully employed for exploring the whole space of solutions, whereas the PSO is used to explore local regions where FSO had found solutions, and BCA to refine the solutions found by PSO, thanks its better performances in local search. Findings - The optimization of the focusing magnetic field of a Travelling Wave Tubes (TWT) collector is presented in order to show the effectiveness of MeTEO, in combination with COLLGUN FE simulator and equivalent source representation. The optimization of the focusing magnetic structure is obtained by using a maximum of 100 steps for each heuristic. Practical implications - The paper describes the development of a novel efficient parallel method for the solution of electromagnetic device optimization problems. Originality/value - The paper shows the capabilities of a novel combination of optimization methods inspired by "artificial life" which allows us to achieve effective solutions of multimodal optimization problems, typical of the electromagnetic device optimization, with an acceptable computational cost, thanks also to its natural parallel implementation.

关键词： Programming and algorithm theory Artificial intelligence Magnetic fields Swarm intelligence Artificial life hybrid optimization algorithm Traveling wave tube Focusing magnetic field Particle-in-cell method Finite element method

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