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International Conference on Power System Technology

作者： Li, PK Du, XX Liu, YL No Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China

ISBN: (纸本)0780374592

A real coded genetic algorithm (RCGA) for parameter optimization of multiarea Automatic Generating Control (AGC) has been proposed. Instead of using traditional analysis algorithm to obtain the controller parameters, GA optimization technology is introduced and MATLAB Simulink modal is designed as AGC parameter optimization tool to deal with the interconnection of the AGC loops., Utilizing GA's parallel strings searching in many peaks, the multi-variable optimization of multiarea power systems AGC will be processed quickly. The nonlinear objects such as generation rate constraint (GRC) and deadband of the turbine governor are treated easily by combination of GA with Simulink. The simulation of a two-area power systems with PID controllers is reported and the results are reasonable.

关键词： automatic generating control real coded genetic algorithm sliding mode PID control optimization

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Comparative study of Artificial Bee Colony algorithm and real coded genetic algorithm for Analysing their performances and Development of a new algorithmic framework 2

Comparative study of Artificial Bee Colony Algorithm and Rea...

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2nd International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Kishor, Avadh Singh, Pramod Kumar ABV Indian Inst Informat Technol & Management Computat Intelligence & Data Min Res Lab Gwalior 474015 India

ISBN: (纸本)9781467398190

This paper compares performance of the artificial bee colony algorithm (ABC) and the real coded genetic algorithm (RCGA) on a suite of 9 standard benchmark problems. The problem suite comprises a diverse set of unimodal, multimodal and rotated multimodal numerical optimization functions and the comparison criteria include (i) solution quality, (ii) convergence speed, (iii) robustness, and (iv) scalability to test efficacy of the algorithms. To the best knowledge of the authors, such a comprehensive comparative study of the two algorithms is not available in the literature. An empirical study shows that the RCGA has advantages over the ABC in terms of all the criteria for the unimodal and the rotated multimodal functions. On other hand, the ABC outperforms the RCGA in terms of solution quality for the multimodal functions. Therefore, based on the insights gained out of this comparative study, the authors propose an algorithm ABC-GA with new algorithmic framework that comprises advantages of both the ABC and the GA. An empirical study of the proposed algorithm ABC-GA shows its promising performance as the obtained results are superior to both the comparative algorithms for all the problems in all the criteria.

关键词： Honey bees Swarm intelligence real coded genetic algorithm Selection operator

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Application of real coded genetic algorithm for Target Sensing

Application of Real Coded Genetic Algorithm for Target Sensi...

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6th International Conference on Sensing Technology (ICST)

作者： Pappula, Lakshman Ghosh, Debalina IIT Bhubaneswar Sch Elect Sci Bhubaneswar Orissa India

ISBN: (纸本)9781467322485

Complex matrix equations need to be solved for extracting the impulse response of the target. In this paper, an evolutionary soft-computing technique of real coded genetic algorithm is applied to solve the system of linear equations. The proposed method is compared with the existing conjugate gradient method for deconvolution. The proposed method demonstrates how to overcome the drawbacks of the traditional method.

关键词： Target sensing impulse response real coded genetic algorithm conjugate gradient method complex matrix equations deconvolution

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STUDY OF CROSSOVER AND MUTATION OPERATORS CONTROL IN real coded genetic algorithm APPLYING TO SOLVE AN OPTIMIZATION TASK

STUDY OF CROSSOVER AND MUTATION OPERATORS CONTROL IN REAL CO...

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14th International Conference on Soft Computing

作者： Lovtsova, Irina Aleksejeva, Ludmila Riga Tech Univ Fac Comp Sci & Informat Technol LV-1658 Riga Latvia

ISBN: (纸本)9788021436756

The paper discusses the possibility to manage search direction in genetic algorithm crossover and mutation operators using "Depository of Weak Individuals' Fitness Values". In order to control crossover and mutation, it was decided to observe the individuals that take part in those operators. All offsprings that are produced during crossover operator run will be compared with the individuals that are their predecessors - worse individual fitness value will be kept in the "Depository of Weak Individuals' Fitness Values" during all generations. And mutation operator consists of several steps - individual's mutation will be repeated until better offspring is found If better solution is found, then the individual selected for mutation will be kept during all generations in the "Depository of Weak Individuals Fitness Values".

关键词： real coded genetic algorithm crossover and mutation operators optimization task

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Volume optimization of helical gear with profile shift using real coded genetic algorithm 1

Volume optimization of helical gear with profile shift using...

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1st International Conference on Robotics and Smart Manufacturing (RoSMa)

作者： Rai, Paridhi Agrawal, Aman Saini, Mohan L. Jodder, Chinmay Barman, Asim Gopal Natl Inst Technol Patna Dept Mech Engn Patna 800005 Bihar India New Allenberry Works Design & Dev Kolkata 700019 India

In recent years, there is increase in demand for gears with high load carrying capacity, efficiency, less cost, less noise and volume. real coded genetic algorithm (RCGA) is applied to achieve optimal helical gear design. In the present study, volume of a helical gear pair is minimized by including profile shift coefficients as design variables along with module, face width and number of teeth using RCGA. Transverse load factor, face load factor, form factor, stress factor and zone factor are affected by profile shift and hence profile shift influences the design optimization result for helical gear pair. Tooth contact and bending strength are the two constraints used in the optimization procedure. It has been found that volume with profile shift is smaller compared to that of without profile shift. For further validation of RCGA results, commercially used software KISS soft has been used.

关键词： Design Optimization Helical Gear Profile Shift real coded genetic algorithm

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real coded genetic algorithm for minimizing wavelength and number of hops in virtual wavelength path routed WDM network

Real coded genetic algorithm for minimizing wavelength and n...

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2008 IET International Conference on Wireless, Mobile and Multimedia Networks

作者： Rakhi A. Khedkar Vibha Vyas JS College of Engineering Pune India College of Engineering Pune India

ISBN: (纸本)9780863418877

In this paper, a novel real coded genetic algorithm(RcGA) is proposed for routing and wavelength assignment in virtual wavelength path routed WDM network. The comparison is done with heuristic search algorithm. Here, a wavelength routed WDM optical network is considered and the RcGA search algorithm is implemented on it. The main target of the algorithm is to find solution among all possible ones, which is most optimized solution. Therefore they may be considered as approximate and not accurate algorithms. The results are always close to the best solution. The objective of this algorithm is to minimize the requirement of wavelength in any network topology demanded by network traffic. It also minimizes the hop length between source and destination nodes in the traffic. As wavelength and number of hops get reduced, the cost of the network also gets reduced and maximizes the resource utilization. In the first phase of this algorithm, we assigned minimum hop length to each route demanded by traffic and also assigned wavelengths to each route. In second phase of algorithm effective rerouting is performed to reduce the number of wavelengths required in the network and it also minimizes the hop length of each rerouted route. By minimizing wavelength requirement, the need of wavelength converter gets reduced, so that the network cost is also reduced. Along with the implementation of RcGA algorithm and heuristic algorithm, few more parameters such as Network Congestion and Network wavelength converter requirement are discovered. The Network Congestion on each link of network is used for calculation of Network Wavelength Requirement, and Network wavelength converter Requirement.

关键词： real coded genetic algorithm Heuristic search algorithm Network Conjunction Hops

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Short-term Hydrothermal Scheduling Based on Adaptive Chaotic real coded genetic algorithm 11

Short-term Hydrothermal Scheduling Based on Adaptive Chaotic...

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11th World Congress on Intelligent Control and Automation

作者： Fang, Na Zhou, Jianzhong Ma, Jimin Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan Hubei Peoples R China Hubei Univ Technol Sch Elect & Elect Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781479958252

This paper proposes an adaptive chaotic real coded genetic algorithm (ACRCGA) to solve short-term hydrothermal scheduling (SHS) problem. Adaptive crossover and mutation operator is introduced to improve the global search ability. Meantime, chaotic local search is incorporated into RCGA to enhance the local search ability. An effective constraints handling method for SHS problem is designed to deal with complicated constraints. Finally, the proposed method is applied to a hydrothermal system. The simulation results show that the proposed ACRCGA is superior to other optimization method in quality and efficiency.

关键词： short-term hydrothermal scheduling real coded genetic algorithm adaptive crossover and mutation chaotic local search constraints handling

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Volume optimization of helical gear with profile shift using real coded genetic algorithm

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Procedia Computer Science 2018年 133卷 718-724页

作者： Paridhi Rai Aman Agrawal Mohan L. Saini Chinmay Jodder Asim Gopal Barman Department of Mechanical Engineering National Institute of Technology Patna Patna 800005 India Design and Development New Allenberry Works Kolkatta 700019 India

关键词： Design Optimization Helical Gear Profile Shift real coded genetic algorithm

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Aerospace design optimization using a steady state real-coded genetic algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2012年第9期218卷 4710-4730页

作者： Dyer, John D. Hartfield, Roy J. Dozier, Gerry V. Burkhalter, John E. Auburn Univ Auburn AL 36849 USA N Carolina A&T Greensboro NC 27411 USA

This study demonstrates the advantages of using a real coded genetic algorithm (GA) for aerospace engineering design applications. The GA developed for this study runs steady state, meaning that after every function evaluation the worst performer is determined and that worst performer is then thrown out and replaced by a new member that has been evaluated. The new member is produced by mating two successful parents through a crossover routine, and then mutating that new member. For this study three different preliminary design studies were conducted using both a binary and a real coded GA including a single stage solid propellant missile systems design, a two stage solid propellant missile systems design and a single stage liquid propellant missile systems design. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Aerospace Optimization Rocket propulsion genetic algorithm real coded genetic algorithm Steady state genetic algorithm

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A simple and efficient real-coded genetic algorithm for constrained optimization

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APPLIED SOFT COMPUTING 2016年 38卷 87-105页

作者： Chuang, Yao-Chen Chen, Chyi-Tsong Hwang, Chyi Feng Chia Univ Dept Chem Engn Taichung 407 Taiwan Natl Quemoy Univ Ctr Gen Educ Jinning Township 892 Kinmen Taiwan

This paper presents a simple and efficient real-coded genetic algorithm (RCGA) for constrained real-parameter optimization. Different from some conventional RCGAs that operate evolutionary operators in a series framework, the proposed RCGA implements three specially designed evolutionary operators, named the ranking selection (RS), direction-based crossover (DBX), and the dynamic random mutation (DRM), to mimic a specific evolutionary process that has a parallel-structured inner loop. A variety of benchmark constrained optimization problems (COPs) are used to evaluate the effectiveness and the applicability of the proposed RCGA. Besides, some existing state-of-the-art optimization algorithms in the same category of the proposed algorithm are considered and utilized as a rigorous base of performance evaluation. Extensive comparison results reveal that the proposed RCGA is superior to most of the comparison algorithms in providing a much faster convergence speed as well as a better solution accuracy, especially for problems subject to stringent equality constraints. Finally, as a specific application, the proposed RCGA is applied to optimize the GaAs film growth of a horizontal metal-organic chemical vapor deposition reactor. Simulation studies have confirmed the superior performance of the proposed RCGA in solving COPs. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Evolutionary algorithms real coded genetic algorithm Crossover operator Constrained optimization Penalty functions

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