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Enhancing Genetic Algorithm Performance with Hybrid Strategy for Solving Optimization Problems
(2023-01-01)
Farda, Irfan
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Thammano, Arit
This study presents a novel algorithm called hybrid-GA, which combines genetic algorithm (GA) with the Harris Hawks Optimization (HHO) algorithm to address the challenge of enhancing GA performance in solving optimization problems. While GA is known for its strong exploration capabilities, it often faces challenges in exploitation, limiting its ability to find global optimal solutions. The hybrid-GA algorithm aims to surpass existing methods by achieving a better balance between exploration and exploitation, resulting in improved solution quality, faster convergence, and enhanced exploration-exploitation ability. The algorithm effectiveness is demonstrated through experiments on six benchmark functions from CEC2017, where the hybrid-GA outperforms compared algorithms on five of six functions, showcasing its potential for enhancing GA performance in optimization problem-solving. These findings contribute to advancing the field by providing a promising solution to address the exploration-exploitation challenge in GA-based optimization.
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Optimum Generated Power with the Minimum Cost of a Radial Network using Firefly and Genetic Algorithms
(2021-01-01)
Abdelwareth, Mohamed Els S.
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Riawan, Dedet Candra
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Chompoo-Inwai, Chow
This paper presented two artificial intelligence methods to find the optimum output power from Diesel generator (DG), Photovoltaic system (PV) and batteries to satisfy the load with the minimum cost considering the minimum losses. Our case study was a micro-gird 20 kV radial network consists of 21 busses located in Tomia island, south-east Sulawesi Island, Indonesia. Firefly algorithm (FA) and Genetic algorithm (GA) used in this study to do the optimization and chose the optimum operation. Forward-Backward sweep method used for the power flow calculations.
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A new selection operator to improve the performance of genetic algorithm for optimization problems
(2013-11-25)
Ritthipakdee, Amarita
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Thammano, Arit
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Premasathian, Nol
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Uyyanonvara, Bunyarit
Nature-inspired algorithms, such as Particle swarm optimization (PSO), Ant colony optimization (ACO), and Firefly algorithm, are well known for solving NP-hard optimization problems. They are capable of obtaining optimal solutions in a reasonable time. The algorithm presented in this paper is a combination of a firefly mating concept and genetic algorithm. Genetic algorithm is used as the core of the algorithm while a firefly mating concept is used to compose a new selection operator. The proposed algorithm is tested on four standard benchmark functions. Experimental results have confirmed that the proposed algorithm is not only computationally more efficient than both the original firefly algorithm and the genetic algorithm but also almost always ensure the optimal solutions. © 2013 IEEE.
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Modified genetic algorithm for flexible job-shop scheduling problems
(2012-01-01)
Teekeng, Wannaporn
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Thammano, Arit
This paper proposes a modified version of the genetic algorithm for flexible job-shop scheduling problems (FJSP). The genetic algorithm (GA), a class of stochastic search algorithms, is very effective at finding optimal solutions to a wide variety of problems. The proposed modified GA consists of 1) an effective selection method called "fuzzy roulette wheel selection," 2) a new crossover operator that uses a hierarchical clustering concept to cluster the population in each generation, and 3) a new mutation operator that helps in maintaining population diversity and overcoming premature convergence. The objective of this research is to find a schedule that minimizes the makespan of the FJSP. The experimental results on 10 well-known benchmark instances show that the proposed algorithm is quite efficient in solving flexible job-shop scheduling problems. © 2012 Published by Elsevier B.V.
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A design of IIR based digital hearing aids using genetic algorithm
(2011-08-12)
Srisangngam, Pichet
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Chivapreecha, Sorawat
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Dejhan, Kobchai
This paper presents a design of digital filter for digital hearing aids application. The structure of filter is consist a combination in parallel form of IIR (Infinite Impulse Response) a low-pass, a band-pass and a high-pass filter. This study shows an advantage of IIR filter can gives a good result in the low complexity digital hearing aids which leads to low hardware resources requirement and low power consumption for VLSI design. The filter coefficients of there IIR filter will obtained from the optimization procedure by genetic algorithm (GA.) The error between desired magnitude response and actual magnitude response will be minimized by GA. In order to achieve a capable of the best compensation for each hearing loss pattern. Finally, the design example and simulation results will show the accuracy of hearing loss compensation and optimal coefficients. © 2011 IEEE.
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Robust load frequency control for two area interconnected power system using GA
(2010-12-01)
Koisap, Chamnan
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Kaitwanidvilai, Somyot
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Ngamroo, Issarachai
The design of two area interconnected power system robust controller is described in this paper. The proposed technique uses a fixed-structure robust loop shaping control which can guarantee the robust performance of a structure-specified controller. Genetic Algorithm (GA) is adopted in the design, and the inverse of infinity norm from disturbances to states is formulated as the fitness function to find the optimal controller. Simulation results of designing load frequency controller for two area interconnected power system show that the proposed controller has simpler structure than that of the H <inf>∞</inf> loop shaping controller, and its stability is better than the controllers designed by the LMI approach[6] and the reduced order controller by the Hankel norm model reduction technique.
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A new fuzzy-neural system for time series forecasting
(2005-11-30)
Thammano, Arit
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Palahan, Sirinda
This paper proposes a new time series forecasting system, whose learning algorithm is a hybrid of the fuzzy c-means algorithm, the genetic algorithm, and the backpropagation algorithm. The proposed fuzzy-neural system consists of 5 layers: the input layer, the fuzzification layer, the rule layer, the hidden layer, and the output layer. The fuzzy cmeans algorithm is used to determine the center and width of the fuzzy membership functions. The artificial neural network is used as the fuzzy inference engine, while the genetic algorithm is used to optimize the fuzzy rule-base. This proposed system is tested with five time series data. The results obtained are very encouraging.
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Speed control of three-phase induction motor online tuning by genetic algorithm
(2003-01-01)
Chiewchitboon, P.
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Tipsuwanporn, V.
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Soonthornphisaj, N.
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Piyarat, W.
This paper presents the technique of three phase induction motor controls by using the genetic algorithm. The genetic algorithm is control by a PC and PWM is generated by MCS-51, the gain K<inf>P</inf>, K<inf>i</inf> which will be selected by genetic algorithm controller to sense and select the adequate load. The genetic algorithm technique can control the suitable amount supplying power to motor speed by variable input frequency feed to the motor. The controller is implemented to the real time application by creating the controller box that signal is linked to the PC via the RS232 port. The result are shows that the good performance compared with PI controller that gain K<inf>P</inf>, K<inf>i</inf> are manual inputs. This real time application will support the development in industrial.

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