Search Results - (( parameter optimization method algorithm ) OR ( using evolutionary modified algorithm ))

Application of Evolutionary Algorithm for Assisted History Matching by Zahari, Muhammad Izzat

“…Besides, algorithm based method has been widely used to forecast future result in various field for example art, biology, marketing including engineering. …”
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Optimisation of automatic generation control performance in two-area power system with pid controllers using mepso / Lu Li by Lu , Li

“…Also, using MEPSO-TVAC algorithm, the performance of AGC by using two PID controllers is better in terms of rise time and settling time than using one PID controller. …”
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Overview of Evolutionary Algorithms and Neural Networks for Modern Mobile Communication by Lida Kouhalvandi, Ibraheem Shayea, Serdar Ozoguz, Hafizal Mohamad

Optimization of chemotherapy using metaheuristic optimization algorithms / Prakas Gopal Samy by Prakas Gopal , Samy

“…Advancing multi-objective optimization techniques for cancer treatment strategies, the study strategically incorporates Swarm Intelligence (SI) and Evolutionary Algorithms (EA). …”
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PID controller adjustment for MA-LFC by using imperialist competitive algorithm by Soheilirad, Mohammadsoroush, Farzan, Payam, Othman, Mohammad Lutfi, Karami, K., Hojabri, Mojgan

“…In this paper a new evolutionary computing method based on Imperialist Competitive Algorithm (ICA) is used for tuning the elements of a PID controller which is applied in a Multi Area Load Frequency Control System (MA-LFC). …”
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Improved genetic algorithm for direct current motor high speed controller implemented on field programmable gate array by Alkhafaji, Falih Salih

“…There are many researches have been done to optimize PI controller based evolutionary algorithm, such as Genetic Algorithm (GA). …”
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Enhancing benchmark optimization with evolutionary random approach: A comparative analysis of modified adaptive bats sonar algorithm (MABSA) by Nor Shuhada, Ibrahim, Nafrizuan, Mat Yahya, Saiful Bahri, Mohamed, Mohd Ismail, Yusof

“…This article presents a novel hybrid algorithm, combining the Modified Adaptive Bats Sonar Algorithm (MABSA) with the Squirrel Search Algorithm (SSA), and compares its performance with the original MABSA. …”
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Hybrid evolutionary optimization algorithms: A case study in manufacturing industry by Vasant, P.

“…Such complex problems of vagueness and uncertainty can be handled by the hybrid evolutionary intelligence algorithms. In this chapter, a new hybrid evolutionary optimization based methodology using a specific non-linear membership function, named as modified S-curve membership function, is proposed. …”
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Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation by Illias, Hazlee Azil, Wee, Zhao Liang

“…In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. …”
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Modified archive update mechanism of multi-objective particle swarm optimization in fuzzy classification and clustering by Rashed, Alwatben Batoul

“…Evolutionary algorithms have been extensively used to resolve problems associated with multiple and often conflicting objectives. …”
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Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain by Mohamad Zihin , Mohd Zain

“…Two multi-objective fed-batch models are also used as case studies to verify the performance of the proposed algorithm. …”
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Application of an evolutionary algorithm-based ensemble model to job-shop scheduling by Tan, Choo Jun, Neoh, Siew Chin, Lim, Chee Peng, Hanoun, Samer, Wong, Wai Peng, Loo, Chu Kiong, Zhang, Li, Nahavandi, Saeid

“…Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. …”
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Evolutionary Multiobjective optimization for automatic generation Of Neural game controller by Chin, Kim On, Yong, Yung Nan

“…This research presents the result of implementing evolutionary algorithms towards computational intelligence in Tower Defense game (TD game). …”
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Optimization of turning parameters using genetic algorithm method by Shah Izwandi, Mohd Zawawi

“…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). …”
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Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis by Asrul, Adam

“…The efficiency of the three algorithms are evaluated and compared with previous results obtained by other optimization methods on similar studies. …”
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Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization by Nur Iffah, Mohamed Azmi

“…Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. …”
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An evolutionary harmony search algorithm with dominant point detection for recognition-based segmentation of online Arabic text recognition by Moayad, Yousif Potrus, Ngah, Umi Kalthum, Bestoun S. , Ahmed

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A multi-objective evolutionary approach for fuzzy optimization in production planning by P., Vasant, F., Jimenez, G., Sanchez, J.L., Verdegay

“…An ad hoc Pareto-based multi-objective evolutionary algorithm is proposed to capture multiple non dominated solutions in a single run of the algorithm. …”
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Optimization of turning parameters using ant colony optimization by Mohamad Nazri, Semoin

“…The project objectives are to develop Ant Colony Optimization (ACO) algorithm for CNC turning process and to optimize turning parameters for minimized production cost per unit. …”
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Parameter estimation in double exponential smoothing using genetic algorithm / Foo Fong Yeng, Lau Gee Choon and Zuhaimy Ismail by Foo, Fong Yeng, Lau, Gee Choon, Ismail, Zuhaimy

“…Trial and error often serves as the best method to determine the parameter. Therefore, a good optimization technique is required for identify the best parameter in minimizing the forecast errors. …”
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