Search Results - (( evolution optimization a algorithm ) OR ( variable optimization method algorithm ))

A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising by Al-Dabbagh, Mohanad Dawood, Al-Dabbagh, Rawaa Dawoud, Raja Abdullah, Raja Syamsul Azmir, Hashim, Fazirulhisyam

“…As the standard DE algorithm is a fixed length optimizer, it is not suitable for solving signal de-noising problems that call for variability. …”
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Optimized differential evolution algorithm for linear frequency modulation radar signal denoising by Al-Dabbagh, Mohanad Dawood Hasan

“…The standard DE algorithm is known as a fixed length optimizer, while our problem demands the need for methods that aren’t tolerated to a fixed individual size, and that was made by altering the mutation and crossover strategies as well as the selection operation. …”
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Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method by Mohd Arfian, Ismail, Mezhuyev, Vitaliy, Safaai, Deris, Mohd Saberi, Mohamad, Shahreen, Kasim, Saedudin, Rd Rohmat

“…Due to that, this study proposed and improved a method that comprises with Newton method, differential evolution algorithm (DE) and competitive co-evolutionary algorithm(ComCA). …”
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Evolutionary and population dynamics of 3 parents differential evolution (3PDE) using self-adaptive tuning methodologies by Teng, Nga Sing, Teo, Jason Tze Wi

“…Differential Evolution is known for its simplicity and effectiveness as an evolutionary optimizer. …”
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Literature Review of Optimization Techniques for Chatter Suppression In Machining by A. R., Yusoff, Mohamed Reza Zalani, Mohamed Suffian, Mohd Yusof, Taib

“…Various algorithms can be applied in the optimization of machining problems; however, Differential Evolution is the most appropriate for use in chatter suppression, being less time consuming, locally optimal, and more robust than both Genetic Algorithms, despite their wide applications, and Sequential Quadratic Programming, which is a famous conventional algorithm.…”
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Design of digital circuit structure based on evolutionary algorithm method by Chong, Kok Hen, Aris, Ishak, Bashi, Senan Mahmood, Koh, Johnny Siaw Paw

“…In this paper, a method for automatic optimization of the digital circuit design method has been introduced. …”
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Optimal placement of unified power flow controller by dynamic implementation of system-variable-based voltage-stability indices to enhance voltage stability by Ahmad, S., Albatsh, F.M., Mekhilef, Saad, Mokhlis, Hazlie

“…Furthermore, to verify the suitability of the explored locations, a comparative study has been conducted after placing UPFC in the present locations and other locations obtained using optimization techniques like particle swarm optimization (PSO), differential evolution (DE), genetic algorithm (GA), and bacteria foraging algorithm (BFA). …”
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Crossover and mutation operators of real coded genetic algorithms for global optimization problems by Lim, Siew Mooi

“…The strength of the algorithm lies in the fact that it assists the evolution of a population of individuals who would thrive in the survival of the fittest towards the next generation. …”
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Direct Tuning Phase-Shift Angle In Dab Dc-Dc Converter Using Moth Flame Optimization (Mfo) Algorithm by Lo, Norman Kun King

“…Since the conventional tuning has a couple of downsides, mainly related to the time-consuming and inconsistence accuracy, the auto-tuning phase shift angle using Moth Flame Optimization (MFO) algorithm in DAB is proposed in this project. …”
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Genetic ensemble biased ARTMAP method of ECG-Based emotion classification by Loo, C.K., Liew, W.S., Sayeed, M.S.

“…The ARTMAP system is dependent on training sequence presentation to determine the effectiveness of the learning processes, as well as the strength of the biasing parameter, lambda λ. The optimal combination of λ and training sequence can be computed efficiently using a genetic permutation algorithm. …”
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A refined differential evolution algorithm for improving the performance of optimization process by A. R., Yusoff, Nafrizuan, Mat Yahya

“…Various Artificial Intelligent (AI) algorithms can be applied in solving optimization problems. …”
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Broadening selection competitive constraint handling algorithm for faster convergence by Shaikh, T.A., Hussain, S.S., Tanweer, M.R., Hashmani, M.A.

“…In this study, the BSCCH algorithm has been coupled with Differential Evolution algorithm as a proof of concept because it is found to be an efficient algorithm in the literature for constrained optimization problems. …”
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Two level Differential Evolution algorithms for ARMA parameters estimatio by Salami, Momoh Jimoh Emiyoka, Tijani, Ismaila, Aibinu, Abiodun Musa

“…The results of both examples show the effectiveness of the proposed algorithm over a well known conventional technique.…”
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Performance comparison of differential evolution and particle swarm optimization in constrained optimization by Iwan, Mahmud, Akmeliawati, Rini, Faisal, Tarig, Al-Assadi, Hayder M.A.A.

“…Particle swarm optimization (PSO) and differential evolution (DE) are among the well-known modern optimization algorithms. …”
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Hybrid algorithm for NARX network parameters' determination using differential evolution and genetic algorithm by Salami, Momoh Jimoh Eyiomika, Tijani, Ismaila, Isqeel , Abdullateef Ayodele, Aibinu, Abiodun Musa

“…The proposed algorithm involves a two level optimization scheme to search for both optimal network architecture and weights. …”
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Reliably optimal PMU placement using disparity evolution-based genetic algorithm by Matsukawa, Yoshiaki, Othman, Mohammad Lutfi, Watanabe, Masayuki, Mitani, Yasunori

“…In this paper, Disparity Evolution-type Genetic Algorithm (DEGA) based on disparity theory of evolution is applied. …”
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Differential evolution optimization algorithm based on generation systems reliability assessment integrated with wind energy by Kadhem, Athraa Ali, Abdul Wahab, Noor Izzri, Abdalla, Ahmed N.

“…Meanwhile, the reliability of electrical systems is currently being influenced by the increasing acceptance of "Wind Energy Conversion System" (WECS) in power systems compared to other conventional sources. This stuffy proposed a novel optimization method labeled the "Differential Evolution Optimization Algorithm" (DEOA) to assess the reliability of power generation systems (PGS). …”
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Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain by Mohamad Zihin , Mohd Zain

“…BSA gave the best overall performance by showing improved solutions and more robust convergence in comparison with various metaheuristics used in this work. Multi-objective optimization problems are also addressed by proposing a modified multi-criterion optimization algorithm based on a Pareto-based Particle Swarm Optimization (PSO) algorithm called Multi-Objective Particle Swarm Optimization (MOPSO). …”
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Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses by Tarajo, Buba Ahmed, Lee, Lai Soon

“…This paper proposes a hybrid differential evolution with particle swarm optimization (DE-PSO) algorithm to solve the UTNDP, aiming to simultaneously optimize route configuration and service frequency with specific objectives in minimizing both the passengers’ and operators’ costs. …”
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Improved chemotaxis differential evolution optimization algorithm by Yıldız, Y. Emre, Altun, Oğuz, Topal, A. Osman

“…The social foraging behavior of Escherichia coli has recently received great attention and it has been employed to solve complex search optimization problems.This paper presents a modified bacterial foraging optimization BFO algorithm, ICDEOA (Improved Chemotaxis Differential Evolution Optimization Algorithm), to cope with premature convergence of reproduction operator.In ICDEOA, reproduction operator of BFOA is replaced with probabilistic reposition operator to enhance the intensification and the diversification of the search space.ICDEOA was compared with state-of-the-art DE and non-DE variants on 7 numerical functions of the 2014 Congress on Evolutionary Computation (CEC 2014). …”
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