Search Results - (( using (evolutionary OR evolution) study algorithm ) OR ( basic optimization bees algorithm ))

A hybrid algorithm based on artificial bee colony and artificial rabbits optimization for solving economic dispatch problem by Lee, Wei Wen, Hashim, Mohd Ruzaini

“…Hence, a hybrid optimization algorithm called Artificial Bee Rabbit Optimization (ABRO) is proposed in this paper. …”
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Artificial bee colony algorithm with proposed discrete nearest neighborhood algorithm for discrete optimization problems by Rahimi, Amir Masoud, Ramezani-Khansari, Ehsan

“…To reduce the optimization time of the tours created by the artificial bee colony algorithm, the fixed-radius near neighbor 2-opt algorithm was used as well. …”
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A comprehensive comparison of evolutionary optimization limited by number of evaluations against time constraints by Jia, Hui Ong, Teo, Jason Tze Wi

“…Practically allevolutionary optimization studies have focused exclusively on the use of number of fitness evaluations as the constraining factor when comparing different evolutionary algorithms (EAs). …”
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Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms by Tey, Jing Yuen, Ramli, Rahizar

“…Results show that the non-dominated sorting genetic algorithm II outperforms the constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution in obtaining a wider distributed Pareto solution and converges at a relatively shorter time frame. …”
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Crossover-first differential evolution for improved global optimization in non-uniform search landscapes by Teo, Jason Tze Wi, Mohd Hanafi Ahmad Hijazi, Hui, Keng Lau, Salmah Fattah, Aslina Baharum

“…The differential evolution (DE) algorithm is currently one of the most widely used evolutionary-based optimizers for global optimization due to its simplicity, robustness and efficiency. …”
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Parameters extraction of double diode photovoltaic module's model based on hybrid evolutionary algorithm by Muhsen D.H., Ghazali A.B., Khatib T., Abed I.A.

Comparative study of optimal power flow using evolutionary programming and immune evolutionary programming technique in power system / Mohd Khairil Izwan Md Daim by Md Daim, Mohd Khairil Izwan

“…This project presents a new technique for solving the optimal power flow problem, in a power system using an Evolutionary Programming and Immune Evolution Programming optimization technique. …”
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Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy by Al-Dabbagh, Rawaa Dawoud, Neri, Ferrante, Idris, Norisma, Baba, Mohd Sapiyan

“…This study has two main objectives: (1) to present an extension for the original taxonomy of evolutionary algorithms (EAs) parameter settings that has been overlooked by prior research and therefore minimize any confusion that might arise from the former taxonomy and (2) to investigate the various algorithmic design schemes that have been used in the different variants of adaptive DE and convey them in a new classification style. …”
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Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach by Lai, V., Ahmed, Ali Najah, Malek, Marlinda Abdul, El-Shafie, Ahmed, El-Shafie, Amr

“…Genetic Programming (GP) algorithm is an example of an evolutionary algorithm (EA) in the field of evolutionally computation (EC) and, more broadly, in Artificial Intelligence. …”
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Local search manoeuvres recruitment in the bees algorithm by Muhamad, Zaidi, Mahmuddin, Massudi, Nasrudin, Mohammad Faidzul, Sahran, Shahnorbanun

“…The Bees Algorithm (BA) was created specifically by mimicking the foraging behavior of foraging bees in searching for food sources.During the searching, the original BA ignores the possibilities of the recruits being lost during the flying.The BA algorithm can become closer to the nature foraging behavior of bees by taking account of this phenomenon.This paper proposes an enhanced BA which adds a neighbourhood search parameter which we called as the Local Search Manoeuvres (LSM) recruitment factor.The parameter controls the possibilities of a bee extends its neighbourhood searching area in certain direction.The aim of LSM recruitment is to decrease the number of searching iteration in solving optimization problems that have high dimensions.The experiment results on several benchmark functions show that the BA with LSM performs better compared to the one with basic recruitment.…”
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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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The design and applications of the african buffalo algorithm for general optimization problems by Odili, Julius Beneoluchi

“…Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. …”
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Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

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Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Nor Farizan, Zakaria, Mohd Mawardi, Saari

“…To show the effectiveness of EMA-DL, comparison studies were conducted among other metaheuristic optimizers that were also used to optimize the DL parameters viz, Particles Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) as well as the Adaptive Moment Estimation (ADAM). …”
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Data Analysis using Particle Swarm Optimization Algorithm by Tai, Zu Jie

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Forecasting solar power generation using evolutionary mating algorithm-deep neural networks by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

“…This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. …”
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Pareto ensembles for evolutionary synthesis of Neurocontrollers in a 2D maze-based video game by Tse, Guan Tan, Jason Teo, Chin, Kim On, Patricia Anthony

“…A system using multi-objective evolutionary algorithm is developed, which is called as Pareto Archived Evolution Strategy Neural Network(PAESNet), with the attempt to find a set of Pareto optimal solutions by simultaneously optimizing two conflicting objectives. …”
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Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence by Doolun, Ian Shivraj, Ponnambalam, S. G., Subramanian, Nachiappan, Kanagaraj, G.

“…Data driven hybrid evolutionary analytical approach is proposed by integrating Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) to handle multiple objectives into Differential Evolution (DE) algorithm. …”
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The Development Of A Robust Algorithm For Uav Path Planning In 3d Environment by Kok, Kai Yit

“…Thus, a new algorithm known as infection evolution (IE) was developed in this study. …”
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Pareto Ensembles for evolutionary Synthesis of Neurocontrollers in a 2D Maze-based video game by Tse, Guan Tan, Jason Teo, Kim, On Chin, Patricia Anthony

“…A system using multi-objective evolutionary algorithm is developed, which is called as Pareto Archived Evolution Strategy Neural Network(PAESNet), with the attempt to find a set of Pareto optimal solutions by simultaneously optimizing two conflicting objectives. …”
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