Search Results - (( evolution optimization swarm algorithm ) OR ( evolution optimization means algorithm ))

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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A novel hybrid metaheuristic algorithm for short term load forecasting by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Yuhanis, Yusof, Syafiq Fauzi, Kamarulzaman

“…Later, the efficiency of GWO-LSSVM is compared against three comparable hybrid algorithms namely LSSVM optimized by Artificial Bee Colony (ABC), Differential Evolution (DE) and Firefly Algorithms (FA). …”
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Hybrid Metaheuristic Algorithm for Short Term Load Forecasting by Zuriani, Mustaffa, M. H., Sulaiman, Yuhanis, Yusof, Syafiq Fauzi, Kamarulzaman

“…Later, the efficiency of GWO-LSSVM is compared against three comparable hybrid algorithms namely LSSVM optimized by Artificial Bee Colony (ABC), Differential Evolution (DE) and Firefly Algorithms (FA). …”
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Forecasting solar power generation using evolutionary mating algorithm-deep neural networks by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

“…Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). …”
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Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud

“…Furthermore, to evaluate the efficiency of the proposed modified differential evolution for the training of ridgelet probabilistic neural network, four statistical search techniques, namely, particle swarm optimization, genetic algorithm, simulated angling, and classical differential evolution are used and their results are compared. …”
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Optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: Application of shield tunnelling ground surface settlement prediction by Liu, Xinni, Hussein, Sadaam Hadee, Kamarul Hawari, Ghazali, Tung, Tran Minh, Yaseen, Zaher Mundher

“…The predictive models were various nature-inspired frameworks, such as differential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimizer (ACO) to tune the adaptive neuro-fuzzy inference system (ANFIS). …”
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Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation by Halabi, Laith M., Mekhilef, Saad, Hossain, Monowar

“…The proposed hybrid models include particle swarm optimization, genetic algorithm and differential evolution. …”
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Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability by Hossain, Monowar, Mekhilef, Saad, Afifi, Firdaus, Halabi, Laith M., Olatomiwa, Lanre, Seyedmahmoudian, Mehdi, Horan, Ben, Stojcevski, Alex

“…In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. …”
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Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

“…The study compares the proposed CNN-LSTM-BMO against other metaheuristic optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Differential Evolution (DE). …”
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Techno-economic evaluation of off-grid hybrid renewable energy system for rural resort electrification in Malaysia / Monowar Hossain by Monowar, Hossain

“…For these purposes, the standalone ANFIS, ANFIS-PSO (particle swarm optimization),ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) prediction algorithms have been developed in MATLAB platform. …”
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Development of a robust intelligent controller for a semi-active car suspension system by Abas, Hesham Ahmed Abdul Mutleba

“…Commonly, the Fuzzy rules are optimized using offline optimization methods such as Differential Evolutionary (DE), Particle Swarms Optimization (PSO), or Artificial Neural Network (ANN) algorithms. …”
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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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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 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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A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim, Mohd Saad, Norhashimah

“…Hence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work. …”
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Improving Ant Swarm Optimization With Embedded Vaccination For Optimum Reducts Generation by Pratiwi, Lustiana, Choo, Yun Huoy, Draman @ Muda, Azah Kamilah, Draman @ Muda, Noor Azilah

“…Ant Swarm Optimization refers to the hybridization of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms to enhance optimization performance. …”
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Representation Of Rational Bézier Quadratics Using Genetic Algorithm, Differential Evolution And Particle Swarm Optimization by Yahya, Zainor Ridzuan

“…Three soft computing techniques namely Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) are utilized for the desired manipulation of curves and surfaces. …”
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Coil Optimization using Metaheuristic Techniques for Wireless Charging of Electric Vehicles - A Comparative Analysis. by Imtiaz T., Elsanabary A., Mekhilef S., Mubin M.B., Soon T.K., Aziz N.F.A.

“…Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) optimization algorithms are used to obtain the lengths of all the turns of the transmitter coil. …”

Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm by Mohd Zain, Mohamad Zihin, Kanesan, Jeevan, Kendall, Graham, Chuah, Joon Huang

“…DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation. …”
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