Search Results - (( using optimization method algorithm ) OR ( parameter adaptation based algorithm ))*

Optimising neural network training efficiency through spectral parameter-based multiple adaptive learning rates by Yeong, Lin Koay, Hong, Seng Sim, Yong, Kheng Goh, Sing, Yee Chua, Wah, June Leong

“…The process of training neural networks heavily involves solving optimization problems. Most optimization algorithms use a !…”
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Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

“…ARDE algorithm makes use of JADE strategy and the MDE_pBX parameters adaptive schemes as frameworks. …”
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Adaptive route optimization for mobile robot navigation using evolutionary algorithm by Kit Guan Lim, Guan Lim, Yoong Hean Lee, Hean Lee, Min Keng Tan, Keng Tan, Hou, Pin Yoong, Tienlei, Wang, Tze, Kenneth Kin Teo

“…For example, Ant Colony Optimization (ACO) is an optimization algorithm based on swarm intelligence which is widely used to solve path planning problem. …”
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Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems by Yasear, Shaymah Akram

“…Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. …”
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Estimation of photovoltaic models using an enhanced Henry gas solubility optimization algorithm with first-order adaptive damping Berndt-Hall-Hall-Hausman method by Ramachandran, Murugan, Sundaram, Arunachalam, Ridha, Hussein Mohammed, Mirjalili, Seyedali

“…Then in terms of methodology, the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is combined with the Sine-Cosine mutualism phase of Symbiotic Organisms Search (SOS) for efficiently estimating the unknown parameters of PV models. …”
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Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms by Ridha, Hussein Mohammed

“…The IEM algorithm uses the attraction-repulsion mechanism to change the positions of solutions towards the optimality. …”
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BBO algorithm-based tuning of PID controller for speed control of synchronous machine by Kasilingam G., Pasupuleti J.

Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem by Anniza, Hamdan, San Nah, Sze, Say Leng, Goh, Kang Leng, Chiew, Wei King, Tiong

“…Hybridization in evolutionary algorithm mechanisms such as initialization methods, local searches, specific design operators, and self-adaptive parameters enhance the algorithm’s performance. …”
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Improved stochastic gradient descent algorithm with mean-gradient adaptive stepsize for solving large-scale optimization problems by Zulkifli, Munierah, Abd Rahmin, Nor Aliza, Wah, June Leong

“…Stochastic gradient descent (SGD) is one of the most common algorithms used in solving large unconstrained optimization problems. …”
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Nature-inspired parameter controllers for ACO-based reactive search by Sagban, Rafid, Ku-Mahamud, Ku Ruhana, Abu Bakar, Muhamad Shahbani

“…This study proposes machine learning strategies to control the parameter adaptation in ant colony optimization algorithm, the prominent swarm intelligence metaheuristic.The sensitivity to parameters’ selection is one of the main limitations within the swarm intelligence algorithms when solving combinatorial problems.These parameters are often tuned manually by algorithm experts to a set that seems to work well for the problem under study, a standard set from the literature or using off-line parameter tuning procedures. …”
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Enhanced gravitational search algorithm for nano-process parameter optimization problem / Norlina Mohd Sabri by Mohd Sabri, Norlina

“…Based on the capabilities of the metaheuristic algorithms, this research is proposing the enhanced Gravitational Search Algorithm (eGSA) to solve the nano-process parameter optimization problem. …”
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A review of training methods of ANFIS for applications in business and economic by Mohd Salleh, Mohd Najib, Hussain, Kashif

“…Therefore many researchers have trained ANFIS parameters using metaheuristic algorithms however very few have considered optimizing the ANFIS rule-base. …”
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Accuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better management of the generation of renewable energy by Ehteram M., Ahmed A.N., Fai C.M., Afan H.A., El-Shafie A.

A review of training methods of ANFIS for applications in business and economics by Mohd Salleh, Mohd Najib, Hussain, Kashif

“…Therefore many researchers have trained ANFIS parameters using metaheuristic algorithms however very few have considered optimizing the ANFIS rule-base. …”
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Efficiency improvement of a standalone photovoltaic system using fuzzy-based maximum power point tracking algorithm by Alhamdawee, Ehsan Mohsin Obaid

“…MPPT algorithms can be categorized into classical methods and artificial intelligence-based methods. …”
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Parameter extraction of photovoltaic module using hybrid evolutionary algorithm by Muhsen D.H., Ghazali A.B., Khatib T.

Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength by Hussain Talpur, Kashif

“…Many researchers have trained ANFIS parameters using metaheuristic algorithms but very few have considered optimizing the ANFIS rule-base. …”
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Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection by Norfadzlan, Yusup, Izzatul Nabila, Sarbini, Dayang Nurfatimah, Awang Iskandar, Azlan, Mohd Zain, Didik Dwi, Prasetya

“…The experiment results show how these algorithms could be used to improve methods for recognizing human activities using wearables technology, such as feature selection, parameter adjustment, and model optimization.…”
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Data driven neuroendocrine pid controller for mimo plants based adaptive safe experimentation dynamics algorithm by Mohd Riduwan, Ghazali

“…Data-driven tools are an optimization method to find the optimal controller parameters using the system’s input and output data. …”
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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

“…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. …”