Search Results - (( evolution optimization approach algorithm ) OR ( parameter optimization method algorithm ))

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

“…Other metaheuristic approaches such as genetic algorithm, differential evolution algorithm, particle swarm optimization, and ant colony optimization are still preferable to address combinatorial optimization problems. …”
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Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

“…Three programs have developed; Differential Evolution Neural Network (DENN), Genetic Algorithm Neural Network (GANN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on ANN learning using various datasets. …”
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Comparative analysis of spiral dynamic algorithm and artificial bee colony optimization for position control of flexible link manipulators by Nor Maniha, Abdul Ghani, Nizaruddin, M. Nasir, Azrul Azim, Abdullah Hashim

“…This study aims to evaluate the effectiveness of two optimization algorithms, artificial bee colony (ABC) and spiral dynamic algorithm (SDA), in controlling the position of a flexible-link manipulator. …”
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Optimization of chemotherapy using metaheuristic optimization algorithms / Prakas Gopal Samy by Prakas Gopal , Samy

“…The HM emerges as a dominant strategy, driven by the Multi-Objective Differential Evolution (MODE) algorithm under literature-based control parameter settings for the mathematical model. …”
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Mobility management schemes based on multiple criteria for optimization of seamless handover in long term evolution networks by Hussein, Yassein Soubhi

“…The results demonstrated that our proposed method results in significant reductions of HOF, HOPP and packet loss ratio (PLR) compared to the conventional HHO and enhanced weighted performance HO parameter optimization (EWPHPO) algorithm. …”
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An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines by Bala, A., Ismail, I., Ibrahim, R., Sait, S.M., Oliva, D.

“…This problem is often formulated as a typical optimization problem. Metaheuristic algorithms are known to be excellent tools for solving optimization problems. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

“…Among others, multilevel thresholding is a robust and most widely adopted image segmentation approach. To find the optimal multilevel threshold values, various heuristic and meta-heuristic algorithms have been applied to segment COVID-19 medical images. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, ., Fakhrud, Din, Shah, Khalid, Kamal Z., Zamli, Alam, Aftab

“…Among others, multilevel thresholding is a robust and most widely adopted image segmentation approach. To find the optimal multilevel threshold values, various heuristic and meta-heuristic algorithms have been applied to segment COVID-19 medical images. …”
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Novel farmland fertility algorithm based PIDPSS design for SMIB angular stability enhancement by Sabo, Aliyu, Abdul Wahab, Noor Izzri, Othman, Mohammad Lutfi, Mohd Jaffar, Mai Zurwatul Ahlam

“…A new metaheuristics method called Farmland Fertility Algorithm (FFA) inspired by nature is proposed for optimal design of PIDPSS using a robust ISTSE objective function which had to be minimized. …”
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Nature-Inspired cognitive evolution to play Ms. Pac-Man by Tse, Guan Tan, Jason Teo, Patricia Anthony

“…The focus of this research is to explore the hybridization of nature-inspired computation methods for optimization of neural network-based cognition in video games, in this case the combination of a neural network with an evolutionary algorithm. …”
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Solving an application of university course timetabling problem by using genetic algorithm by Norhana, Shaibatul Khadri

“…Many efforts have been made using various computational heuristic methods to acquire the best solutions. Among the approaches, genetic algorithm (GA), constructed based on Darwin's theory of evolution, becomes the renowned approach to solve various types of timetabling problems. …”
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Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons by Yaseen, Zaher Mundher, Fu, Minglei, Wang, Chen, Mohtar, Wan Hanna Melini Wan, Deo, Ravinesh C., El-Shafie, Ahmed

“…The rolling mechanism method is applied to smooth out the dataset based on the antecedent values of the model inputs before being applied to the GM algorithm. …”
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A new teaching learning artificial bee colony based maximum power point tracking approach for assessing various parameters of photovoltaic system under different atmospheric condit... by Dokala Janandra, Krishna Kishore

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Evaluation and prediction of time overruns in Jordanian construction projects using coral reefs optimization and deep learning methods by Shihadeh, Jumana, Al-Shaibie, Ghyda, Bisharah, Majdi, Alshami, Dania, Alkhadrawi, Sajeda, Al-Bdour, Hamza

“…This study aimed to utilize deep learning, specifically the Multi-Layer Perceptron (MLP), and enhance its overrun predictive ability by incorporating the Coral Reefs Optimization Algorithm (CROA). The approach employed in our study involved the utilization of a comprehensive dataset encompassing diverse aspects of building projects, ranging from financial indicators to project durations. …”
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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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Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution by Tijani, Ismaila B., Akmeliawati, Rini, Legowo, Ari, Budiyono, Agus

“…This study proposes a hybrid of conventional back propagation training algorithm for the NARX network and multiobjective differential evolution (MODE) algorithm for identification of a nonlinear model of an unmanned small scale helicopter from experimental flight data.The proposed hybrid algorithm was able to produce models with Pareto-optimal compromise between the design objectives. …”
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A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem by Asaad Shakir, Hameed, Mohd Aboobaider, Burhanuddin, Mutar, Modhi Lafta, Ngo, Hea Choon

“…The primary aim of this study is to propose a hybrid approach which combines Discrete Differential Evolution (DDE) algorithm and Tabu Search (TS) algorithm to enhance solutions of QAP model, to reduce the distances between the locations by finding the best distribution of N facilities to N locations, and to implement hybrid approach based on discrete differential evolution (HDDETS) on many instances of QAP from the benchmark. …”
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Multiobjective optimization of bioethanol production via hydrolysis using hopfield- enhanced differential evolution by Ganesan, T., Elamvazuthi, I., Shaari, K.Z.K., Vasant, P.

“…In this chapter, the weighted sum scalarization approach is used in conjunction with three meta-heuristic algorithms: Differential Evolution (DE), Hopfield-Enhanced Differential Evolution (HEDE), and Gravitational Search Algorithm (GSA). …”
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Resource allocation in coordinated multipoint long term evolution-advanced networks by Katiran, Norshidah

“…The resource allocation algorithm is developed through three phases, namely Low-Complexity Resource Allocation (LRA), Optimized Resource Allocation (ORA) and Cross-Layer Design of ORA (CLD-ORA). …”
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Multiobjective design optimization of a nano-CMOS voltage-controlled oscillator using game theoretic-differential evolution by Ganesan, T., Elamvazuthi, I., Vasant, P.

“…The weighted sum scalarization approach was employed in this work in conjunction with three metaheuristic algorithms: particle swarm optimization (PSO), differential evolution (DE) and the improved DE algorithm (GTDE) (which was enhanced using ideas from evolutionary game theory). …”
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