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Broadening selection competitive constraint handling algorithm for faster convergence
Published 2020“…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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Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach
Published 2018“…GP is a meta-heuristic search and optimization technique based on natural evolution. The control and optimization parameters in this study are tuned. …”
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Finding minimum path by using Genetic Algorithm (GA)/ Siti Zuraifah Hashim
Published 2007“…Roadways and telephone systems are the examples of them. Genetic Algorithms (GA), pioneered by John Holland, applies the principle of evolution found in nature to the problem of finding an optimal solution. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…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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Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach
Published 2023“…GP is a meta-heuristic search and optimization technique based on natural evolution. The control and optimization parameters in this study are tuned. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…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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Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer
Published 2025“…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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An adaptive HMM based approach for improving e-Learning methods
Published 2023“…The model proposed in this research is based on clustering of students using K-means algorithm and the course of content delivery is adaptively characterized for each student using Hidden Markov Models. …”
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Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution
Published 2014“…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
Published 2020“…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
Published 2014“…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
Published 2015“…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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Differential evolution for neural networks learning enhancement
Published 2008“…To overcome this problem, Differential Evolution (DE) has been used to determine optimal value for ANN parameters such as learning rate and momentum rate and also for weight optimization. …”
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Multiobjective design optimization of a nano-CMOS voltage-controlled oscillator using game theoretic-differential evolution
Published 2015“…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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Investigation and validation of an eleven level symmetric modular multilevel inverter using grey wolf optimization and differential evolution control algorithm for solar PV applica...
Published 2021“…Purpose: This paper aims to examine the design and control of a symmetric multilevel inverter (MLI) using grey wolf optimization and differential evolution algorithms. Design/methodology/approach: The optimal modulation index along with the switching angles are calculated for an 11 level inverter. …”
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Evolution of RF-signal cognition for wheeled mobile robots using pareto multi-objective optimization
Published 2009“…The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal set of ANNs that could optimize two objectives in a single run; (1) maximize the mobile robot homing behavior whilst (2) minimize the hidden neurons involved in the feed-forward ANN. …”
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Crosssover-first different evolution: a comprehensive post- Hoc analysis
Published 2017“…Explore a new evolutionary optimization approach using Crossover-First Differential Evolution. …”
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GPU-accelerated extractive multi-document text summarization using decomposition-based multi-objective differential evolution
Published 2025“…Multi-document text summarization is computationally intensive, mainly when employing complex optimization algorithms. The computational demands increase significantly due to the integration of complex optimization algorithms and the computationally expensive repair operator. …”
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