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1
Reliably optimal PMU placement using disparity evolution-based genetic algorithm
Published 2017“…The optimization model is solved for IEEE 118 standard bus system. …”
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Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain
Published 2018“…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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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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Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia
Published 2024“…Post-optimization ANN model was trained using eleven different leaning algorithms. …”
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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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Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms
Published 2020“…A new method based on constraint multi-objective optimization using evolutionary algorithms is proposed to optimize the powertrain design of a battery electric formula vehicle with an all-wheel independent motor drive. …”
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Parameter extraction of photovoltaic module using hybrid evolutionary algorithm
Published 2023Conference Paper -
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VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.
Published 2012“…The aim of the proposed approach is to study the benefit of using visualization techniques to explorer Genetic Algorithm data based on gene values. …”
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An enhanced metaheuristic approach to solve quadratic assignment problem using hybrid technique
Published 2021“…The valuate of performance HDDETS algorithm comparison to existing hybrid-based algorithms, namely: Biogeography-Based Optimization Tabu Search (BBOTS), Whale Algorithm with Tabu Search (WAITS), Hybrid Ant System (HAS), Lexisearch and Genetic Algorithms (LSGA), and Golden Ball Simulated Annealing (GBSA) algorithms. …”
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10
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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Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…The IEM algorithm uses the attraction-repulsion mechanism to change the positions of solutions towards the optimality. …”
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Mixed Unscented Kalman Filter and differential evolution for parameter identification
Published 2013“…First, a non-linear mathematical model is developed. Next, its parameters are identified using mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) based on the experimental data. …”
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Sub-route reversal repair mechanism and differential evolution for urban transit network design problem
Published 2017“…From the literature of UTNDP, the most widely used metaheuristic is the genetic algorithm, at the expense of other population-based metaheuristics. …”
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15
Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio...
Published 2023“…VL-WIDE was also integrated with the solution selection model based on the Analytical Hierarchical Process (AHP) that considers decision-maker preference for the optimized objectives. …”
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Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem
Published 2023“…Two main aspects are used to classify the evolutionary algorithm variants: population-based and evolutionary strategies (variation and replacement). …”
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Performances Of Metaheuristic Algorithms In Optimizing Tool Capacity Allocations
Published 2014“…In this research, the algorithms studied includes Genetic Algorithm, Particle Swarm Optimization Algorithm, Differential Evolution Algorithm, Harmony Search Algorithm, Teaching-LearningBased Optimization Algorithm and Black Hole Algorithm. …”
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Optimization Of Fractional-Slot Permanent Magnet Synchronous Machine Using Analytical Sub-Domain Model And Differential Evolution
Published 2019“…Three multi-objective optimizations based on Analytical Sub-Domain (ASD) and Differential Evolution Algorithm (DEA), Analytical Sub-Domain (ASD) and Particle Swarm Optimization (PSO), Analytical Sub-Domain (ASD) and Genetic Algorithm (GA), for fractionalslot Permanent Magnet Synchronous Machines (PMSM) are formulated, computed and optimized. …”
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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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