Search Results - (( evolution optimization strategy algorithm ) OR ( using evolution study algorithm ))
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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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Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain
Published 2018“…Two multi-objective fed-batch models are also used as case studies to verify the performance of the proposed algorithm. …”
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Resource allocation in coordinated multipoint long term evolution-advanced networks
Published 2015“…ORA is formulated based on Lagrangian method and optimized using Particle Swarm Optimization (PSO). …”
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A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection
Published 2019“…To examine the effectiveness of proposed method, four recent and popular feature selection methods namely BPSO, genetic algorithm (GA), binary gravitational search algorithm (BGSA) and competitive binary grey wolf optimizer (CBGWO) are used in a performance comparison. …”
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Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
Published 2018“…DE is very sensitive to its parameter settings and mutation strategy; thus, this study aims to investigate these settings with the diverse versions of adaptive DE algorithms. …”
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Optimized differential evolution algorithm for linear frequency modulation radar signal denoising
Published 2013“…The main contention of this thesis is to investigate the development of new optimization technique based on Differential Evolution algorithm (DE), applied for radar signal denoising application. …”
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Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm
Published 2018“…However, the complexity of these processes requires an expert system that involves swarm intelligence-based metaheuristics such as Artificial Algae Algorithm (AAA), Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Differential Evolution (DE) for simulation and optimization of the feeding trajectories. …”
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A comprehensive comparison of evolutionary optimization limited by number of evaluations against time constraints
Published 2016“…To find out the answer for this question, four well-known and most commonly-used algorithms are tested. Particle swarm optimization (PSO), Differential Evolution (DE), Genetic Algorithms (GA), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are tested in three different setups of experiments. …”
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Evolving controllers for simulated car racing using differential evolution
Published 2013“…This paper presents an initial approach for creating autonomous controllers for the car racing game using a hybrid technique. The Differential Evolution (DE) algorithm is combined with Feed-forward Artificial Neural Networks (FFANNs) to generate the required intelligent controllers in a well-known car racing game, namely The Open Racing Car Simulator (TORCS). …”
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Sensitivity analysis of GA parameters for ECED problem
Published 2023“…The paper presents the study on sensitivity analysis of the parameters of Genetic Algorithm (GA) for the ECED problem. …”
Conference paper -
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A comparative study for parameter selection in online auctions
Published 2009“…Hence, this work attempts to improve an existing bidding strategy by taking into accounts the evolution of various model of generic algorithm in optimizing the parameter of the bidding strategies. …”
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Optimizing crystal size distribution based on different cooling strategies in batch crystallization process
Published 2024“…Thus, this study aims to maximize the CSD of the grown seed crystals while minimizing the nucleus-grown crystals by employing the best optimization algorithm for the potash alum crystallization process. …”
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Application of manta ray foraging optimization with gradient-based mutation (cMRFO) for solving power system problems
Published 2023“…This study found cMRFO to be a competitive optimization algorithm for solving constrained optimization problems. …”
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Optimization of chemotherapy using metaheuristic optimization algorithms / Prakas Gopal Samy
Published 2024“…Advancing multi-objective optimization techniques for cancer treatment strategies, the study strategically incorporates Swarm Intelligence (SI) and Evolutionary Algorithms (EA). …”
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Pareto ensembles for evolutionary synthesis of Neurocontrollers in a 2D maze-based video game
Published 2013“…A system using multi-objective evolutionary algorithm is developed, which is called as Pareto Archived Evolution Strategy Neural Network(PAESNet), with the attempt to find a set of Pareto optimal solutions by simultaneously optimizing two conflicting objectives. …”
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Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review
Published 2024journal::journal article -
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Crossover and mutation operators of real coded genetic algorithms for global optimization problems
Published 2016“…This study is primarily aimed at investigating two issues in genetic algorithm (GA) and one issue in conformational search (CS) problems. …”
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Pareto Ensembles for evolutionary Synthesis of Neurocontrollers in a 2D Maze-based video game
Published 2013“…A system using multi-objective evolutionary algorithm is developed, which is called as Pareto Archived Evolution Strategy Neural Network(PAESNet), with the attempt to find a set of Pareto optimal solutions by simultaneously optimizing two conflicting objectives. …”
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Accurate numerical treatment on a stochastic SIR epidemic model with optimal control strategy
Published 2022“…In this paper, a numerical study has been undertaken on the susceptible-infectedrecovered (SIR) epidemic model that encompasses the mechanisms of the evolution of disease transmission; a prophylactic vaccination strategy in the susceptible populations, depending on the infective individuals. …”
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