Search Results - (( evolution optimization swarm algorithm ) OR ( motion optimization path algorithm ))
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1
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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2
Performance comparison of differential evolution and particle swarm optimization in constrained optimization
Published 2012“…Particle swarm optimization (PSO) and differential evolution (DE) are among the well-known modern optimization algorithms. …”
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3
Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses
Published 2019“…This paper proposes a hybrid differential evolution with particle swarm optimization (DE-PSO) algorithm to solve the UTNDP, aiming to simultaneously optimize route configuration and service frequency with specific objectives in minimizing both the passengers’ and operators’ costs. …”
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4
A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection
Published 2019“…Hence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work. …”
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5
Improving Ant Swarm Optimization With Embedded Vaccination For Optimum Reducts Generation
Published 2011“…Ant Swarm Optimization refers to the hybridization of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms to enhance optimization performance. …”
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6
Representation Of Rational Bézier Quadratics Using Genetic Algorithm, Differential Evolution And Particle Swarm Optimization
Published 2013“…Three soft computing techniques namely Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) are utilized for the desired manipulation of curves and surfaces. …”
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7
A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments
Published 2013“…Sampling-based motion planning is a class of randomized path planning algorithms with proven completeness. …”
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8
Coil Optimization using Metaheuristic Techniques for Wireless Charging of Electric Vehicles - A Comparative Analysis.
Published 2024“…Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) optimization algorithms are used to obtain the lengths of all the turns of the transmitter coil. …”
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9
Runtime reduction in optimal multi-query sampling-based motion planning
Published 2023“…Algorithms; Dispersions; Manufacture; Query processing; Robotics; High-dimensional; Low dispersions; Optimal solutions; Path length; Planning tasks; Sampling-based; Sampling-based algorithms; Sampling-based motion planning; Motion planning…”
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10
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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11
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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12
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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13
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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14
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2022“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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15
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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16
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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17
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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18
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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19
Minimizing machining airtime motion with an ant colony algorithm
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20
Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm
Published 2011“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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