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Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book;

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Auto Tuning of PID And Fuzzy Controllers Using Genetic Algorithm

In this book, The powerful capabilities of tuning PID and FLC controllers based on Genetic Algorithm (GA) in locating the optimal solutions;

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Optimisation in Signal and Image Processing

approach; hidden Markov models; and, metaheuristics (genetic algorithms, ant colony algorithms, cross-entropy, particle swarm optimization;

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Artificial Evolution

colony optimization, applications, combinatorial and discrete optimization, memetic algorithms, genetic programming, interactive evolution;

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Advances in Swarm Intelligence

; theories and models of swarm intelligence; ant colony optimization; particle swarm optimization; artificial bee colony algorithms; genetic;

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Advances in Swarm Intelligence

swarm-based optimization algorithms; particle swarm optimization; applications of particle swarm optimization; ant colony optimization;

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Advances in Swarm Intelligence

swarm-based optimization algorithms; particle swarm optimization; applications of particle swarm optimization; ant colony optimization;

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Introduction to Nature-Inspired Optimization

optimization. Lindfield/Penny provide concise coverage to all the major algorithms, including genetic algorithms, artificial bee colony algorithms, ant;

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Advances in Swarm Intelligence, Part I

, applications of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, artificial immune;

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Advances in Swarm and Computational Intelligence

-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms;

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Advances in Swarm and Computational Intelligence

-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms;

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Advances in Swarm and Computational Intelligence

-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms;

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Path Planning Algorithms for Mobile Robots

A comparative analysis of three most commonly used path planning algorithms, i.e. A* Algorithm, Genetic Algorithm and Ant Colony;

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Emerging Evolutionary Algorithms for Antennas and Wireless Communications

), evolution strategies (ES), particle swarm optimization (PSO), differential evolution (DE), and ant colony optimization (ACO). In addition, new;

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Nature-Inspired Metaheuristic Algorithms

in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search;

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Advances in Swarm Intelligence

optimization; particle swarm optimization; ant colony optimization; fireworks algorithms and brain storm optimization; swarm intelligence algorithms;

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Advances in Swarm Intelligence

optimization; particle swarm optimization; ant colony optimization; fireworks algorithms and brain storm optimization; swarm intelligence algorithms;

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Advances in Swarm Intelligence

Optimization; Particle Swarm Optimization; Ant Colony Optimization; Differential Evolution; Genetic Algorithm and Evolutionary Computation; Fireworks;

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Evolutionary Algorithms and Neural Networks

first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony;

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Evolutionary Machine Learning Techniques

neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization;

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Evolutionary Machine Learning Techniques

types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer;

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Modern Optimization with R

of interactive Web applications; usage of parallel computing; and more modern optimization algorithms (e.g., iterated racing, ant colony optimization;

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Classification Analysis of DNA Microarrays

involving particle swarm optimization and ant colony optimization.;

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Metaheuristic Optimization

. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm;

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Decision Tree and Ensemble Learning Based on Ant Colony Optimization

good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the;

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Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes

various algorithms that include the genetic algorithm, simulated annealing, differential evolution, ant colony optimization, tabu search, particle;

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Advances in Swarm Intelligence

computing; swarm-based computing algorithms for optimization; particle swarm optimization; ant colony optimization; brain storm optimization;

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