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Applied Optimization and Swarm Intelligence

researchers, lecturers and practitioners interested in swarm intelligence, optimization problems and artificial intelligence.;

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Swarm Intelligence

mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then;

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Swarm Intelligence

mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then;

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Advances in Swarm Intelligence

of swarm intelligence research; novel swarm-based optimization algorithms; swarming behaviour; some swarm intelligence algorithms and their;

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Advances in Swarm Intelligence

of swarm intelligence research; novel swarm-based optimization algorithms; swarming behaviour; some swarm intelligence algorithms and their;

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Emerging Research on Swarm Intelligence and Algorithm Optimization

intelligence. >Emerging Research on Swarm Intelligence and Algorithm Optimization discusses current research analyzing how the collective behavior;

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Advances in Swarm Intelligence

; and firefly algorithm. Part II: multi-objective optimization; portfolio optimization; community detection; multi-agent systems and swarm;

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Advances in Swarm Intelligence

; and firefly algorithm. Part II: multi-objective optimization; portfolio optimization; community detection; multi-agent systems and swarm;

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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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Advances in Swarm Intelligence

selected from 127 submissions. The papers are organized in 12 cohesive topical sections as follows: Swarm intelligence and nature-inspired;

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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 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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Advances in Swarm Intelligence, Part I

. Topics addressed in part II are such as multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent systems, data mining;

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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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Swarm Intelligence Based Optimization

Based Optimization, ICSIBO 2016, held in Mulhouse, France, in June 2016. The 9 full papers presented were carefully reviewed and selected from;

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Swarm Intelligence: Principles, current algorithms and methods

improvements from typical particle swarm optimization (PSO), ant colony optimization (ACO) and fireworks algorithm (FWA) as well as other swarm;

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Nature-Inspired Computation and Swarm Intelligence

Nature-inspired computation and swarm intelligence have become popular and effective tools for solving problems in optimization;

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Fractional Order Darwinian Particle Swarm Optimization

segmentation, and swarm robotics. It compares the capabilities of some of the better-known bio-inspired optimization approaches, especially Particle;

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Particle Swarm Optimization

modelled on swarm intelligence. Swarm intelligence is based on social-psychological principles and provides insights into social behaviour, as well;

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Applied Optimization MATLAB Programming

such as Multidisciplinary Optimization, Explicit Solution for Boundary Value Problems, and Particle Swarm Optimization.;

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Recent Advances in Swarm Intelligence and Evolutionary Computation

, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as;

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Fundamentals of Computational Swarm Intelligence

of birds in a flock as a basis for the Particle Swarm Optimization (PSO) models, and provides an extensive treatment of different classes of PSO;

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Swarm Intelligence Methods for Statistical Regression

to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization;

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Swarm Intelligence Methods for Statistical Regression

Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO) Uses concrete and realistic data analysis examples to guide;

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