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A particle swarm searching for the global minimum of a function. In computational science, particle swarm optimization (PSO) [1] is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
Distributed Evolutionary Algorithms in Python (DEAP) is an evolutionary computation framework for rapid prototyping and testing of ideas. [2] [3] [4] It incorporates the data structures and tools required to implement most common evolutionary computation techniques such as genetic algorithm, genetic programming, evolution strategies, particle swarm optimization, differential evolution, traffic ...
Particle swarm optimization (PSO) A swarm intelligence method. Intelligent water drops (IWD) A swarm-based optimization algorithm based on natural water drops flowing in rivers Gravitational search algorithm (GSA) A swarm intelligence method. Ant colony clustering method (ACCM) A method that make use of clustering approach, extending the ACO.
Particle swarm optimization is an algorithm modeled on swarm intelligence that finds a solution to an optimization problem in a search space, or models and predicts social behavior in the presence of objectives.
Particle swarm optimization is based on the ideas of animal flocking behaviour. Also primarily suited for numerical optimization problems. Gaussian adaptation – Based on information theory. Used for maximization of manufacturing yield, mean fitness or average information.
Swarm intelligence. Ant colony optimization; Bees algorithm: a search algorithm which mimics the food foraging behavior of swarms of honey bees; Particle swarm; Frank-Wolfe algorithm: an iterative first-order optimization algorithm for constrained convex optimization; Golden-section search: an algorithm for finding the maximum of a real function
In the ABC algorithm, the first half of the swarm consists of employed bees, and the second half constitutes the onlooker bees. The number of employed bees or the onlooker bees is equal to the number of solutions in the swarm. The ABC generates a randomly distributed initial population of SN solutions (food sources), where SN denotes the swarm ...
The Griewank function is commonly used to benchmark global optimization algorithms, such as genetic algorithms or particle swarm optimization. In addition to the original version, there are several variants of the Griewank function specifically designed to test algorithms in high-dimensional optimization scenarios [ 2 ] .