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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.
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
James Kennedy (born November 5, 1950) is an American social psychologist, best known as an originator and researcher of particle swarm optimization.The first papers on the topic, by Kennedy and Russell C. Eberhart, were presented in 1995; since then tens of thousands of papers have been published on particle swarms.
Stochastic optimization is an umbrella set of methods that includes simulated annealing and numerous other approaches. 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.
Ant colony optimization is based on the ideas of ant foraging by pheromone communication to form paths. Primarily suited for combinatorial optimization and graph problems. Particle swarm optimization is based on the ideas of animal flocking behaviour. Also primarily suited for numerical optimization problems.
Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity , as well as a communication channel between the particles.
Estimation of distribution algorithms (EDAs), sometimes called probabilistic model-building genetic algorithms (PMBGAs), [1] are stochastic optimization methods that guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. Optimization is viewed as a series of incremental updates ...
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.