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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
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.
The following is an example of a generic evolutionary algorithm: [7] [8] [9] Generate the initial population of individuals, the first generation, randomly. Evaluate the fitness of each individual in the population. Check, if the goal is reached and the algorithm can be terminated. Select individuals as parents, preferably of higher fitness.
Multi-swarm optimization is a variant of particle swarm optimization (PSO) based on the use of multiple sub-swarms instead of one (standard) swarm. The general approach in multi-swarm optimization is that each sub-swarm focuses on a specific region while a specific diversification method decides where and when to launch the sub-swarms.
It can be shown that the limiting case corresponds to the standard Particle Swarm Optimization (PSO). In fact, if the inner loop (for j) is removed and the brightness I j {\displaystyle I_{j}} is replaced by the current global best g ∗ {\displaystyle g^{*}} , then FA essentially becomes the standard PSO.
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.
In operations research, cuckoo search is an optimization algorithm developed by Xin-She Yang and Suash Deb in 2009. [1] [2] It has been shown to be a special case of the well-known (μ + λ)-evolution strategy. [3] It was inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of host birds of other ...