Ad
related to: particle swarm optimization matlab
Search results
Results From The WOW.Com Content Network
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.
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.
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].
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.
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.
Additionally, he compared differential evolution (DE), particle swarm optimization (PSO), and a hybrid algorithm (HPSDE) for optimizing hydrocarbon reservoir well placement, showing the hybrid approach's superior performance in maximizing recovery and addressing geological uncertainty.
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.
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.