Type 1 Particle Swarm Optimization (T1PSO) + Conve
2016-08-23
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Type 1 Particle Swarm Optimization is a modified version of Particle Swarm Optimization (PSO) which is a computational method that optimizes a problem by iteravely trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search space according to simple mathematical formula over the particle's position and velocity.
Each particle's movement is influenced by its local best known position
but, is also guided toward the best known positions in the
search-space, which are updated as better positions are found by other
particles. This is expected to move the swarm toward the best solutions. PSO is a metaheuristic as it makes few or no assumptions about the problem being optimized and
can search very large spaces of candidate solutions. However,
metaheurist
matlab
算法
优化
粒子
类型
常规
TPSO
CPSO
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