Speedless item particle swarm optimization algorithm based on circuit breaker

Abstract

The invention discloses a speedless item particle swarm optimization algorithm based on a circuit breaker. The speedless item particle swarm optimization algorithm introduces a stock market circuit breaker by targeting defects that a traditional particle swarm algorithm is slow in convergence and easy to produce local optimization; a particle swarm iteration evolution process is divided into 20 segments; if the particle swarm evolution process is in segments (1,2,5,6,9,10) of front 10 segments or in last 10 segments and random selection probability is smaller than 1 / 3, a circuit breaker is started to update positions of particles, if not, the circuit breaker is not started; and a speedless item global optimal position gbest guiding iteration equation is adopted to update positions of the particles. The speedless item particle swarm optimization algorithm based on the circuit breaker introduces the circuit breaker to change motion directions and pace lengths of the particles, jumps out of local optimization, and adopts the speedless item global optimal position gbest guiding iteration equation to accelerate convergence speed. As a result, the speedless item particle swarm optimization algorithm has an excellent global searching capability and has fast convergence speed.

Description

technical field [0001] The invention relates to optimization theory, in particular to a trial particle swarm optimization algorithm based on fusing mechanism. Background technique [0002] Particle swarm optimization algorithm (particle swarm optimization, pso) is a new evolutionary evolution algorithm. This algorithm is a stochastic optimization algorithm based on swarm intelligence proposed by J.Kennedy and R.C.Eberhart in 1995. The bionic basic point of this type of algorithm is: group animals (such as ants, birds, fish, etc.) effectively seek food and escape hunting through grouping. In this group of animals, the behavior of each individual is based on group behavior, that is, information is shared in the whole group, and there is information exchange and cooperation among individuals. For example, in an ant colony, when each individual finds food, it will recruit companions through contact or chemical signals, so that the whole colony can find food sources; in the fli...

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Problems solved by technology

[0004] The particle swarm optimization algorithm has good performance for single-objective, single-mode optimization problems, but the algorithm also has some defects. When the dimension of the particle swarm optimization algorithm optimization target scheme increases or the objective function has multiple local extremums, The algorithm is often premature; in addition, the particle swarm optimization algorithm also has the problem of good search speed in the early stage and slow search speed in the later stage

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