A Two-Stage Particle Swarm Optimization Algorithm Including Independent Global Search

Abstract

The invention discloses a two-stage particle swarm optimization algorithm including independent global search; it includes the steps of initializing the population; adopting a chaotic method to initialize the position X and velocity V of the particles; using the fitness function fitness to calculate the current values ​​of all particles Adaptation value, initialize the historical optimal position pbesti of each particle and the global optimal position gbest of all particles; perform the first-stage iteration-global search; perform the second-stage iteration-local search. Beneficial effects of the present invention: in each iteration of the first stage iteration-global search, the particle randomly selects a non-self particle to learn from all the particles, and this random selection ensures that the group will not track specific particles, and also This ensures that aggregation does not occur. The second stage of iterative-local search can quickly converge and obtain a high-precision solution, the precision of the optimal solution is improved, and the "premature" defect is significantly improved.

Description

technical field [0001] The invention relates to a particle swarm optimization algorithm, in particular to a two-stage particle swarm optimization algorithm including independent global search, which can be applied to problems such as power system load scheduling and electric vehicle group control. Background technique [0002] The real-time balance between power generation and load is the basic requirement for maintaining safe and stable operation of electric power. The randomness and volatility of renewable energy power generation output will become a huge challenge to the operation of power systems in the future. The traditional operation strategies and control methods, which use power generation to track load fluctuations to achieve system balance, and use power generation control to adjust the system's operating status, will be difficult to provide. continue. Load scheduling—using load to track changes in the output of renewable energy and controlling the load to adjust...

AI Technical Summary

Problems solved by technology

[0004] Economic scheduling optimization algorithms mainly include traditional algorithms such as Lagrangian method and direct search method, and intelligent algorithms such as simulated annealing method, genetic algorithm, particle swarm algorithm, etc., but there are still some shortcomings, such as: the priority method cannot find the optimal solution , only a few sets of better combinations can be obtained; the Lagrangian relaxation method may have oscillations or singular phenomena; the dynamic programming method is too computationally intensive and must be simplified by an approximate method

[0005] Intelligent optimization algorithms such as genetic algorithm and particle swarm optimization algorithm have great advantages in solving optimal scheduling problems. and c2, combined with genetic algorithm and differential algorithm to form a hybrid optimization algorithm, local search and chaotic initial value, etc., but these schemes have not fundamentally changed the disadvantages of premature aggregation of particle swarm in the early stage, and still There is a problem that it is easy to fall into a local optimum; on the other hand, the previous algorithm either redistributes the position and speed of the particles through disturbance, chaos, etc. after all particle motions stagnate, the global search ability is poor, or through mutation, crossover, etc. The method makes the algorithm enhance the global search ability in the iterative process, but its global search and local search are mixed

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