Parallel asynchronous particle swarm optimization method and system and electronic equipment

Abstract

The invention provides a parallel asynchronous particle swarm optimization method and system and electronic equipment, and the method comprises the steps: building a fitness function for a to-be-optimized target, and enabling the fitness function to be used for measuring a decision variable; grouping the particle groups, and randomly initializing initial positions, optimal values and diversity optimization parameters of particles in each particle group; establishing an information sharing mechanism for each particle group, wherein the information sharing mechanism is used for sharing the optimal value of each particle group; arranging the particle groups on different CPU cores for distributed parallel iterative calculation, and asynchronously updating historical optimal values of the particle groups according to an information sharing mechanism; and when the global number of iterations is greater than or equal to the threshold value of the number of iterations, ending the iterative update of each particle group, and outputting the optimal value of the particle group as a final optimization result. Compared with a traditional particle swarm algorithm, the method has the advantages that the optimization performance and robustness of the algorithm are improved, the calculation amount of the algorithm is reduced, the operation efficiency of the algorithm is improved, and the method can be suitable for various complex optimization scenes.

Description

technical field

[0001] The invention relates to the field of optimization, in particular to the field of optimization of non-convex, non-continuous and non-derivable functions. Background technique

[0002] Optimization technology is crucial to the development of various fields of society. For example, in the field of engine design, through the optimization design of various parts of the engine, the efficiency of the engine can be made higher and the emission lower; Optimizing the power output combination of multiple generators can improve energy utilization and increase economic benefits.

[0003] Optimization techniques can be mainly divided into the following two types: optimization algorithms based on gradient information, such as stochastic gradient descent algorithm; intelligent optimization algorithms not based on gradient information, such as particle swarm optimization algorithm, genetic algorithm, simulated annealing algorithm, etc. Optimization algorithms based o...

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