Target assignment optimizing method based on particle swarm algorithm of population explosion

Abstract

The invention provides a target assignment optimizing method based on a particle swarm algorithm of population explosion, and belongs to the field of intelligent algorithm optimization. In the optimizing method, according to aggregation situation of population in a process of optimization searching, a "population explosion operator" is introduced; divergent treatment of particles is performed under a restricted condition based on partial principles of chaos searching and self-adapting; and parameter adjustment is performed at the same time. The population is prevented against prematurely falling into local optimization, and the problem of target assignment of a battle between intelligent tanks in a virtual battlefield is solved. The method comprises: step 1, performing real number coding of an own side and an opposite side, and generating excellent initial population by utilizing chaos searching; step 2, adjusting initial parameters of the algorithm; step 3, performing iterative optimization searching by adopting the particle swarm algorithm based on the "population explosion operator"; and step 4, ending iteration when the iteration number reaches a set number, and obtaining an optimal scheme. Compared to a method based on an original algorithm, the method increases the total optimization searching ability of particles, and satisfies the requirement of high timeliness.

Description

technical field [0001] The invention belongs to the field of intelligent algorithm optimization, in particular to a target allocation optimization method based on population explosion particle swarm algorithm. Background technique [0002] Target allocation in the virtual battlefield environment is an important aspect of CGF (Computer Generated Forces) real behavior simulation. Target allocation is a common behavior of many CGF combat entities, and the accuracy of its simulation directly affects other combat behaviors of CGF and the authenticity of CGF simulations. [0003] The research significance of "target allocation" is not only to improve the credibility of the simulation effect in the CGF simulation system, but also to give people a good "immersion". At the same time, the research on "target allocation" can also play a huge role in actual combat, providing combatants with fast and efficient strike solutions. [0004] The target allocation of CGF in the virtual battl...

AI Technical Summary

Problems solved by technology

However, the PSO algorithm tends to converge too quickly at the initial stage of the search. When there are multiple extreme points in the solution space (that is, in the area near this point, the allocation scheme represented by this point is the best), the population tends to converge in Near the extreme point, so the population is easy to fall into the local optimal solution in the later stage of optimization, making it difficult to obtain the optimal solution

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