Coverage with Sets Based on the Integration of Swarm Intelligence and Genetic Evolution
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The composite architecture of a multi-agent bionic search system based on swarm intelligence and genetic evolution is proposed for solving the problem of covering with sets. Two approaches to hybridization of the search by particle swarm and genetic search are considered: sequential and combinatorial. The link of this approach is a single data structure describing the solution of the problem in the form of a chromosome. New ways of coding solutions and chromosome structures have been developed to represent solutions. The key problem that was solved in this paper is related to the development of the structure of the affine space of positions (solutions), which allows displaying and searching for solution interpretations with integer parameter values. In contrast to the canonical particle swarm method, to reduce the weight of affinity bonds, by moving the pi particle to a new position of the affine solution space, a directed mutation operator was developed, the essence of which is to change the integer values of genes in the chromosome. The overall estimate of time complexity lies within O(n2)−O(n3).
KeywordsSet coverage Particle swarm Genetic evolution Affine space Integer parameters Integration Directional mutation operator
This research is supported by grants of the Russian Foundation for Basic Research of the Russian Federation, the project № 18-07-00737 a.
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