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Intelligent Frequency Assignment Algorithm Based on Hybrid Genetic Algorithm

Yichen Liu, Bo Li, Zhao Chenqian, Teng Ma

202024 citationsDOI

Abstract

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

Topics & Concepts

AlgorithmTabu searchSimulated annealingGenetic algorithmComputer scienceFrequency assignmentPopulation-based incremental learningConvergence (economics)Algorithm designAnt colony optimization algorithmsHeuristicHybrid algorithm (constraint satisfaction)Artificial intelligenceMachine learningEconomicsProbabilistic logicTelecommunicationsEconomic growthConstraint satisfactionConstraint logic programmingSatellite Communication SystemsEngineering and Test SystemsMobile Agent-Based Network Management