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Simulation of Genetic Simulated Annealing Algorithm in Initial Alignment

GU Hong-qiang

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Abstract

Genetic algorithm could be used to search for error angles of the strapdown inertial guidance system after coarse alignment is completed, for there is great superiority in speed because of its global searching ability. But there are also limitations in local searching ability of the algorithm, so as for the precision of searching results. Simulated annealing tends to be trapped in local optima, but it also possess powerful fine adjustment ability. So the combination of genetic algorithm and simulated annealing could be sufficient to initial alignment for speed and precision. Simulations showed that genetic simulated annealing algorithm could greatly improve local searching ability of the genetic algorithm, and could get results which are better in precision.

About this research paper

What this paper is about

Genetic algorithm could be used to search for error angles of the strapdown inertial guidance system after coarse alignment is completed, for there is great superiority in speed because of its global searching ability. But there are also limitations in local searching ability of the algorithm, so as for the precision of searching results. Simulated annealing tends to be trapped in local optima, but it also possess powerful fine adjustment ability. So the combination of genetic algorithm and simulated annealing could be sufficient to initial alignment for speed and precision. Simulations showed that genetic simulated annealing algorithm could greatly improve local searching ability of the genetic algorithm, and could get results which are better in precision.

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Available abstract

Genetic algorithm could be used to search for error angles of the strapdown inertial guidance system after coarse alignment is completed, for there is great superiority in speed because of its global searching ability. But there are also limitations in local searching ability of the algorithm, so as for the precision of searching results. Simulated annealing tends to be trapped in local optima, but it also possess powerful fine adjustment ability. So the combination of genetic algorithm and simulated annealing could be sufficient to initial alignment for speed and precision. Simulations showed that genetic simulated annealing algorithm could greatly improve local searching ability of the genetic algorithm, and could get results which are better in precision.

Key concepts: Simulated annealing, Local optimum, Adaptive simulated annealing, Genetic algorithm, Algorithm, Hill climbing, Computer science, Inertial frame of reference

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