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Improved GA and PSO culled hybrid algorithm for antenna array pattern synthesis
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DOI：10.2528/PIER07121503.png)
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Abstract
En 中文
In this paper, a new evolutionary learning algorithm based on a hybrid of improved real-code genetic algorithm (IGA) and particle swarm optimization (PSO) called HIGAPSO is proposed. In order to overcome the drawbacks of standard genetic algorithm and particle swarm optimization, some improved mechanisms based on non-linear ranking selection, competition and selection among several crossover offspring and adaptive change of mutation scaling are adopted in the genetic algorithm, and dynamical parameters are adopted in PSO. The new population is produced through three approaches to improve the global optimization performance, which are elitist strategy, PSO strategy and improved genetic algorithm ( IGA) strategy. The effectiveness of the proposed algorithm has been compared with GAs and PSO, synthesizing a circular array, a linear array and a base station array. Results show that the proposed algorithm is able to adapt itself to different electromagnetic optimization problems more effectively.
Keywords:
PARTICLE SWARM OPTIMIZATION
CLONAL SELECTION ALGORITHM
GENETIC ALGORITHMS
SIDELOBE LEVEL
NULL
DESIGN
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