Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications
Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Antenna Design Using Genetic Algorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Design of a Six-Sector Switched Parasitic Planar Array Using the Method of Genetic Algorithms
Wireless Personal Communications: An International Journal
Wireless Personal Communications: An International Journal
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Antenna Theory: Analysis and Design
Antenna Theory: Analysis and Design
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
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This paper deals with the design of electronically steerable linear arrays for intelligent antenna systems. The design problem is modeled as a multi-objective optimization problem with non-linear constraints. The well-known NSGA-II and SPEA 2 algorithms are employed as the methodologies to solve the resulting optimization problem. The main goal and contribution of this paper is computation of the trade-off curves between side lobe level and main beam width for steerable linear arrays. The addressed problem considers a driving-point impedance restriction placed on each element in the array. This consideration makes the problem more restrictive and therefore more difficult to solve. Experimental results show the effectiveness of the algorithms for the design of steerable linear arrays.