Evolutionary optimization algorithms and their applications in array signal processing

Abstract

Evolutionary algorithm is one of the hottest research topics in recent decades. The most well-known two evolutionary algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), were studied in this project. Effectiveness of various implementations of GA operators (parent selection and crossover) were analyzed and tested via a predefined test suite. The comparative results are tabulated and discussed in this report. Recommendations for later GA users based on the comparative results have also been given. Evolutionary algorithm has been applied in many fields. In this project, their applications in array signal processing was researched. Applications that this project focused are adaptive beamforming and passive array calibration which are well-known important problems in array signal processing. Algorithm with PSO was proposed to solve the beamforming problem and algorithm embedded with GA is applied to passive array calibration. Numeric examples are presented and the simulation results show that evolutionary algorithms do have great potential in solving complex signal processing problems.