Nature Based Search Algortihms for Antenna Array Optimization Problems
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Stochastic techniques which have taken inspiration from Nature are called Nature Based Search Algorithms (NBSA). They make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. They can work efficiently even when the number of variables are large i.e., the solution space is large and multi-dimensional. The book covers some of these nature inspired techniques that can be used for optimization problems. Some popular NBS Algorithms are Genetic Algorithms (GA), Particle swarm optimization (PSO) and Ant colony organization (ACO). GA is mode...
Stochastic techniques which have taken inspiration from Nature are called Nature Based Search Algorithms (NBSA). They make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. They can work efficiently even when the number of variables are large i.e., the solution space is large and multi-dimensional. The book covers some of these nature inspired techniques that can be used for optimization problems. Some popular NBS Algorithms are Genetic Algorithms (GA), Particle swarm optimization (PSO) and Ant colony organization (ACO). GA is modeled after the Darwin's theory of evolution. PSO draws its inspiration from a group of birds flocking together and ACO is based on the foraging behavior of ant colonies. All of them are inspired from nature, and work on a set of population of solutions rather than from a single solution. The book describes use of these techniques to solve, an electromagnetic antenna array thinning problem. From mathematical perspective this this a nonlinear, multi-dimensional problem with multiple objectives and many constraints.
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