Algorithm tuners for PSO methods and genetic programming techniques for learning tuning rules

In recent years, improvements in processing power have allowed the application of optimization methods to complicated large optimization problems. Among these methods, heuristic optimization techniques such as particle swarm optimization (PSO) have been a particular focus of attention because of their simplicity, performance, and easy software implementation. However, there is no solid theoretical foundation for analyzing the convergence of these algorithms, and in practice, their rate of convergence is often determined by the choice of parameters. For this reason, the algorithm's parameters must be tuned appropriately for each new optimization problem we want to solve, and in some cases the parameters must be varied as the algorithm is updated. In this paper, we combine a feedback element as an algorithm tuner with an original algorithm; the resulting algorithm is applied to the optimization problem in question, and we use genetic programming (GP) to generate tuning rules to automatically tune the algorithm, according to its current state, as the algorithm is updated. More specifically, we adopt PSO as a heuristic optimization method, and we augment PSO by using GP as a meta-algorithm to solve the learning problem of automatically generating tuning rules for the parameters in the PSO algorithm. This leads to the proposed method for generating parameter tuning rules to solve optimization problems more efficiently.