Optimization of High-Dimensional Functions through Hypercube Evaluation

A novel learning algorithm for solving global numerical optimization problems is proposed. The proposed learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The HO algorithm comprises the initialization and evaluation process, displacement-shrink process, and searching space process. The initialization and evaluation process initializes initial solution and evaluates the solutions in given hypercube. The displacement-shrink process determines displacement and evaluates objective functions using new points, and the search area process determines next hypercube using certain rules and evaluates the new solutions. The algorithms for these processes have been designed and presented in the paper. The designed HO algorithm is tested on specific benchmark functions. The simulations of HO algorithm have been performed for optimization of functions of 1000-, 5000-, or even 10000 dimensions. The comparative simulation results with other approaches demonstrate that the proposed algorithm is a potential candidate for optimization of both low and high dimensional functions

Modeling of Chaotic Behavior of Benchmark Datasets using Hybrid Heuristic Optimization

Optimization is required for producing the best results. Heuristic algorithm is one of the techniques which can be used for finding best results. By making use of artificial neural network and particle swarm optimization values can be predicted and chaotic signals can be modeled which forms the base of this project. The chaotic signals here use are Mackey series and Box Jenkins Gas Furnace data series. The results of this work shows the comparative study of predicted number of neurons in the second hidden layer also it gives the value of mean square error while making the prediction