Evolutionary Search Algorithm Based on Hypercube Optimization For High-Dimensional Functions
Mustafa Tunay · DergiPark (Istanbul University) · 2020
This study paper is devoted to the design of a novel evolutionary search algorithm to solve optimization of multivariate systems.Nowadays for many real world optimization problems the designing of evolutionary search algorithm with high optimization accuracy is a crucial optimization of multivariate systems.The proposed optimization search algorithm is a new intense stochastic search method that is based on a hypercube evolution driven.This algorithm is inspired from the behaviour of a pigeon that discovers new location of areas for seeds in natural world.The hypercube is used a statement that shows the area of life for the behaviour of a pigeon in real life.The performance of the proposed algorithm is tested on optimization functions as some Benchmark function and test suite functions including; four unimodal functions and composition functions.The performance of the proposed algorithm are shown much better experimental results in EAs and is encouraging in terms of solution accuracy for global optimization.In addition, the proposed algorithm approach is applied to solve a timetabling problem as two exams in adjoining periods, conflict of exams, two or more exams in one day etc.They are very difficult to solve for many institutions of higher education and as resulted in a significant increase in their complexity.