Programming and implementation of the chicken swarm optimization (cso) algorithm in optimization problems

The aim of this Master's thesis is to develop and assess an optimization software based on the cooperative behavior of individual members of the three subgroups (roosters, hens, and chicks) within a chicken swarm as they search for food.The performance of the Chicken Swarm Optimization (CSO) algorithm is compared with Evolutionary Algorithm (EA) and Metamodel-Assisted Evolutionary Algortihm (MAEA), which are implemeted through the EASY software, developed by the Parallel CFD & Optimization Unit of the School of Mechanical Engineering of NTUA.In the future, an overarching objective is to potentially integrate promising elements of the proposed algorithm into EASY, to further improve its performance.As part of the Swarm Intelligence family of algorithms, the CSO technique manages populations of chickens that move through the design space in search of optimal solutions.This thesis investigates the interactions among the three aforementioned subpopulations that comprise the total swarm, the parameter settings related to their rearrangement, and the overall performance of CSO compared to EA and MAEA.This algorithm effectively handles both Single-Objective (SOO) and Multi-Objective Optimization (MOO) problems.New features have also been introduced by the author beyond what is described in the literature, aimed at further improving the algorithm's 5 Conclusions Bibliography 5.