A hybridized algorithm of combination of modified teaching-learning-based optimization and modified chicken swarm optimization

Abstract To overcome the defect of Teaching-Learning-based Optimization (TLBO) that it converges to the global optima with a relative slow speed, a modified Teaching-Learning-based Optimization (mTLBO) is proposed to enhance the convergence rate of TLBO. Then, the updation equation of rooster of Chicken Swarm Optimization (CSO) is improved to obtain modified Chicken Swarm Optimization (mCSO) to boost the global exploring capacity of CSO. Moreover, mTLBO and mCSO are hybridized to produce mTLBO-mCSO to get a comprehensive capability of fast searching and global exploring. Above all, relevant simulations are conducted using seven unimodal benchmark functions and six multimodal benchmark functions and related analyses are given for them. The simulation results reveal that mTLBO-mCSO works better or at least equal to basic algorithms for the vast majority of test functions and worse in extremely few cases.