On the performance improvement of elephant herding optimization algorithm

Abstract Thanks to fewer numbers of control parameters and easier implementation, the Elephant Herding Optimization (EHO) has been gaining research interest during the past decade. In our paper, to understand the impact of the control parameters, a parametric study of the EHO is carried out using a standard test bench, engineering problems, and real-world problems. On top of that, the main aim of this paper is to propose different approaches to enhance the performance of the original EHO, i.e., cultural-based, alpha-tuning, and biased initialization EHO. Acomparative study has been made between these EHO variants and the state-of-the-art swarm optimization methods. Case studies ranging from the recent test bench problems of CEC 2016 to the popular engineering problems of gear train, welded beam, three-bar truss design problem, continuous stirred tank reactor, and fed-batch fermentor are used to validate and test the performances of the proposed EHOs against the existing techniques. Numerical results show that the performances of the three new EHOs are better than or competitive with the population-based optimization algorithms.