Boosted Reptile Search Algorithm for Engineering and Optimization Problems

Recently, various metaheuristic (MH) optimization algorithms have been presented and applied to solve complex engineering and optimization problems. One main category of MH algorithms is the naturally inspired swarm intelligence (SI) algorithms. SI methods have shown great performance on different problems. However, individual MH and SI methods face some shortcomings, such as trapping at local optima. To solve this issue, hybrid SI methods can perform better than individual ones. In this study, we developed a boosted version of the reptile search algorithm (RSA) to be employed for different complex problems, such as intrusion detection systems (IDSs) in cloud-IoT environments, as well as different optimization and engineering problems. This modification was performed by employing the operators of the red fox algorithm (RFO) and triangular mutation operator (TMO). The aim of using the RFO was to boost the exploration of the RSA, whereas the TMO was used for enhancing the exploitation stage of the RSA. To assess the developed approach, called RSRFT, a set of six constrained engineering benchmarks was used. The experimental results illustrated the ability of RSRFT to find the solution to those tested engineering problems. In addition, it outperformed the other well-known optimization techniques that have been used to handle these problems.

Automated summary

Recently, various metaheuristic optimization algorithms have been applied to solve complex engineering and optimization problems. Swarm intelligence algorithms, a type of metaheuristic algorithm, have shown great performance but face shortcomings such as trapping at local optima. To address this, a boosted version of the reptile search algorithm (RSA) was developed by employing the operators of the red fox algorithm (RFO) and triangular mutation operator (TMO). The approach, called RSRFT, outperformed other optimization techniques in solving constrained engineering benchmarks.