A New Hybrid Particle Swarm Optimization-Teaching Learning Based Optimization for Solving Optimization Problems

Abstract

Abstract This research paper develops a novel hybrid approach, called hybrid Particle Swarm Optimization-Teaching Learning Based Optimization (hPSO-TLBO), by combining two metaheuristic algorithms to solve optimization problems. The hPSO-TLBO approach integrates the exploitation capabilities of PSO with the exploration abilities of TLBO, resulting in a synergistic combination. The algorithm is presented in detail, accompanied by a comprehensive mathematical model. A group of benchmarks is used to evaluate the effectiveness of hPSO-TLBO, covering various types such as unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. In addition, CEC 2017 benchmark problems are also utilized for evaluation purposes. The optimization results clearly demonstrate that hPSO-TLBO performs remarkably well in addressing the benchmark functions. It exhibits a remarkable ability to explore and exploit the search space while maintaining a balanced approach throughout the optimization process. Furthermore, a comparative analysis is conducted to evaluate the performance of hPSO-TLBO against twelve widely recognized metaheuristic algorithms. The evaluation of the experimental findings illustrates that hPSO-TLBO consistently outperforms the competing algorithms across various benchmark functions, showcasing its superior performance. The successful deployment of hPSO-TLBO in addressing four engineering challenges highlights its effectiveness in tackling real-world applications.