A Two-Phase Hybrid GWO:TP-AB Algorithm for Solving Optimization Problems

INTRODUCTION: Population-based algorithms are popular stochastic algorithms used for solving optimization problems. Grey Wolf Optimizer (GWO) proposed in 2014 is one of the most studied algorithms in the past decade. Population-based two-phase trigonometric AB (TP-AB) is a recently proposed algorithm for handling optimization problems. OBJECTIVES: The objective of this work is to propose one new hybrid algorithm  combining the strengths of two better performing algorithms in two different phases. The performance is analysed using popular benchmarks and the results are compared with a few popular algorithms available in the literature. METHODS: One new two-phase hybrid algorithm is designed by taking GWO in its first phase and the second phase of the TP-AB algorithm in the second phase. In the second phase, the Levy Strategy is introduced which was not in the original TP-AB algorithm. RESULTS: The performance of the new hybrid GWO:TP-AB algorithm is analysed using 23 classic mathematical functions, 10 numbers of the CEC2019 dataset and 18 real-world engineering problems In addition, to demonstrate its capability to handle higher dimension problems, 13 scalable problems are solved. These include unimodal and multimodal instances with dimensions 30, 100, 500 and 1000. CONCLUSION: The results demonstrate the better performance of the GWO:TP-AB algorithm when compared to several optimization algorithms of recent times.