Abstract
Grey wolf optimizer (GWO) is a new meta-heuristic algorithm. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Three main stages of hunting include: encircling, tracking and attacking. It is easy to fall into local optimum when used to optimize high-dimensional data, and there is imbalance between exploration and exploitation. An improved grey wolf optimizer based on tracking mode and seeking mode is proposed to improve the diversity of the population and the ability of the algorithm to balance exploration and exploitation. The algorithm is verified by simulation experiments in three parts. Firstly, the proposed grey wolf optimizer based on tracking mode (TGWO), the improved grey wolf optimizer based on seeking mode (SGWO), the improved grey wolf optimizer based on tracking and seeking mode (TSGWO), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), Sine Cosine Algorithm (SCA), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA) and Moth-flame Optimization (MFO) are adopted to optimize 21 typical benchmark functions respectively, and the obtained statistical simulation results are compared; Secondly, the improved algorithm proposed in this paper is compared with Binary Grey Wolf Optimizer (BGWO), Hybrid PSOGWO Optimization (PSOGWO) and GWO Algorithm Integrated with Cuckoo Search (GWOCS); Finally, it is applied to the lightest design engineering problem of pressure vessels. Simulation results show that the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration. The improved grey wolf optimizer based on tracking mode and seeking mode can better solve function optimization and classical engineering problems with constraints. It was found the improved grey wolf optimizer based on tracking mode has the high precision and the characteristics of balanced exploration and exploitation.
Highlights
There are more and more demands for solving various complexity problems
This study presented an improved grey wolf optimizer based on tracking mode and seeking mode
The results showed that tracking mode based grey wolf optimizer (TGWO) was able to provide highly competitive results compared to well-known heuristics such as Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), Sine Cosine Algorithm (SCA), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Moth-flame Optimization (MFO), Binary Grey Wolf Optimizer (BGWO), PSOGWO, GWO Algorithm Integrated with Cuckoo Search (GWOCS)
Summary
There are more and more demands for solving various complexity problems. In recent years, the emergence of metaheuristic algorithms for bionics has emerged in an endless stream, and there is a faster way to solve many complex optimization problems. Guo et al.: Improved GWO Based on Tracking and Seeking Modes to Solve Function Optimization Problems and it is difficult to obtain optimal solutions. This is an important reason why researchers are committed to practical optimization problems. The Grey Wolf Optimizer (GWO) was proposed by Mirjalili [33], which is a new heuristic algorithm to solve the optimization problems. Zawbaa et al propose a combination of antlion optimization and grey wolf optimization in a new algorithm called ALO-GWO [43]. This paper is compared with Binary?Grey?Wolf?Optimizer (BGWO), Hybrid PSOGWO Optimization (PSOGWO) and GWO Algorithm Integrated with Cuckoo Search (GWOCS); it is applied to the lightest design engineering problem of pressure vessels
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