Abstract
The grasshopper optimization algorithm (GOA) is a promising metaheuristic algorithm for optimization. In the current study, a hybrid grasshopper optimization algorithm with invasive weed optimization (IWGOA) is proposed. The invasive weed optimization (IWO) and random walk strategy are helpful for improving the search precision and accelerating the convergence rate. In addition, the exploration and exploitation capability of the IWGOA algorithm are further enhanced by the grouping strategy. The IWGOA algorithm is compared with some typical and latest optimization algorithms including genetic algorithm (GA), moth-flame optimization algorithm (MFO), particle swarm optimization and gravitational search algorithm (PSOGSA), ant lion optimizer (ALO), conventional GOA algorithm, chaotic GOA algorithm (CGOA) and opposition-based learning GOA algorithm (OBLGOA) on 23 benchmark functions and 30 CEC 2014 benchmark functions. The results show that the IWGOA algorithm is able to provide better outcomes than the other algorithms on the majority of the benchmark functions. Additionally, the IWGOA algorithm is applied to multi-level image segmentation, and obtains promising results. All of these findings demonstrate the superiority of the IWGOA algorithm.
Highlights
Optimization exists in many fields such as engineering [1], image processing [2], energy [3], feature selection [4] and industrial applications [5]
(3) The proposed IWGOA algorithm is applied to multilevel image segmentation and compare the performance with the genetic algorithm (GA), moth-flame optimization algorithm (MFO), particle swarm optimization and gravitational search algorithm (PSOGSA), ant lion optimizer (ALO), grasshopper optimization algorithm (GOA), chaotic GOA algorithm (CGOA) and opposition-based learning GOA algorithm (OBLGOA) based methods
The invasive weed optimization (IWO) algorithm and random walk strategy are integrated into the GOA algorithm to enhance the exploitation capability
Summary
Optimization exists in many fields such as engineering [1], image processing [2], energy [3], feature selection [4] and industrial applications [5]. The GOA not always performs well in solving optimization tasks, because the algorithm has drawbacks of limited local search capability and falling into the local best solutions [13]. Wu J et al proposed an adaptive grasshopper optimization algorithm (AGOA) to solve trajectory optimization problem. In the conventional GOA algorithm, the movement of each grasshopper neglects the objective function value, which leads that the grasshopper with better objective function value may search the best solutions with a big step. (3) The proposed IWGOA algorithm is applied to multilevel image segmentation and compare the performance with the GA, MFO, PSOGSA, ALO, GOA, CGOA and OBLGOA based methods.
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