A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization

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. INDEX TERMS Grasshopper optimization algorithm, invasive weed optimization, grouping strategy, random walk strategy, global optimization.

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

“…Content may change prior to final publication. [122] hybrid-GOA-GA [123] GOA-jDE [124] DE-GOA-KELM [125] HAGOA [126] GWGHA [127] BGOA [128] HGOA [129] SM-GNCSOA [130] GOALO [131] HAGOA [132] TLGOA [133] IWGOA [128] GOA-SVR [134] GOA-SVM [72] GOA-SVM [73] GOA-SVM [135] GOA-ImLSTM [136] GOA-MSVM [137] GOA-SVM [138] GOA was proposed by hybridizing GOA with GWO for tackling the text feature selection problem. GWO-GOA was assessed using eight datasets taking into account five metrics (i.e accuracy, sensitivity, specificity, precision, recall, and F-measure).…”

“…Yue and Zhang [128] suggested a hybrid model (BGOA) by hybridizing GOA with BA for solving global optimization problems. BGOA was assessed using 23 test benchmark test functions comparing with GA, BA, and the standard GOA.…”

A minimum cross-entropy multi-thresholds segmentation algorithm based on improved WOA

“…Invasive Weed Optimization (IWO) is a new intelligent optimization algorithm proposed by Mehrabian et al IWO [4] algorithm propagates multiple offspring from individuals with excellent fitness and the offspring are randomly distributed around the parent in the form of normal distribution. The offspring and those individuals are sorted by fitness [5].…”

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

“…Heuristic algorithms can solve various optimization problems including the feature selection. Due to their effectiveness and simplicity, many heuristic algorithms have been proposed for solving the feature selection problems, e.g., GA [15], PSO [16], GWO [17], flower pollination algorithm (FPA) [18], artificial bee colony (ABC) [19], bacterial foraging optimization (BFO) [20], BA [21], cuckoo search (CS) [22], firefly algorithm (FA) [23], whale optimization algorithm (WOA) [24], grasshopper optimization algorithm (GOA) [25]. Recently, more and more heuristic algorithms are proposed to deal with many kinds of optimization problems.…”