Abstract
The grasshopper optimization algorithm (GOA) is a novel metaheuristic algorithm. Because of its easy deployment and high accuracy, it is widely used in a variety of industrial scenarios and obtains good solution. But, at the same time, the GOA algorithm has some shortcomings: (1) original linear convergence parameter causes the processes of exploration and exploitation unbalanced; (2) unstable convergence speed; and (3) easy to fall into the local optimum. In this paper, we propose an enhanced grasshopper optimization algorithm (EGOA) using a nonlinear convergence parameter, niche mechanism, and the β-hill climbing technique to overcome the abovementioned shortcomings. In order to evaluate EGOA, we first select the benchmark set of GOA authors to test the performance improvement of EGOA compared to the basic GOA. The analysis includes exploration ability, exploitation ability, and convergence speed. Second, we select the novel CEC2019 benchmark set to test the optimization ability of EGOA in complex problems. According to the analysis of the results of the algorithms in two benchmark sets, it can be found that EGOA performs better than the other five metaheuristic algorithms. In order to further evaluate EGOA, we also apply EGOA to the engineering problem, such as the bin packing problem. We test EGOA and five other metaheuristic algorithms in SchWae2 instance. After analyzing the test results by the Friedman test, we can find that the performance of EGOA is better than other algorithms in bin packing problems.
Highlights
Bin packing problem (BPP) is one of the most important combinatorial optimization problems
To investigate the differences between the results obtained by the enhanced grasshopper optimization algorithm (EGOA) and those obtained by the other algorithms, a nonparametric Wilcoxon rank sum test [31] with a significance level of 5% was adopted in this study. e Wilcoxon rank sum test generates a p value to determine if two datasets came from the same distributed set
In order to test the validity of the proposed algorithm, some experiments are conducted on selected benchmarks from the ”SchWae2” instances [38] in which EGOA is compared to other metaheuristic algorithms
Summary
Bin packing problem (BPP) is one of the most important combinatorial optimization problems. In [23], the authors apply the basic multiobjective GOA to solve several benchmark problems with superior performance. The improvement of algorithm in other shortcomings is not significant because they consider neither the balance between exploration and exploitation nor the relationship between the diversity of the population and the convergence speed during the group optimization process. To overcome these disadvantages, this paper proposed an enhanced grasshopper optimization algorithm. (i) We introduced a nonlinear convergence parameter into the basic GOA to balance the exploration and exploitation phases to improve the overall performance.
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