An Enhanced Grasshopper Optimization Algorithm to the Bin Packing Problem
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 con...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/3894987 |
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| _version_ | 1850230007424614400 |
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| author | Hangwei Feng Hong Ni Ran Zhao Xiaoyong Zhu |
| author_facet | Hangwei Feng Hong Ni Ran Zhao Xiaoyong Zhu |
| author_sort | Hangwei Feng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2a3c8ac8b3e2400f9902a238c084c65f |
| institution | OA Journals |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-2a3c8ac8b3e2400f9902a238c084c65f2025-08-20T02:04:00ZengWileyJournal of Control Science and Engineering1687-52491687-52572020-01-01202010.1155/2020/38949873894987An Enhanced Grasshopper Optimization Algorithm to the Bin Packing ProblemHangwei Feng0Hong Ni1Ran Zhao2Xiaoyong Zhu3National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaNational Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaNational Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaNational Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaThe 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.http://dx.doi.org/10.1155/2020/3894987 |
| spellingShingle | Hangwei Feng Hong Ni Ran Zhao Xiaoyong Zhu An Enhanced Grasshopper Optimization Algorithm to the Bin Packing Problem Journal of Control Science and Engineering |
| title | An Enhanced Grasshopper Optimization Algorithm to the Bin Packing Problem |
| title_full | An Enhanced Grasshopper Optimization Algorithm to the Bin Packing Problem |
| title_fullStr | An Enhanced Grasshopper Optimization Algorithm to the Bin Packing Problem |
| title_full_unstemmed | An Enhanced Grasshopper Optimization Algorithm to the Bin Packing Problem |
| title_short | An Enhanced Grasshopper Optimization Algorithm to the Bin Packing Problem |
| title_sort | enhanced grasshopper optimization algorithm to the bin packing problem |
| url | http://dx.doi.org/10.1155/2020/3894987 |
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