A Collision Avoidance Method for Intelligent Ship Based on the Improved Bacterial Foraging Optimization Algorithm
The bacterial foraging optimization algorithm (BFOA) is an intelligent population optimization algorithm widely used in collision avoidance problems; however, the BFOA is inappropriate for the intelligent ship collision avoidance planning with high safety requirements because BFOA converges slowly,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2021/6661986 |
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| _version_ | 1832550234732888064 |
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| author | Xingzhong Wang Xinghua Kou Jinfeng Huang Xianchun Tan |
| author_facet | Xingzhong Wang Xinghua Kou Jinfeng Huang Xianchun Tan |
| author_sort | Xingzhong Wang |
| collection | DOAJ |
| description | The bacterial foraging optimization algorithm (BFOA) is an intelligent population optimization algorithm widely used in collision avoidance problems; however, the BFOA is inappropriate for the intelligent ship collision avoidance planning with high safety requirements because BFOA converges slowly, optimizes inaccurately, and has low stability. To fix the above shortcomings of BFOA, an autonomous collision avoidance algorithm based on the improved bacterial foraging optimization algorithm (IBFOA) is demonstrated in this paper. An adaptive diminishing fractal dimension chemotactic step length is designed to replace the fixed step length to achieve the adaptive step length adjustment, an optimal swimming search method is proposed to solve the invalid searching and repeated searching problems of the traditional BFOA, and the adaptive migration probability is developed to take the place of the fixed migration probability to prevent elite individuals from being lost in BOFA. The simulation of benchmark tests shows that the IBFOA has a better convergence speed, optimized accuracy, and higher stability; according to a collision avoidance simulation of intelligent ships which applies the IBFOA, it can realize the autonomous collision avoidance of intelligent ships in dynamic obstacles environment is quick and safe. This research can also be used for intelligent collision avoidance of automatic driving ships. |
| format | Article |
| id | doaj-art-add553841ea747728934ed094a048342 |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-add553841ea747728934ed094a0483422025-02-03T06:07:17ZengWileyJournal of Robotics1687-96001687-96192021-01-01202110.1155/2021/66619866661986A Collision Avoidance Method for Intelligent Ship Based on the Improved Bacterial Foraging Optimization AlgorithmXingzhong Wang0Xinghua Kou1Jinfeng Huang2Xianchun Tan3China Ship Development and Design Center, Wuhan, ChinaChina Ship Development and Design Center, Wuhan, ChinaChina Ship Development and Design Center, Wuhan, ChinaChina Ship Development and Design Center, Wuhan, ChinaThe bacterial foraging optimization algorithm (BFOA) is an intelligent population optimization algorithm widely used in collision avoidance problems; however, the BFOA is inappropriate for the intelligent ship collision avoidance planning with high safety requirements because BFOA converges slowly, optimizes inaccurately, and has low stability. To fix the above shortcomings of BFOA, an autonomous collision avoidance algorithm based on the improved bacterial foraging optimization algorithm (IBFOA) is demonstrated in this paper. An adaptive diminishing fractal dimension chemotactic step length is designed to replace the fixed step length to achieve the adaptive step length adjustment, an optimal swimming search method is proposed to solve the invalid searching and repeated searching problems of the traditional BFOA, and the adaptive migration probability is developed to take the place of the fixed migration probability to prevent elite individuals from being lost in BOFA. The simulation of benchmark tests shows that the IBFOA has a better convergence speed, optimized accuracy, and higher stability; according to a collision avoidance simulation of intelligent ships which applies the IBFOA, it can realize the autonomous collision avoidance of intelligent ships in dynamic obstacles environment is quick and safe. This research can also be used for intelligent collision avoidance of automatic driving ships.http://dx.doi.org/10.1155/2021/6661986 |
| spellingShingle | Xingzhong Wang Xinghua Kou Jinfeng Huang Xianchun Tan A Collision Avoidance Method for Intelligent Ship Based on the Improved Bacterial Foraging Optimization Algorithm Journal of Robotics |
| title | A Collision Avoidance Method for Intelligent Ship Based on the Improved Bacterial Foraging Optimization Algorithm |
| title_full | A Collision Avoidance Method for Intelligent Ship Based on the Improved Bacterial Foraging Optimization Algorithm |
| title_fullStr | A Collision Avoidance Method for Intelligent Ship Based on the Improved Bacterial Foraging Optimization Algorithm |
| title_full_unstemmed | A Collision Avoidance Method for Intelligent Ship Based on the Improved Bacterial Foraging Optimization Algorithm |
| title_short | A Collision Avoidance Method for Intelligent Ship Based on the Improved Bacterial Foraging Optimization Algorithm |
| title_sort | collision avoidance method for intelligent ship based on the improved bacterial foraging optimization algorithm |
| url | http://dx.doi.org/10.1155/2021/6661986 |
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