An Enhanced Slime Mould Algorithm Combines Multiple Strategies
In recent years, due to the growing complexity of real-world problems, researchers have been favoring stochastic search algorithms as their preferred method for problem solving. The slime mould algorithm is a high-performance, stochastic search algorithm inspired by the foraging behavior of slime mo...
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MDPI AG
2023-09-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/12/10/907 |
| _version_ | 1827721784356503552 |
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| author | Wenqing Xiong Dahai Li Donglin Zhu Rui Li Zhang Lin |
| author_facet | Wenqing Xiong Dahai Li Donglin Zhu Rui Li Zhang Lin |
| author_sort | Wenqing Xiong |
| collection | DOAJ |
| description | In recent years, due to the growing complexity of real-world problems, researchers have been favoring stochastic search algorithms as their preferred method for problem solving. The slime mould algorithm is a high-performance, stochastic search algorithm inspired by the foraging behavior of slime moulds. However, it faces challenges such as low population diversity, high randomness, and susceptibility to falling into local optima. Therefore, this paper presents an enhanced slime mould algorithm that combines multiple strategies, called the ESMA. The incorporation of selective average position and Lévy flights with jumps in the global exploration phase improves the flexibility of the search approach. A dynamic lens learning approach is employed to adjust the position of the optimal slime mould individual, guiding the entire population to move towards the correct position within the given search space. In the updating method, an improved crisscross strategy is adopted to reorganize the slime mould individuals, which makes the search method of the slime mould population more refined. Finally, the performance of the ESMA is evaluated using 40 well-known benchmark functions, including those from CEC2017 and CEC2013 test suites. It is also recognized by Friedman’s test as statistically significant. The analysis of the results on two real-world engineering problems demonstrates that the ESMA presents a substantial advantage in terms of search capability. |
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| format | Article |
| id | doaj.art-ddcc2cd0229b43ff9e96026e43a3ddce |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-10T21:26:53Z |
| publishDate | 2023-09-01 |
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| series | Axioms |
| spelling | doaj.art-ddcc2cd0229b43ff9e96026e43a3ddce2023-11-19T15:37:44ZengMDPI AGAxioms2075-16802023-09-01121090710.3390/axioms12100907An Enhanced Slime Mould Algorithm Combines Multiple StrategiesWenqing Xiong0Dahai Li1Donglin Zhu2Rui Li3Zhang Lin4Information Engineering School, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaInformation Engineering School, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaCollege of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, ChinaSchool of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, ChinaIn recent years, due to the growing complexity of real-world problems, researchers have been favoring stochastic search algorithms as their preferred method for problem solving. The slime mould algorithm is a high-performance, stochastic search algorithm inspired by the foraging behavior of slime moulds. However, it faces challenges such as low population diversity, high randomness, and susceptibility to falling into local optima. Therefore, this paper presents an enhanced slime mould algorithm that combines multiple strategies, called the ESMA. The incorporation of selective average position and Lévy flights with jumps in the global exploration phase improves the flexibility of the search approach. A dynamic lens learning approach is employed to adjust the position of the optimal slime mould individual, guiding the entire population to move towards the correct position within the given search space. In the updating method, an improved crisscross strategy is adopted to reorganize the slime mould individuals, which makes the search method of the slime mould population more refined. Finally, the performance of the ESMA is evaluated using 40 well-known benchmark functions, including those from CEC2017 and CEC2013 test suites. It is also recognized by Friedman’s test as statistically significant. The analysis of the results on two real-world engineering problems demonstrates that the ESMA presents a substantial advantage in terms of search capability.https://www.mdpi.com/2075-1680/12/10/907enhanced slime mould algorithmglobal explorationlens learningvertical and horizontal crossoverengineering problem optimization |
| spellingShingle | Wenqing Xiong Dahai Li Donglin Zhu Rui Li Zhang Lin An Enhanced Slime Mould Algorithm Combines Multiple Strategies Axioms enhanced slime mould algorithm global exploration lens learning vertical and horizontal crossover engineering problem optimization |
| title | An Enhanced Slime Mould Algorithm Combines Multiple Strategies |
| title_full | An Enhanced Slime Mould Algorithm Combines Multiple Strategies |
| title_fullStr | An Enhanced Slime Mould Algorithm Combines Multiple Strategies |
| title_full_unstemmed | An Enhanced Slime Mould Algorithm Combines Multiple Strategies |
| title_short | An Enhanced Slime Mould Algorithm Combines Multiple Strategies |
| title_sort | enhanced slime mould algorithm combines multiple strategies |
| topic | enhanced slime mould algorithm global exploration lens learning vertical and horizontal crossover engineering problem optimization |
| url | https://www.mdpi.com/2075-1680/12/10/907 |
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