Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns
Ant Lion Optimizer (ALO) is a new meta-heuristic algorithm that simulates the ant lion predator mechanism in nature. Five main steps of hunting include: random walks of ants, building traps, trapping in antlion's pits, sliding ants towards antlion, catching prey and re-building pits. As the pre...
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| Format: | Article |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8967089/ |
| _version_ | 1828977356739444736 |
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| author | M. W. Guo J. S. Wang L. F. Zhu S. S. Guo W. Xie |
| author_facet | M. W. Guo J. S. Wang L. F. Zhu S. S. Guo W. Xie |
| author_sort | M. W. Guo |
| collection | DOAJ |
| description | Ant Lion Optimizer (ALO) is a new meta-heuristic algorithm that simulates the ant lion predator mechanism in nature. Five main steps of hunting include: random walks of ants, building traps, trapping in antlion's pits, sliding ants towards antlion, catching prey and re-building pits. As the predator radius of antlion decreases with the number of iterations, there is an unbalanced between the ant lion optimizer between exploration and exploitation, and it is easy to fall into the local optimal solution. An improved ant lion optimizer based on spiral complex path searching pattern is proposed, where eight spiral paths (Hypotrochoid, Rose spiral curve, Logarithmic spiral curve, Archimedes spiral curve, Epitrochoid, Inverse spiral curve, Cycloid, Overshoot parameter setting of the spiral) searching strategies were adopted to improve the diversity of the population and the ability of the algorithm to balance exploration and exploitation. The proposed algorithm can accelerate the convergence speed of ALO and improve its performance. The algorithm is verified by simulation experiments in three parts. Firstly, 28 function optimization problems were adopted to test the optimization performance of the improved ALO. Secondly, it is applied to the lightest design engineering problem of pressure vessels. Finally, the spiral complex path searching patterns are introduced into the muti-objective ALO and 4 typical muti-objective functions are optimized. Simulation results show that the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration. The improved algorithm can better solve function optimization, classical engineering problems with constraints and multi-objective function optimization problems. The improved ALO based on the spiral complex path searching mode has the characteristics of balanced exploration and exploitation, fast convergence speed and high precision. |
| first_indexed | 2024-12-14T14:57:27Z |
| format | Article |
| id | doaj.art-392054b6654140049ebbc2bfa7b0b5d2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T14:57:27Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-392054b6654140049ebbc2bfa7b0b5d22022-12-21T22:56:58ZengIEEEIEEE Access2169-35362020-01-018220942212610.1109/ACCESS.2020.29689438967089Improved Ant Lion Optimizer Based on Spiral Complex Path Searching PatternsM. W. Guo0J. S. Wang1https://orcid.org/0000-0002-8853-1927L. F. Zhu2S. S. Guo3https://orcid.org/0000-0003-1883-9958W. Xie4School of Electronic and Information Engineering, University of Science and Technology at Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology at Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology at Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology at Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology at Liaoning, Anshan, ChinaAnt Lion Optimizer (ALO) is a new meta-heuristic algorithm that simulates the ant lion predator mechanism in nature. Five main steps of hunting include: random walks of ants, building traps, trapping in antlion's pits, sliding ants towards antlion, catching prey and re-building pits. As the predator radius of antlion decreases with the number of iterations, there is an unbalanced between the ant lion optimizer between exploration and exploitation, and it is easy to fall into the local optimal solution. An improved ant lion optimizer based on spiral complex path searching pattern is proposed, where eight spiral paths (Hypotrochoid, Rose spiral curve, Logarithmic spiral curve, Archimedes spiral curve, Epitrochoid, Inverse spiral curve, Cycloid, Overshoot parameter setting of the spiral) searching strategies were adopted to improve the diversity of the population and the ability of the algorithm to balance exploration and exploitation. The proposed algorithm can accelerate the convergence speed of ALO and improve its performance. The algorithm is verified by simulation experiments in three parts. Firstly, 28 function optimization problems were adopted to test the optimization performance of the improved ALO. Secondly, it is applied to the lightest design engineering problem of pressure vessels. Finally, the spiral complex path searching patterns are introduced into the muti-objective ALO and 4 typical muti-objective functions are optimized. Simulation results show that the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration. The improved algorithm can better solve function optimization, classical engineering problems with constraints and multi-objective function optimization problems. The improved ALO based on the spiral complex path searching mode has the characteristics of balanced exploration and exploitation, fast convergence speed and high precision.https://ieeexplore.ieee.org/document/8967089/Ant lion optimizerspiral complex pathfunction optimizationconstrained optimizationmuti-objective optimization |
| spellingShingle | M. W. Guo J. S. Wang L. F. Zhu S. S. Guo W. Xie Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns IEEE Access Ant lion optimizer spiral complex path function optimization constrained optimization muti-objective optimization |
| title | Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns |
| title_full | Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns |
| title_fullStr | Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns |
| title_full_unstemmed | Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns |
| title_short | Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns |
| title_sort | improved ant lion optimizer based on spiral complex path searching patterns |
| topic | Ant lion optimizer spiral complex path function optimization constrained optimization muti-objective optimization |
| url | https://ieeexplore.ieee.org/document/8967089/ |
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