SHuffled Ant Lion Optimization approach with an exponentially weighted random walk strategy

Ant Lion Optimization (ALO) method is one of the population-based nature-inspired optimization algorithms which mimics the hunting strategy of antlions. ALO is successfully employed for solving many complicated optimization problems. However, it is reported in the literature that the original ALO ha...

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Vydáno v:Neural computing & applications Ročník 36; číslo 18; s. 10475 - 10499
Hlavní autoři: Durgut, Pinar G., Tozak, Mirac Bugse, Ayvaz, M. Tamer
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.06.2024
Springer Nature B.V
Témata:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Genetic algorithms
Image Processing and Computer Vision
Optimization
Original Article
Particle swarm optimization
Probability and Statistics in Computer Science
Random walk
Boundary shrinking procedure
Ant Lion Optimization (ALO)
SHALO
Shuffling
ISSN:0941-0643, 1433-3058
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Shrnutí:Ant Lion Optimization (ALO) method is one of the population-based nature-inspired optimization algorithms which mimics the hunting strategy of antlions. ALO is successfully employed for solving many complicated optimization problems. However, it is reported in the literature that the original ALO has some limitations such as the requirement of high number of iterations and possibility of trapping to local optimum solutions, especially for complex or large-scale problems. For this purpose, the SHuffled Ant Lion Optimization (SHALO) approach is proposed by conducting two improvements in the original ALO. Performance of the proposed SHALO approach is evaluated by solving some unconstrained and constrained problems for different conditions. Furthermore, the identified results are statistically compared with the ones obtained by using the original ALO, two improved ALOs which are the self-adaptive ALO (saALO) and the exponentially weighted ALO (EALO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) approaches. Identified results indicated that the proposed SHALO approach significantly improves the solution accuracy with a mean success rate of 76% in terms of finding the global or near-global optimum solutions and provides better results than ALO (22%), saALO (25%), EALO (14%), GA (28%), and PSO (49%) approaches for the same conditions.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-024-09566-5