2021
DOI: 10.3390/math9070781
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Mexican Axolotl Optimization: A Novel Bioinspired Heuristic

Abstract: When facing certain problems in science, engineering or technology, it is not enough to find a solution, but it is essential to seek and find the best possible solution through optimization. In many cases the exact optimization procedures are not applicable due to the great computational complexity of the problems. As an alternative to exact optimization, there are approximate optimization algorithms, whose purpose is to reduce computational complexity by pruning some areas of the problem search space. To achi… Show more

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Cited by 32 publications
(14 citation statements)
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“…In all the carried out tests, the number of iterations was set at 200, this being a lower value than that commonly used in the literature (500 or 1000) [16][17][18]. Therefore, the JSOA algorithm is more efficient in finding optimal (near-to optimal) solutions with a smaller number of iterations.…”
Section: Resultsmentioning
confidence: 99%
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“…In all the carried out tests, the number of iterations was set at 200, this being a lower value than that commonly used in the literature (500 or 1000) [16][17][18]. Therefore, the JSOA algorithm is more efficient in finding optimal (near-to optimal) solutions with a smaller number of iterations.…”
Section: Resultsmentioning
confidence: 99%
“…The shapes of the testbench functions considered in this study are shown in appendix A. The JSOA algorithm was compared with ten state-of-the-art bioinspired algorithms taken from the literature; these are Coot Bird Algorithm (COOT) [16], Chaos Game Optimization (CGO) [42], Mexican Axolotl Optimization (MAO) [17], Golden Eagle Optimizer (GEO) [18], Archimedes Optimization algorithm (AOA) [21], Arithmetic Optimization Algorithm (ArOA) [22], Gradient-based Optimizer (GBO) [23], Hunger Game Search (HGS) [24], Henry Gas Solubility Optimization (HGSO) [25] and, Harris Hawks Optimization (HHO) [26]. For each testbench function, the eleven algorithms were run 30 times, the size of the population (search agents) and the number of iterations were set to 30 and 200, respectively.…”
Section: Methodsmentioning
confidence: 99%
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