A prescription of methodological guidelines for comparing bio-inspired optimization algorithms

LaTorre, Antonio; Molina, Daniel; Osaba, Eneko; Poyatos, Javier; Ser, Javier Del; Herrera, Francisco

doi:10.1016/j.swevo.2021.100973

Cited by 113 publications

(51 citation statements)

References 66 publications

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“…However, since the distribution of the objective function values are generally not normally distributed, we will use the medians instead of the means. This will be more consistent with the recommendations of using nonparametric tests when comparing metaheuristic methods [20]. In addition, we will use the absolute value of the differences between the medians since we are interested in finding if there is a significant difference between two algorithms.…”

Section: P-tests For Metaheuristicsmentioning

confidence: 81%

“…After presenting the summary statistics of each approach and comparing the different algorithms visually using scatter plots and Box-and-Whiskers plots, statistical tests were used to validate our conclusions. We only used nonparametric tests since, in general, the distribution of data is not normal [20]. In this subsection, the proposed P-test is compared with two nonparametric tests:…”

Section: Permutation Testsmentioning

confidence: 99%

“…Thus, if the assumptions of parametric tests do not hold or are not checked, nonparametric tests should be used. However, nonparametric tests are less powerful than parametric tests [20]. Nevertheless, the parametric tests' assumption often fails to hold when comparing the results of metaheuristic algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Permutation Tests for Metaheuristic Algorithms

Omran

Clerc²,

Ghaddar

et al. 2022

Mathematics

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Many metaheuristic approaches are inherently stochastic. In order to compare such methods, statistical tests are needed. However, choosing an appropriate test is not trivial, given that each test has some assumptions about the distribution of the underlying data that must be true before it can be used. Permutation tests (P-Tests) are statistical tests with minimal number of assumptions. These tests are simple, intuitive and nonparametric. In this paper, we argue researchers in the field of metaheuristics to adopt P-Tests to compare their algorithms. We define two statistic tests and then present an algorithm that uses them to compute the p-value. The proposed process is used to compare 5 metaheuristic algorithms on 10 benchmark functions. The resulting p-values are compared with the p-values of two widely used statistical tests. The results show that the proposed P-test is generally consistent with the classical tests, but more conservative in few cases.

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Section: P-tests For Metaheuristicsmentioning

confidence: 81%

Section: Permutation Testsmentioning

confidence: 99%

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Permutation Tests for Metaheuristic Algorithms

Omran

Clerc²,

Ghaddar

et al. 2022

Mathematics

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show abstract

“…Due to its huge computational amount, complex structures and many parameters are needed to control the bio-inspired algorithm in the application. LaTorre et al [ 21 ] proposed methodological guidelines to prepare a successful proposal through many surveys. Liu et al [ 22 ] introduced a parameter control approach utilized as feedback to control evolution processes adaptively.…”

Section: The Estimation Methods Of Pipe Embedding Parametersmentioning

confidence: 99%

Application of the Differential Evolutionary Algorithm to the Estimation of Pipe Embedding Parameters

Chen

Sheng

et al. 2022

Sensors

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The time-delay estimation (TDE) method is the primary method for predicting leakage locations in buried water distribution pipelines. The accuracy of TDE depends on the acoustic speed and attenuation of the leakage signal propagating along the pipeline. The analytical prediction model is the typical approach for obtaining the propagation speed and attenuation of leakage waves. However, the embedding parameters of the buried pipe in this model must be measured using soil tests, which are very difficult, costly, and time-consuming. These factors restrict the application of the TDE method in pinpointing pipeline leakage. A method for inverse identification of pipe embedding parameters using discrete wavenumbers obtained in field testing is presented in this paper, and the differential evolution algorithm is introduced as an optimization solution. A field experiment is conducted to validate the method, and the test wavenumbers are measured in a cast-iron pipeline. The estimated sensitive parameters in the analytical model using the method are soil elastic modulus, Poisson’s ratio, and pipe–soil contact coefficient, while the conventional soil test is used to measure the soil density due to the character of the optimization algorithm and the soil properties. The application effects show that the estimated parameters are close to those measured from a conventional soil test. The wave speed based on the estimated parameters was an excellent match for the on-site test in the engineering application. This work provides a less costly and more straightforward way to apply the TDE method for leak localization in buried pipelines.

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“…As shown in Table 11, ANA results are considered significant in 10 statistical tests of Student's t-test that are namely F3, F4, F5, F7, F9, F11, F12, F13, F16, and F18, 7 statistical tests of Welch's t-test that are namely F1, F2, F4, F5, F10, F15, and F17, and 3 statistical tests of Wilcoxon signed-rank test that are namely F2, F5, and F17 that is because the results are less than 0.05. According to this work [64], the statistical test results of the Wilcoxon signed-rank test should be relied on for ensuring the statistical significance of the ANA algorithm in comparison to FDO as the data is not normally distributed nor homoscedastic. Tables A6 and A7 in Appendix A contains the normality test using the Shapiro-Wilk test and the homoscedastic test using Levene's test of ANA and FDO on the standard benchmark functions.…”

Section: Statistical Testmentioning

confidence: 99%

ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

Rashid

Mirjalili

2021

Mathematics

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In this paper, a novel swarm intelligent algorithm is proposed called ant nesting algorithm (ANA). The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. Although the algorithm is inspired by the swarming behavior of ants, it does not have any algorithmic similarity with the ant colony optimization (ACO) algorithm. It is worth mentioning that ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change (e.g., step or velocity). ANA computes the rate of change differently as it uses previous, current solutions, fitness values during the optimization process to generate weights by utilizing the Pythagorean theorem. These weights drive the search agents during the exploration and exploitation phases. The ANA algorithm is benchmarked on 26 well-known test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), five modified versions of PSO, whale optimization algorithm (WOA), salp swarm algorithm (SSA), and fitness dependent optimizer (FDO). ANA outperformances these prominent metaheuristic algorithms on several test cases and provides quite competitive results. Finally, the algorithm is employed for optimizing two well-known real-world engineering problems: antenna array design and frequency-modulated synthesis. The results on the engineering case studies demonstrate the proposed algorithm’s capability in optimizing real-world problems.

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A prescription of methodological guidelines for comparing bio-inspired optimization algorithms

Cited by 113 publications

References 66 publications

Permutation Tests for Metaheuristic Algorithms

Permutation Tests for Metaheuristic Algorithms

Application of the Differential Evolutionary Algorithm to the Estimation of Pipe Embedding Parameters

ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

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