Binarization Methods for Shuffled Frog Leaping Algorithms That Solve Set Covering Problems

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

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“…Let X j new,er be the binarization value obtained by using the Elitist Roulette method [72]. Then, X j new,er is defined as follows.…”

Section: Binarizationmentioning

confidence: 99%

Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

et al. 2021

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“…However, the most widely applied metaheuristics for solving SCP are SIAs. Some examples are the artificial bee colony (ABC) algorithm [13,40,41], the ant colony optimization (ACO) algorithm [42][43][44], the firefly algorithm (FA) [45,46], the teaching-learning-based optimization (TLBO) algorithm [47,48], the electromagnetism-like (EM-like) algorithm [49,50], the shuffled frog leaping algorithm (SFLA) [51], the fruit fly optimization algorithm (FFOA) [52], the cuckoo search algorithm (CSA) [53,54], the cat swarm optimization (CSO) algorithm [55,56], the jumping particle swarm optimization (JPSO) method [57], the black hole optimization [54,58], and the monkey search algorithm [59].…”

Section: Related Workmentioning

confidence: 99%

Exploring Further Advantages in an Alternative Formulation for the Set Covering Problem

Lanza-Gutiérrez

Caballé

Crawford

et al. 2020

Mathematical Problems in Engineering

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The set covering problem (SCP) is an NP-complete optimization problem, fitting with many problems in engineering. The traditional SCP formulation does not directly address both solution unsatisfiability and set redundancy aspects. As a result, the solving methods have to control these aspects to avoid getting unfeasible and nonoptimized in cost solutions. In the last years, an alternative SCP formulation was proposed, directly covering both aspects. This alternative formulation received limited attention because managing both aspects is considered straightforward at this time. This paper questions whether there is some advantage in the alternative formulation, beyond addressing the two issues. Thus, two studies based on a metaheuristic approach are proposed to identify if there is any concept in the alternative formulation, which could be considered for enhancing a solving method considering the traditional SCP formulation. As a result, the authors conclude that there are concepts from the alternative formulation, which could be applied for guiding the search process and for designing heuristic feasibilit\y operators. Thus, such concepts could be recommended for designing state-of-the-art algorithms addressing the traditional SCP formulation.

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“…Numerous complex optimization problems can be solved by SFLA, such as multimodal problems, nonlinear and non-differential [67,[70][71][72]. This method can achieve better results through the distribution of water resources, the fuzzy or multivariable PID controller, and the repair of bridge-deck [73,74]. In this study, SFLA was used to optimize the parameters of the ANFIS to develop a hybrid model, namely ANFIS-SFLA.…”

Section: Shuffled Frog Leaping Algorithm (Sfla)mentioning

confidence: 99%

Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams

et al. 2019

Applied Sciences

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The principal purpose of this work is to develop three hybrid machine learning (ML) algorithms, namely ANFIS-RCSA, ANFIS-CA, and ANFIS-SFLA which are a combination of adaptive neuro-fuzzy inference system (ANFIS) with metaheuristic optimization techniques such as real-coded simulated annealing (RCSA), cultural algorithm (CA) and shuffled frog leaping algorithm (SFLA), respectively, to predict the critical buckling load of I-shaped cellular steel beams with circular openings. For this purpose, the existing database of buckling tests on I-shaped steel beams were extracted from the available literature and used to generate the datasets for modeling. Eight inputs, considered as independent variables, including the beam length, beam end-opening distance, opening diameter, inter-opening distance, section height, web thickness, flange width, and flange thickness, as well as one output of the critical buckling load of cellular steel beams considered as a dependent variable, were used in the datasets. Three quality assessment criteria, namely correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE) were employed for assessment of three developed hybrid ML models. The obtained results indicate that all three hybrid ML models have a strong ability to predict the buckling load of steel beams with circular openings, but ANFIS-SFLA (R = 0.960, RMSE = 0.040 and MAE = 0.017) exhibits the best effectiveness as compared with other hybrid models. In addition, sensitivity analysis was investigated and compared with linear statistical correlation between inputs and output to validate the importance of input variables in the models. The sensitivity results show that the most influenced variable affecting beam buckling capacity is the beam length, following by the flange width, the flange thickness, and the web thickness, respectively. This study shows that the hybrid ML techniques could help in establishing a robust numerical tool for beam buckling analysis. The proposed methodology is also promising to predict other types of failure, as well as other types of perforated beams.

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Binarization Methods for Shuffled Frog Leaping Algorithms That Solve Set Covering Problems

Cited by 14 publications

References 20 publications

Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

Exploring Further Advantages in an Alternative Formulation for the Set Covering Problem

Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams

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