Modeling beach realignment using a neuro-fuzzy network optimized by a novel backtracking search algorithm

Chatzipavlis, Antonios; Tsekouras, George E.; Trygonis, Vasilis; Velegrakis, Adonis F.; Tsimikas, John V.; Rigos, Anastasios; Hasiotis, Thomas; Salmas, Constantinos E.

doi:10.1007/s00521-018-3809-2

Cited by 21 publications

(14 citation statements)

References 48 publications

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“…In [19], Tsekouras et al, argue that the use of the historic population and the lack of a strategy to increase the population diversity in the mutation phase, might cause the standard BSA to have an inferior balance between exploitation and exploration, thus exhibiting poor convergence characteristics, such as slow or premature convergence. As a remedy they propose three modifications.…”

Section: B Backtracking Search Optimizationmentioning

confidence: 99%

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Feature Selection with a Backtracking Search Optimization Algorithm

Sikelis

Tsekouras

2022

ITM Web Conf.

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Feature selection carries significance in the outcome of any classification or regression task. Exercising evolutionary computation algorithms in feature selection has led to the construction of efficient discrete optimization algorithms. In this paper, a modified backtracking search algorithm is employed to perform wrapper-based feature selection, where two modifications of the standard backtracking search algorithm are adopted. The first one concentrates on utilizing a particle ranking operator regarding the current population. The second one focuses on removing the case of using a single particle on the mutation process. Then, the implementation of the above algorithm in feature selection is carried out in terms of two general frameworks, which originally were developed for the particle swarm optimization. The first framework is based on the binary and the second on the set-based particle swarm optimization. The experimental analysis shows that the above variants of the backtracking search algorithm perform equally well on the classification of several datasets.

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Section: B Backtracking Search Optimizationmentioning

confidence: 99%

“…According to authors the algorithm outperformed several popular evolutionary algorithms including PSO on a wide range of tests. In [19], Tsekouras et al, proposed a modified BSA algorithm (MBSA) and successfully used it to train a neuro-fuzzy network for modelling shoreline realignment.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection with a Backtracking Search Optimization Algorithm

Sikelis

Tsekouras

2022

ITM Web Conf.

Self Cite

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“…BSA-based neuro-fuzzy network. In [45], the neuro-fuzzy network is utilised in the standard BSA on a few input variables correlated to beach morphology and wave forcing to model the beach realignment.…”

Section: Chaos-based Bsamentioning

confidence: 99%

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Hassan

Rashid

2020

Applied Mathematics and Computation

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Backtracking search optimisation algorithm (BSA) is a commonly used meta-heuristic optimisation algorithm and was proposed by Civicioglu in 2013. When it was first used, it exhibited its strong potential for solving numerical optimisation problems. Additionally, the experiments conducted in previous studies demonstrated the successful performance of BSA and its non-sensitivity toward the several types of optimisation problems.This success of BSA motivated researchers to work on expanding it, e.g., developing its improved versions or employing it for different applications and problem domains. However, there is a lack of literature review on BSA; therefore, reviewing the aforementioned modifications and applications systematically will aid further development of the algorithm. This paper provides a systematic review and meta-analysis that emphasise on reviewing the related studies and recent developments on BSA. Hence, the objectives of this work are twofold: (i) First, two frameworks for depicting the main extensions and the uses of BSA are proposed. The first framework is a general framework to depict the main extensions of BSA, whereas the second is an operational framework to present the expansion procedures of BSA to guide the researchers who are working on improving it. (ii) Second, the experiments conducted in this study fairly compare the analytical performance of BSA with four other competitive algorithms: differential evolution (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly (FF) on 16 different hardness scores of the benchmark functions with different initial control parameters such as problem dimensions and search space. The experimental results indicate that BSA is statistically superior than the aforementioned algorithms in solving different cohorts of numerical optimisation problems such as problems with different levels of hardness score, problem dimensions, and search spaces. This study can act as a systematic and meta-analysis guide for the scholars who are working on improving BSA.In artificial intelligence (AI), besides the existing traditional symbolic and statistical methods [1], computational intelligence (CI) has emerged as a recent research area [2]. According to [3], CI deals with collections of nature-inspired algorithms and approaches that in turn have been used to deal with complex practical problems that cannot be feasibly or effectively solved by traditional methods. CI comprises three subfields: fuzzy logic, evolutionary computation, and neural network.The concept of swarm intelligence (SI) is a part of evolutionary computation. It deals with artificial and natural systems that comprise several individuals and possess the ability of self-organisation and decentralised control.This concept, which was primarily initiated by Gerardo Beni and Jing Wang in 1989 in the context of cellular robotic systems, is used in AI [4]. Typically, the SI system comprises a collection of agents. The agents interact with their own environment or with each other. SI syst...

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“…To address this, automated machine learning approaches (such as AutoML) have been proposed to help build hybrid prediction models (He et al 2021) without extensive knowledge of statistics and machine learning (Zöller and Huber 2021), while reducing human effort and potential bias (Hutter et al 2019). In addition, recent studies (Archetti and Candelieri 2019;Chatzipavlis et al 2018;Frazier 2018) have investigated the use of Bayesian Optimization (BO) to identify an optimal configuration of the hyperparameters of a machine learning algorithm within a limited number of trials, especially for long-term data.…”

Section: Introductionmentioning

confidence: 99%

“…These research studies highlight the importance of continuing the investigation of new methodologies that may offer useful scientific insights to policymakers. Based on recent literature (Archetti and Candelieri 2019;Chatzipavlis et al 2018;Frazier 2018), Bayesian optimization (BO) can identify an optimal configuration of the hyperparameters of a machine learning algorithm within a limited number of trials, especially for long-term data.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Benefits of Nature-Inspired Algorithms for the Parameterization of ANN in the Prediction of Water Demand

Zubaidi

Al-Bdairi

Ortega‐Martorell

et al. 2023

J. Water Resour. Plann. Manage.

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Accurate forecasting techniques for a stochastic pattern of water demand are essential for any city that faces high variability in climate factors and a shortage of water resources. This study was the first research to assess the impact of climatic factors on urban water demand in Iraq, which is one of the hottest countries in the world. We developed a novel forecasting methodology that includes data preprocessing and an artificial neural network (ANN) model, which we integrated with a recent nature-inspired metaheuristic algorithm [marine predators algorithm (MPA)]. The MPA-ANN algorithm was compared with four nature-inspired metaheuristic algorithms. Nine climatic factors were examined with different scenarios to simulate the monthly stochastic urban water demand over 11 years for Baghdad City, Iraq. The results revealed that (1) precipitation, solar radiation, and dew point temperature are the most relevant factors; (2) the ANN model becomes more accurate when it is used in combination with the MPA; and (3) this methodology can accurately forecast water demand considering the variability in climatic factors. These findings are of considerable significance to water utilities in planning, reviewing, and comparing the availability of freshwater resources and increasing water requests (i.e., adaptation variability of climatic factors).

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Modeling beach realignment using a neuro-fuzzy network optimized by a novel backtracking search algorithm

Cited by 21 publications

References 48 publications

Feature Selection with a Backtracking Search Optimization Algorithm

Feature Selection with a Backtracking Search Optimization Algorithm

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Assessing the Benefits of Nature-Inspired Algorithms for the Parameterization of ANN in the Prediction of Water Demand

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