Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey

Ma, Haiping; Shen, Shigen; Yu, Mei; Yang, Zhile; Fei, Minrui; Zhou, Huiyu

doi:10.1016/j.swevo.2018.04.011

Cited by 170 publications

(49 citation statements)

References 269 publications

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“…Swarm intelligence optimization algorithms represents a rapidly emerging field, which is inspired by the collective intelligence of insect or animals organizing in groups, like in ant or bee colonies. The concept of the collective intelligence was originally introduced in [36][37][38] with the target to describe the self-organizing and intelligent behavior of ants. One of the most popular evolutionary algorithms is the Artificial Bee Colony algorithm (ABC) which was originally introduced in [39].…”

Section: Methodsmentioning

confidence: 99%

Prediction Model of Alcohol Intoxication from Facial Temperature Dynamics Based on K-Means Clustering Driven by Evolutionary Computing

et al. 2019

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Alcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identification and classification of alcohol-intoxicated people. We adopted a multiregional segmentation procedure to identify and classify symmetrical facial features, which reliably reflects the facial-temperature variations while subjects are drinking alcohol. Such a model can objectively track alcohol intoxication in the form of a facial temperature map. In our paper, we propose the segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony (ABC) evolutionary optimization with the goal of facial temperature features extraction from the IR (infrared radiation) images. This model allows for a definition of symmetric clusters, identifying facial temperature structures corresponding with intoxication. The ABC algorithm serves as an optimization process for an optimal cluster's distribution to the clustering method the best approximate individual areas linked with gradual alcohol intoxication. In our analysis, we analyzed a set of twenty volunteers, who had IR images taken to reflect the process of alcohol intoxication. The proposed method was represented by multiregional segmentation, allowing for classification of the individual spatial temperature areas into segmentation classes. The proposed method, besides single IR image modelling, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level.

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Section: Methodsmentioning

confidence: 99%

Prediction Model of Alcohol Intoxication from Facial Temperature Dynamics Based on K-Means Clustering Driven by Evolutionary Computing

et al. 2019

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“…In the current research, the feasibility of four different nature-inspired algorithms (i.e., PSO, ACO, DE, and GA) are investigated to tune ANFIS model as a predictive paradigm for simulating shallow foundation settlement. e selection of those optimizers is owing to their capacity in tuning the internal parameters of AI models and that was evidenced through several established research studies over the literature [43,44].…”

Section: Literature Review and Research Motivationmentioning

confidence: 99%

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Mohammed

Sharafati

Al‐Ansari

et al. 2020

Advances in Civil Engineering

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Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on the implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties. The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ratio of footing embedment (Df/B) are considered as predictive variables. Nonhomogeneity and inconsistency of employed dataset is a major concern during prediction modeling. Hence, two different modeling scenarios (i) preprocessed dataset (PP) and (ii) nonprocessed (initial) dataset (NP) were inspected. To assess the accuracy of the applied hybrid models and standalone one, multiple statistical metrics were computed and analyzed over the training and testing phases. Results indicated ANFIS-PSO model exhibited an accurate and reliable prediction data intelligent and had the highest predictability performance against all employed models. In addition, results demonstrated that data preprocessing is highly essential to be performed prior to building the predictive models. Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.

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“…Recently, NIAs have been applied to the fields of medical image classification, robot path planning, financial and industrial optimization, etc. through hybridization with various existing machine learning techniques [8].…”

Section: Nature Inspired Algorithmmentioning

confidence: 99%

Texture classification using convolutional neural network optimized with whale optimization algorithm

et al. 2019

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Texture classification is an active area of research in the field of pattern recognition. Convolutional neural networks (CNNs) have a remarkable capability of recognizing patterns and are one of the most efficient deep learning techniques. But, finding the optimal values of the different hyperparameters of the CNN is a major challenge. Nature-inspired algorithms (NIAs) are the meta-heuristic algorithms well-known for their optimizing capability. Whale optimization algorithm (WOA) is a recent nature-inspired algorithm (NIA) that is inspired by the hunting behaviour of the humpback whales. In this paper, we propose a novel deep learning technique for texture recognition using a CNN optimized through WOA. We apply WOA at the two different levels in the CNN: In the convolutional layer (for optimizing the values of the filters), and in the fully-connected layer (for optimizing the values of the weights and biases). For examining the performance of our technique, we apply it to the following three benchmark texture datasets: Kylberg v1.0, Brodatz, and Outex_TC_00012. Our model performs better than the most of the existing methods for the Kylberg and the Outex_TC_00012 datasets and gives competitive results for the Brodatz dataset. It is evident from the results that our model has the potential for application in the field of texture recognition.

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Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey

Cited by 170 publications

References 269 publications

Prediction Model of Alcohol Intoxication from Facial Temperature Dynamics Based on K-Means Clustering Driven by Evolutionary Computing

Prediction Model of Alcohol Intoxication from Facial Temperature Dynamics Based on K-Means Clustering Driven by Evolutionary Computing

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Texture classification using convolutional neural network optimized with whale optimization algorithm

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