A survey on metaheuristics for optimization in food manufacturing industry

Wari, Ezra; Zhu, Weihang

doi:10.1016/j.asoc.2016.04.034

Cited by 69 publications

(33 citation statements)

References 97 publications

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“…This saves computation time and provides optimum results. In recent years, metaheuristic algorithms like ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), invasive weed optimization (IWO), artificial bee colony (ABC), and other evolutionary algorithms have applied in the food manufacturing industry (Wari & Zhu, ). The application of heuristic methods for food safety (Wang, Li, et al, ; Wang, Wang, et al, ) and quality (Hu, Yin, Ma, & Liu, ; Liu et al, ) analysis is growing steadily with significant improvement in overall classification accuracies.…”

Section: Methodsmentioning

confidence: 99%

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Bonah

Huang

Ren

et al. 2019

J Food Process Engineering

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Rapid detection of bacterial foodborne pathogens is crucial in reducing the incidence of diseases associated with food contaminated with pathogens and toxins. This article presents a classification model of support vector machine (SVM) together with a metaheuristic framework using genetic algorithm (GA), particle swarm optimization (PSO), and grid searching (GS) optimization algorithms for bacterial foodborne pathogen classification and differentiation. LDA and SVM showed classification accuracies of training (90.3% and 91%) and prediction (89.5% and 90.6%), respectively using the Matlab classification learner app. Optimization of the modeling parameters c and g for SVM were performed to increase efficiency and classification accuracy. Simulated results show classification accuracies of 100% (training set) and 98.95% (prediction set) for five selected bacterial pathogens acquired using electronic nose dataset and PSO‐SVM model. Electronic nose recognition system made up of 12 metal oxide semiconductor sensors produced different distinctive response signals for each bacterial and could differentiate Escherichia coli, Escherichia coli O157: H7, Listeria monocytogenes, Salmonella enteritidis, and Salmonella Typhimurium. PSO‐SVM algorithm can be efficiently used in bacterial discrimination at the species and strain level by electronic nose and has the good ability both in learning and in generalization. Practical Applications Existing microbial methods depend on traditional culture‐based methods which are time‐wise lengthy, require trained and qualified personnel, and are not suitable as point‐of‐use (POU) sensing devices. The application of electronic nose for the detection and classification of bacterial foodborne pathogens have shown great promise and proven to be effective with increasing sensitivity and selectivity as compared to traditional methods. The ability for E‐nose to discriminate between individual bacteria colonies at both species and strain level is of great public health importance since most bacteria have many strains. Virulence and pathogenicity are often associated with only a subset of these strains and it is essential for a method to be able to differentiate between pathogenic and nonpathogenic strains during a foodborne outbreak.

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Section: Methodsmentioning

confidence: 99%

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Bonah

Huang

Ren

et al. 2019

J Food Process Engineering

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“…In this layer, the input weight and deviation will be randomly generated and least square method will be deployed to determine output weight analytically [17], which differentiates this method from traditional methods. In this phase, learning happens followed by finding transformation matrix [93][94][95][96][97][98][99][100][101][102][103]. It is deployed to minimize the sum-of-squares error function.…”

Section: Machine Learning and Classification Methodsmentioning

confidence: 99%

Literature Review on Big Data Analytics Methods

Vanani¹,

Majidian²

2020

Social Media and Machine Learning

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Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed.

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“…The LAPO algorithm simulates the above-mentioned fading process and chooses individuals according to formula (5).…”

Section: Branch Fadingmentioning

confidence: 99%

“…Meta-heuristic algorithm is an algorithm designed to solve approximate large-scale difficult optimization problems without deep adaptation to each problem. In recent decades, in order to overcome the shortcomings of slow computation speed and low reliability of traditional numerical methods in solving engineering problems, researchers have proposed a large number of meta-heuristic algorithms [1][2][3], which are widely used to solve complex problems in industries and services, from planning to production management and even engineering [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Lightning Attachment Procedure Optimization Algorithm

Wang

Jiang

2019

Algorithms

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To overcome the shortcomings of the lightning attachment procedure optimization (LAPO) algorithm, such as premature convergence and slow convergence speed, an enhanced lightning attachment procedure optimization (ELAPO) algorithm was proposed in this paper. In the downward leader movement, the idea of differential evolution was introduced to speed up population convergence; in the upward leader movement, by superimposing vectors pointing to the average individual, the individual updating mode was modified to change the direction of individual evolution, avoid falling into local optimum, and carry out a more fine local information search; in the performance enhancement stage, opposition-based learning (OBL) was used to replace the worst individuals, improve the convergence rate of population, and increase the global exploration capability. Finally, 16 typical benchmark functions in CEC2005 are used to carry out simulation experiments with LAPO algorithm, four improved algorithms, and ELAPO. Experimental results showed that ELAPO obtained the better convergence velocity and optimization accuracy.

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A survey on metaheuristics for optimization in food manufacturing industry

Cited by 69 publications

References 97 publications

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Literature Review on Big Data Analytics Methods

An Enhanced Lightning Attachment Procedure Optimization Algorithm

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