A comprehensive meta-analysis of emerging swarm intelligent computing techniques and their research trend

Monga, Preeti; Sharma, Manik; Sharma, Shubham

doi:10.1016/j.jksuci.2021.11.016

Cited by 18 publications

(17 citation statements)

References 127 publications

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“…Hence, this optimizer can be categorized as a radiation-based algorithm. The most beneficial advantage of this algorithm is that it does not face problems with the convergence of parameters as long as the population size is appropriately increased [ 35 ]. Not only can MOEPCA solve continuous optimization problems, but it has also evolved to solve binary problems [ 36 ] and multi-objective problems [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

A Closer Look at Precision Hard Turning of AISI4340: Multi-Objective Optimization for Simultaneous Low Surface Roughness and High Productivity

et al. 2022

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This article reports an extended investigation into the precision hard turning of AISI 4340 alloy steel when machined by two different types of inserts: wiper nose and conventional round nose. It provides a closer look at previously published work and aims at determining the optimal process parameters for simultaneously minimizing surface roughness and maximizing productivity. In the mathematical models developed by the authors, surface roughness at different cutting speeds, depths of cut and feed rates is treated as the objective function. Three robust multi-objective techniques, (1) multi-objective genetic algorithm (MOGA), (2) multi-objective Pareto search algorithm (MOPSA) and (3) multi-objective emperor penguin colony algorithm (MOEPCA), were used to determine the optimal turning parameters when either the wiper or the conventional insert is used, and the results were experimentally validated. To investigate the practicality of the optimization algorithms, two turning scenarios were used. These were the machining of the combustion chamber of a gun barrel, first with an average roughness (Ra) of 0.4 µm and then with 0.8 µm, under conditions of high productivity. In terms of the simultaneous achievement of both high surface quality and productivity in precision hard turning of AISI 4340 alloy steel, this work illustrates that MOPSA provides the best optimal solution for the wiper insert case, and MOEPCA results are the best for the conventional insert. Furthermore, the results extracted from Pareto front plots show that the wiper insert is capable of successfully meeting both the requirements of Ra values of 0.4 µm and 0.8 µm and high productivity. However, the conventional insert could not meet the 0.4 µm Ra requirement; the recorded global minimum was Ra = 0.454 µm, which reveals the superiority of the wiper compared to the conventional insert.

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Section: Methodsmentioning

confidence: 99%

A Closer Look at Precision Hard Turning of AISI4340: Multi-Objective Optimization for Simultaneous Low Surface Roughness and High Productivity

et al. 2022

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“…where p (@) and p ($) are the probabilities of ∈ μ @ and ∈ μ $, respectively. Moreover, conditional entropy is calculated by Equation (12).…”

Section: Information Gain (Ig)mentioning

confidence: 99%

“…Last but not least, the computational time of the proposed approach is analyzed in this section. Computational time is another important factor, which determines how fast an intelligent 20 Monga et al 12 Agarwal and Dhingra 17 Dokeroglu et al 23 Li et al 29 Proposed method…”

Section: Computational Time Analysismentioning

confidence: 99%

“…Genetic algorithm‐based wrapper technique can be considered as the most popular one. However, other meta‐heuristic algorithms, particularly swarm intelligence techniques are widely applied to tackle the problem, as well 12 …”

Section: Introductionmentioning

confidence: 99%

“…However, other meta-heuristic algorithms, particularly swarm intelligence techniques are widely applied to tackle the problem, as well. 12 Meta-heuristic wrapper techniques can be broken down into two general groups, namely, single objective and multiobjective methods. There are numerous meta-heuristic wrapper approaches that try to select the best subset of features just with trusting the accuracy of classification.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective whale optimization algorithm‐based feature selection for intelligent systems

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Rafsanjani

Gupta

et al. 2022

Int J of Intelligent Sys

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With regard to large dimensions of contemporary data sets and restricted computational time of intelligent systems, reducing the dimensions of data sets is necessary. Feature selection is a practical way to remove a set of redundant, irrelevant, and noisy features. In this way, the speed of decision-making procedure will be increased while the accuracy of decisions will be retained. To this end, numerous attentions have been attracted to the topic and consequently, extensive range of methods has been proposed. Regarding the goals of the feature selection concept, the proposed algorithms in this field must be fast and accurate. Therefore, this paper proposes a light meanwhile accurate algorithm to fulfill the mentioned goals. The presented algorithm takes the speed advantage of Whale Optimization Algorithm (WOA) to propose a novel feature selection method for intelligent systems. Moreover, to reach the goal of accuracy, the proposed strategy considers three important fitness objectives, namely, the number of selected features, the accuracy of classification, and information gain. The proposed scheme considers an accurate multiobjective fitness function instead of manipulating the basic algorithm. The reason is that improving the basic algorithms, WOA in our case, may lead to loading

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Multi-objective meta-heuristics to optimize end-of-life laptop remanufacturing decisions under quality grading of returned parts

Anandh,

PrasannaVenkatesan,

Venkatadri

et al. 2024

Soft Comput

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A comprehensive meta-analysis of emerging swarm intelligent computing techniques and their research trend

Cited by 18 publications

References 127 publications

A Closer Look at Precision Hard Turning of AISI4340: Multi-Objective Optimization for Simultaneous Low Surface Roughness and High Productivity

A Closer Look at Precision Hard Turning of AISI4340: Multi-Objective Optimization for Simultaneous Low Surface Roughness and High Productivity

Multiobjective whale optimization algorithm‐based feature selection for intelligent systems

Multi-objective meta-heuristics to optimize end-of-life laptop remanufacturing decisions under quality grading of returned parts

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