2022
DOI: 10.1007/s10462-022-10328-9
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A survey on binary metaheuristic algorithms and their engineering applications

Abstract: This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, th… Show more

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Cited by 37 publications
(16 citation statements)
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References 292 publications
(485 reference statements)
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“…Hence, the binarization approach maps continuous search space to a binary using binarization techniques [ 124 ]. In the binarization approach, the continuous value of each dimension is mapped to either 1 or 0 [ 125 ]. Thus, in the binary problem defined in Eq.…”
Section: Different Approaches To Developing Woamentioning
confidence: 99%
“…Hence, the binarization approach maps continuous search space to a binary using binarization techniques [ 124 ]. In the binarization approach, the continuous value of each dimension is mapped to either 1 or 0 [ 125 ]. Thus, in the binary problem defined in Eq.…”
Section: Different Approaches To Developing Woamentioning
confidence: 99%
“…The "certain probability" here means that the new solution is accepted if it is better than the current solution, otherwise the new solution is accepted based on the Metropolis criterion. The probability of acceptance is given by equation (4).…”
Section: Combined With a Simulated Annealing Strategy(sas)mentioning
confidence: 99%
“…Some advanced threats may require deep inspection of network packets, which is more difficult in SDN [3]. With the development of metaheuristic algorithms [4] and their widespread application to solve combinatorial optimization problems as well as engineering design problems, many intelligent optimization algorithms have also been widely applied to the data optimization and dimensionality reduction problems of SDN intrusion detection systems. Meenaxi et al [5] combined software-defined networks with machine learning techniques to classify data traffic using three supervised learning models, SVM, KNN and NB.…”
Section: Introductionmentioning
confidence: 99%
“…Our specific focus is on developing a customized dataset intended to mimic real-world IoT scenarios impacted by different types of security attacks. In addition, a range of metaheuristic algorithms [ [10] , [11] , [12] , [13] ], such as PSO [ 14 ], GA [ 15 ], GWO [ 16 ], ALO [ 17 ], and AO [ 18 ], will be tested alongside CNNs [ [19] , [20] ]. The goal is to find and tweak the hyperparameters that help CNNs work at their best and increase the accuracy and effectiveness of intrusion detection in IoT networks.…”
Section: Introductionmentioning
confidence: 99%