Parameter tuning for meta-heuristics

Joshi, Susheel Kumar; Bansal, Jagdish Chand

doi:10.1016/j.knosys.2019.105094

Cited by 50 publications

(12 citation statements)

References 40 publications

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“…Parameter tuning provides more flexibility and robustness in problem solving and it requires careful initialization. Indeed, the parameter tunning is highly related to the complexity of the problems but many researchers propose an optimal value for key parameters of the algorithms [36]. In this research, after using trial and error method for finding best value in algorithm setting, the researcher's proposition is used.…”

Section: Proposed Ddos Detection Methodsmentioning

confidence: 99%

A Hybrid Modified Grasshopper Optimization Algorithm and Genetic Algorithm to Detect and Prevent DDoS Attacks

Mohammadi

Babagoli

2021

IJE

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Cyber security has turned into a brutal and vicious environment due to the expansion of cyber-threats and cyberbullying. Distributed Denial of Service (DDoS) is a network menace that compromises victims' resources promptly. Considering the significant role of optimization algorithms in the highly accurate and adaptive detection of network attacks, the present study has proposed Hybrid Modified Grasshopper Optimization algorithm and Genetic Algorithm (HMGOGA) to detect and prevent DDoS attacks. HMGOGA overcomes conventional GOA drawbacks like low convergence speed and getting stuck in local optimum. In this paper, the proposed algorithm is used to detect DDoS attacks through the combined nonlinear regression (NR)-sigmoid model simulation. In order to serve this purpose, initially, the most important features in the network packages are extracted using the Random Forest (RF) method. By removing 55 irrelevant features out of a total of 77, the selected ones play a key role in the proposed model's performance. To affirm the efficiency, the high correlation of the selected features was measured with Decision Tree (DT). Subsequently, the HMGOGA is trained with benchmark cost functions and another proposed cost function that enabling it to detect malicious traffic properly. The usability of the proposed model is evaluated by comparing with two benchmark functions (Sphere and Ackley function). The experimental results have proved that HMGOGA based on NR-sigmoid outperforms other implemented models and conventional GOA methods with 99.90% and 99.34% train and test accuracy, respectively

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Section: Proposed Ddos Detection Methodsmentioning

confidence: 99%

A Hybrid Modified Grasshopper Optimization Algorithm and Genetic Algorithm to Detect and Prevent DDoS Attacks

Mohammadi

Babagoli

2021

IJE

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“…Particle Swarm Optimization (PSO) is a populationbased of meta-heuristic algorithm [16] widely used in bioinspired optimization [17]. PSO has been developed based on social behaviors of swarm flocking that moved towards different directions.…”

Section: B Particle Swarm Optimization Page Stylementioning

confidence: 99%

Particle Swarm Optimization in Machine Learning Prediction of Airbnb Hospitality Price Prediction

Masrom¹,

Baharun²,

Razi³

et al. 2022

IJETAE

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Particle Swarm Optimization is a metaheuristics algorithm widely used for optimization problems. This paper presents the research design and implementation of using Particle Swarm Optimization to automate the features selections in the machine learning models for Airbnb price prediction. Today, Airbnb is changing the business models of the hospitality industry globally. While a bigger impact has been given by the Airbnb community to the local economic development of each country, there has been very little effort that investigates on Airbnb pricing issue with machine learning techniques. Focusing on Airbnb Singapore, the main problem on the dataset is the low correlation of the independent variables to the hospitality price. Choosing the best combination of the independent variables is essential, which can be achieved through features selection optimization. Particle Swarm Optimization is useful to optimize the best variables combination for automating the features selection in machine learning models. By comparing the magnitude of change of the R squared values before and after the use of PSO feature selection, the result showed that the automated features selection has improved the results of all the machine learning algorithms mainly in the linear-based machine learning (Linear Regression, Lasso, Ridge). Keywords—Machine Learning, Automated Features Selection, Particle Swarm Optimization, Airbnb

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“…The algorithms of great deluge (GD) by Duek [10] are comparable to those of SA except that the primary purpose of GD algorithms is to substitute those of SA. The parameters used in GD algorithms are 531 less in amount, compared to those employed in SA algorithms, but like SA algorithms, GD algorithms also always accept improved solutions while the worsening ones are accepted according to certain likelihood.…”

Section: Great Delugementioning

confidence: 99%

“…In the last few years, diverse algorithms have been used in diverse domains that relate to expert and intelligent systems, and these include management systems of water resources, benchmark test functions, and modelling the distribution of signal strength in communication systems [10][11]. For the already addressed problems, it appears that their optimal solutions appeared to be known as priori and were presented in the pre-specified early domains.…”

Section: Introductionmentioning

confidence: 99%

Local search algorithms based on benchmark test functions problem

Bassel

Haglan

Mahmoud

2020

IJ-AI

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<p>Optimization process is normally implemented to solve several objectives in the form of single or multi-objectives modes. Some traditional optimization techniques are computationally burdensome which required exhaustive computational times. Thus, many studies have invented new optimization techniques to address the issues. To realize the effectiveness of the proposed techniques, implementation on several benchmark functions is crucial. In solving benchmark test functions, local search algorithms have been rigorously examined and employed to diverse tasks. This paper highlights different algorithms implemented to solve several problems. The capacity of local search algorithms in the resolution of engineering optimization problem including benchmark test functions is reviewed. The use of local search algorithms, mainly Simulated Annealing (SA) and Great Deluge (GD) according to solve different problems is presented. Improvements and hybridization of the local search and global search algorithms are also reviewed in this paper. Consequently, benchmark test functions are proposed to those involved in local search algorithm.</p>

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Parameter tuning for meta-heuristics

Cited by 50 publications

References 40 publications

A Hybrid Modified Grasshopper Optimization Algorithm and Genetic Algorithm to Detect and Prevent DDoS Attacks

A Hybrid Modified Grasshopper Optimization Algorithm and Genetic Algorithm to Detect and Prevent DDoS Attacks

Particle Swarm Optimization in Machine Learning Prediction of Airbnb Hospitality Price Prediction

Local search algorithms based on benchmark test functions problem

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