Overview of NSGA-II for Optimizing Machining Process Parameters

Yusoff, Yusliza Mohd; Ngadiman, Mohd. Salihin; Zain, Azlan Mohd

doi:10.1016/j.proeng.2011.08.745

Cited by 349 publications

(127 citation statements)

References 10 publications

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“…NSGA-II combines the genetic algorithm, the concept of nondominance, and the crowded distance estimation together [17,18], and the concrete steps of the solving L 1 based on NSGA-II can be described as follows.…”

Section: Else 36mentioning

confidence: 99%

Adaptive Resource Allocation Scheme for Micromotion Feature Extraction Based on Track-Before-Detect

Chen¹,

Zhang

Luo

et al. 2018

Journal of Sensors

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The micromotion feature extraction method based on track-before-detect (TBD) can save the radar resource and improve the realtime performance of micromotion feature extraction by implementing target detecting, tracking, and micromotion feature extraction simultaneously. Usually, multitargets will exist in different areas, and the limited radar resources should be allocated for different areas to achieve the maximal performance of radar. For single-beam phased array radar, an adaptive resource allocation optimization model is established according to the processing steps of the micromotion feature extraction method based on TBD, and an adaptive resource allocation strategy is proposed. With the method, the radar efficiency can be significantly improved. The effectiveness of the proposed method is demonstrated by simulations.

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Section: Else 36mentioning

confidence: 99%

Adaptive Resource Allocation Scheme for Micromotion Feature Extraction Based on Track-Before-Detect

Chen¹,

Zhang

Luo

et al. 2018

Journal of Sensors

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“…2 shows the NSGA II procedure. Generally, NSGA II can be roughly detailed as following steps [72,73,74]:…”

Section: Non-dominated Sorting Genetic Algorithm (Nsga) IImentioning

confidence: 99%

A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

Karami

Zapata

2015

Neurocomputing

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Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based (host-centric) Internet infrastructure. NDN can overcome the fundamental limitations of the current Internet, in particular, Denial-of-Service (DoS) attacks. However, NDN can be subject to new type of DoS attacks namely Interest flooding attacks and content poisoning. These types of attacks exploit key architectural features of NDN. This paper presents a new intelligent hybrid algorithm for proactive detection of DoS attacks and adaptive mitigation reaction in NDN. In the detection phase, a combination of multiobjective evolutionary optimization algorithm with PSO in the context of the RBF neural network has been applied in order to improve the accuracy of DoS attack prediction. Performance of the proposed hybrid approach is also evaluated successfully by some benchmark problems. In the adaptive reaction phase, we introduced a framework for mitigating DoS attacks based on the misbehaving type of network nodes. The evaluation through simulations shows that the proposed intelligent hybrid algorithm (proactive detection and adaptive reaction) can quickly and effectively respond and mitigate DoS attacks in adverse conditions in terms of the applied performance criteria.

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“…Zain et al (2008) discussed on how GA system operates in order to optimize the surface roughness performance measure in milling process. Yusoff et al (2011) reviews the application of non dominated sorting genetic algorithm II (NSGA-II), classified as one of multi-objective GA techniques, for optimizing process parameters in various machining operations.…”

Section: Metaheuristic Techniques In Process Planning and Manufacturingmentioning

confidence: 99%

A metaheuristic for fast machining error compensation

Stryczek

2014

J Intell Manuf

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In case of complex parts machining or multidirectional machining in multi-part fixtures the error compensation in multi-dimensional decision space poses a difficult problem. The article focuses on the limitation of defective products by means of systematic increase of the remaining error budget due to correction of the setup data. A vectorial equation for machine tool space description is presented. The development of geometric dimensioning and tolerancing scheme to the levels connected with the setup data is proposed. The optimization algorithm used here is based on the paradigm particle swarm optimization (PSO), but it includes a few significant modifications inspired by the growth of the coral reef thus the name of the method-coral reefs inspired particle swarm optimization (CRIPSO). CRIPSO has been compared with three other popular metaheuristics: classic PSO, genetic algorithm, and cuckoo optimization algorithm. There is a practical example in this article.

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Overview of NSGA-II for Optimizing Machining Process Parameters

Cited by 349 publications

References 10 publications

Adaptive Resource Allocation Scheme for Micromotion Feature Extraction Based on Track-Before-Detect

Adaptive Resource Allocation Scheme for Micromotion Feature Extraction Based on Track-Before-Detect

A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

A metaheuristic for fast machining error compensation

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