Nature-Inspired Chemical Reaction Optimisation Algorithms

Siddique, Nazmul; Adeli, Hojjat

doi:10.1007/s12559-017-9485-1

Cited by 58 publications

(28 citation statements)

References 54 publications

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“…In addition, different Dimensionality Reduction algorithms such as Large-margin Weakly Supervised Dimensionality Reduction (LWSDR) and Dimensionality Reduction with Subspace Structure Preservation (DRSSP), can be evaluated to assess their impact on the accuracy detection of training algorithms. Furthermore, more biologically-inspired machine learning algorithms such as Deep Learning [43], Genetic Algorithms and Particle Swarm Optimization algorithms [44] can also be utilized, as these have been proven to be highly efficient in features selection and classification. Moreover, the scope of our proposed approaches can be extended to the detection of other NDP-based attacks.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks

et al. 2017

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Background/Introduction RA flooding attack aims to exhaust all node resources, such as CPU and memory, attached to routers on the same link. A biologically-inspired machine learning based approach is proposed in this study to detect RA flooding attacks. Methods The proposed technique exploits information gain ratio (IGR) and Principal Component Analysis (PCA) for feature selection and a Support Vector Machine (SVM) based predictor model, which can also detect input traffic anomaly.

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Section: Discussionmentioning

confidence: 99%

A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks

et al. 2017

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“…Many of these research papers were summarized in several review papers [1][2][3][4][5][6]. Meanwhile, besides the fundamental methods, MCDM developments and novel applications dealing with construction problems have been constantly growing.…”

Section: Introductionmentioning

confidence: 99%

Sustainability in Construction Engineering

2018

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Abstract:The concept of sustainability has been expanding to all areas of economic activity, including construction engineering. Construction engineering is a complex discipline that involves designing, planning, constructing and managing infrastructures. In this Special Issue, 27 selected and peer-reviewed papers contribute to sustainable construction by offering technological, economic, social and environmental benefits through a variety of methodologies and tools, including fundamental decision-making models and methods as well as advanced multi-criteria decision-making (MCDM) methods and techniques. The papers are mainly concentrated in five areas: Sustainable architecture; construction/reconstruction technology and sustainable construction materials; construction economics, including investments, supply, contracting and costs calculation; infrastructure planning and assessment; project risk perception, analysis and assessment, with an emphasis on sustainability.

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“…Many applications involve optimization, and thus require sophisticated optimization algorithms to solve. Such applications can be very diverse, spanning many areas and disciplines from engineering designs and scheduling to data mining and machine learning [41,43,13,33,40]. One of the current trends is to use metaheuristic algorithms inspired by the successful characteristics in nature.…”

Section: Introductionmentioning

confidence: 99%

Multi-species Cuckoo Search Algorithm for Global Optimization

Yang¹,

Deb

Mishra

2018

Cogn Comput

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Background: Many optimization problems in science and engineering are highly nonlinear, and thus require sophisticated optimization techniques to solve. Traditional techniques such as gradient-based algorithms are mostly local search methods, and often struggle to cope with such challenging optimization problems. Recent trends tend to use nature-inspired optimization algorithms.Methods: This work extends the standard cuckoo search (CS) by using the successful features of the cuckoo-host co-evolution with multiple interacting species, and the proposed multi-species cuckoo search (MSCS) intends to mimic the multiple species of cuckoos that compete for the survival of the fittest, and they co-evolve with host species with solution vectors being encoded as position vectors. The proposed algorithm is then validated by 15 benchmark functions as well as five nonlinear, multimodal design case studies in practical applications.Results: Simulation results suggest that the proposed algorithm can be effective for finding optimal solutions and in this case all optimal solutions are achievable. The results for the test benchmarks are also compared with those obtained by other methods such as the standard cuckoo search and genetic algorithm, which demonstrated the efficiency of the present algorithm.Conclusions: Based on numerical experiments and case studies, we can conclude that the proposed algorithm can be more efficient in most cases, leading a potentially very effective tool for solving nonlinear optimization problems.

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Nature-Inspired Chemical Reaction Optimisation Algorithms

Cited by 58 publications

References 54 publications

A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks

A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks

Sustainability in Construction Engineering

Multi-species Cuckoo Search Algorithm for Global Optimization

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