Multi-species Cuckoo Search Algorithm for Global Optimization

Yang, Xin‐She; Deb, Suash; Mishra, Sudhanshu

doi:10.1007/s12559-018-9579-4

Cited by 40 publications

(13 citation statements)

References 47 publications

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“…In addition, many scholars began to study the theory of plant landscape ecology. Various researchers have conducted species cataloging and diversity comparative studies [ 23 , 24 ] on plant diversity in urban built-up areas and natural mountains within the city. The types of urban green space include park green space, green isolation zone, residential green space, ancillary green space, and street green space.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Application of Neural Network Model Based on Multispecies Evolutionary Genetic Algorithm to Planning and Design of Diverse Plant Landscape

Guo

Yang

2021

Computational Intelligence and Neuroscience

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In order to explore the feasibility of applying neural network model to landscape planning, based on the multispecies evolutionary genetic algorithm, a neural network model is proposed in this paper for the system design of diverse plant landscape planning. From the perspective of plant species diversity, this paper discusses landscape planning based on a neural network model. This landscape plan involves more than 180 plant species, mainly shrubs, fungi, and so on. The application of multispecies evolutionary genetic algorithm to landscape planning and design and the application of gene level coding and multispecies parallel evolution strategy to the evolutionary design of neural network have guiding significance for plant landscape planning and design. Compared with the traditional neural network modeling method and genetic algorithm, the proposed method has the advantages of wide network structure search space and simple algorithm calculation and design, independent of specific application background, and has strong application and promotion value. This method makes the model performance evaluation index more comprehensive and accurate and the model solution more reasonable. At the same time, combined with the specific status and corresponding changes of various plants in each season, this paper designs a targeted plan to rationally plan the specific spatial layout of the plant landscape and the combination of different types of plant landscapes, so as to effectively improve the quality of the landscape.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Application of Neural Network Model Based on Multispecies Evolutionary Genetic Algorithm to Planning and Design of Diverse Plant Landscape

Guo

Yang

2021

Computational Intelligence and Neuroscience

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“…The different life stages of cuckoo bird such as cuckoo egg laying and breeding are used as phases in the algorithm for optimization. Yang, X et al, [9] have developed a multi species cuckoo search method for effective nonlinear multi modal design case applications. In this scenario, different multi modal designs are considered as multiple cuckoo species interacting with each other and co-host for evolution and survival of fittest.…”

Section: Motivationmentioning

confidence: 99%

An Improved under Sampling Approaches for Concept Drift and Class Imbalance Data Streams using Improved Cuckoo Search Algorithm

Gullipalli¹

2021

TURCOMAT

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One of the biggest challenges in the recent times in the field of data stream learning is to mitigate the presence of concept drift. There are numerous challenges in overcoming the concept drift, such as changing class ratio, huge volume of data and real time processing for effective knowledge discovery. Evolutionary search techniques are one of the new paradigms to handle huge dimensionality and scalability of the data streams. One of the finest and least applied evolutionary search approaches is the cuckoo search technique for data streams. To solve both the concept drift and class imbalance issues simultaneously, in this paper we have proposed an approach using nature inspired evolutionary optimizing technique known as Cuckoo Feature and Instance Selection (CFIS) algorithm. The performance evaluation of the proposed approach is done on an exclusive experimental setup of 15 data streams formed and compared with two data stream approach. Moreover, a set of six evaluation criteria’s are considered for showing overall better performance of the proposed approach in the presence of concept drift and class imbalance.

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“…In the study in [21], the authors proposed a secure routing protocol known as trustpredicated routing (TPR), which utilizes the cuckoo search algorithm. The TPR scheme guarantees reliability, and the cuckoo search minimized the processing time and facilitated efficient routing.…”

Section: Literature Reviewmentioning

confidence: 99%

A Model to Improve the Effectiveness and Energy Consumption to Address the Routing Problem for Cognitive Radio Ad Hoc Networks by Utilizing an Optimized Cuckoo Search Algorithm

Moila

Velempini

2021

Energies

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A cognitive radio ad hoc network (CRAHN) is a mobile network that can communicate without any form of centralized infrastructure. The nodes can learn about the environment and make routing decisions. Furthermore, distributed computing, spectrum mobility, and the Internet of Things have created large data sets, which require more spectrum for data transmission. Unfortunately, the spectrum is a scarce resource that underutilized by licensed users, while unlicensed users are overcrowding the free spectrum. The CRAHNs technology has emerged as a promising solution to the underutilization of the spectrum. The focus of this study is to improve the effectiveness and energy consumption of routing in order to address the routing problem of CRAHNs through the implementation of the optimized cuckoo search algorithm. In CRAHNs, the node and spectrum mobility cause some frequent link breakages within the network, which degrades the performance of the routing protocols. This requires a routing solution to this routing problem. The proposed scheme was implemented in NS2 installed in Linux operating system, with a cognitive radio cognitive network (CRCN) patch. From the experimental results, we observed that the proposed OCS-AODV scheme outperformed CS-DSDV and ACO-AODV schemes. It obtained at least 3.87% packet delivery ratio and 2.56% and lower packets lost. The scheme enabled the mobile nodes to adjust accordingly to minimize energy consumption. If not busy, they switch to an idle state to save battery power.

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Multi-species Cuckoo Search Algorithm for Global Optimization

Cited by 40 publications

References 47 publications

[Retracted] Application of Neural Network Model Based on Multispecies Evolutionary Genetic Algorithm to Planning and Design of Diverse Plant Landscape

[Retracted] Application of Neural Network Model Based on Multispecies Evolutionary Genetic Algorithm to Planning and Design of Diverse Plant Landscape

An Improved under Sampling Approaches for Concept Drift and Class Imbalance Data Streams using Improved Cuckoo Search Algorithm

A Model to Improve the Effectiveness and Energy Consumption to Address the Routing Problem for Cognitive Radio Ad Hoc Networks by Utilizing an Optimized Cuckoo Search Algorithm

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