Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications

Darwish, Ashraf

doi:10.1016/j.fcij.2018.06.001

Cited by 218 publications

(142 citation statements)

References 24 publications

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“…However, we believe that further investigations are needed to assess the effectiveness of these techniques, and other algorithms should be considered. Some of these are ant colony optimizations, wolf search, cuckoo search, harmony search, firefly search, and bee search which have already provided interesting results in a variety of domains …”

Section: Summary Of Findingsmentioning

confidence: 99%

A systematic literature review of software effort prediction using machine learning methods

Ali

Gravino

2019

J Software Evolu Process

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Machine learning (ML) techniques have been widely investigated for building prediction models, able to estimate software development effort as well as to improve the accuracy of other estimation techniques. The objective of this paper is to systematically review the recent studies which used and discussed the software effort estimation models built using ML techniques. The performed literature review is based on the empirical studies published in the time period of January 1991 to December 2017, by employing widely used guidelines. The review has selected a total of 75 primary studies after the careful filtering of inclusion/exclusion and quality assessment criteria. The performed analysis reveals that artificial neural network (ANN) as ML model, NASA as dataset, and mean magnitude of relative error (MMRE) as accuracy measure are widely used in the selected studies. ANN and support vector machine (SVM) are the two techniques which have outperformed other ML techniques in more studies. Regression techniques are the mostly used among the non-ML techniques, which outperformed other ML techniques in about 19 studies. Moreover, SVM and regression techniques in combination are characterized by better predictions when compared with other ML and non-ML techniques.

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Section: Summary Of Findingsmentioning

confidence: 99%

A systematic literature review of software effort prediction using machine learning methods

Ali

Gravino

2019

J Software Evolu Process

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“…CSO optimization is an enduring approach of enhancement that can emulate the cat's conduct [21]. In the most recent years, CSO have been applied to locate the ideal answer for certain applications [22] [23]. The biological name of cat used in this section is felis catus.The mode looking for is applied during the resting time frame for cats, however they are observant; while the drawing attention mode is relating to the neighborhood search strategy to acquire the ideal arrangement elucidation of the present issue.…”

Section: Cat Swarm Optimization (Cso)mentioning

confidence: 99%

Bio-Inspired Based Distributed Monitoring Technique for Clustering and Optimization in Wireless Sensor Networks

Vinmathi*,

Josephine,

Jeyabalaraja

2020

IJRTE

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Security threats are vulnerable in recent times. Kaspersky intelligence report confirms that clustering and attacks makes crucial impact on the network as compared with other security attacks in wireless based environment .The current work focus on clustering of nodes in WSNs. Cat swarm optimization (CSO) algorithm is one of the bio-inspired algorithm and implemented for optimization of nodes to find a better route to transmit data packets in WSN. Also the work focuses on the presence of Distributed Monitoring Approach to evaluate the seek or hunt mode and tracing mode nodes for better improvement in clustering the network. The network recital parameters such as ratio of delivery of data packets, and transmission capacity usage are analyzed to detect network flow in WSN. Reviews have been performed with the analysis of network performance with some of the existing techniques.

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“…SSO is a bio-inspired algorithm [17], has been developed by Neshat.al [18] as a new swarm intelligence method [19] based on the behavior of swallow swarms. This algorithm is based on special characteristics of swallow that is revealed on some studies of the social behaviors taking into consideration swallows diversity, such as the very intelligent social life relation; the high-speed flying; and the hunting skills; and the migration of large groups.…”

Section: Swallow Swarm Optimization Algorithmmentioning

confidence: 99%

Solving the Quadratic Assignment Problem using the Swallow Swarm Optimization Problem

Bouzidi¹,

Bouzidi²,

Riffi³

2019

IJEAT

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In recent years, there is a growing interest in swarm intelligent algorithms inspired by the observation of the natural behavior of swarm to define a computational method, which may resolve the hardest combinatorial optimization problems. The Quadratic Assignment Problem is one of the well-known combinatorial problems, which simulate with the assignment problem in several domains such as the industrial domain. This paper proposes an adaptation of a recent algorithm called the swallow swarm optimization to solve the Quadratic Assignment Problem; this algorithm is characterized by a hierarchy of search who allow it to search in a totality of research space. The obtained results in solving some benchmark instances from QAPLIB are compared with those obtained from other know metaheuristics in other to evaluate the performance of the proposed adaptation.

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Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications

Cited by 218 publications

References 24 publications

A systematic literature review of software effort prediction using machine learning methods

A systematic literature review of software effort prediction using machine learning methods

Bio-Inspired Based Distributed Monitoring Technique for Clustering and Optimization in Wireless Sensor Networks

Solving the Quadratic Assignment Problem using the Swallow Swarm Optimization Problem

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