A membrane-inspired bat algorithm to recognize faces in unconstrained scenarios

Alsalibi, Bisan; Venkat, Ibrahim; Al-Betar, Mohammed Azmi

doi:10.1016/j.engappai.2017.06.018

Cited by 25 publications

(14 citation statements)

References 66 publications

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“…Furthermore, there are many applications in big data and machine learning [41,42], such as Hamidzadeh et al [43], who proposed a novel method called chaotic bat algorithm for support vector data description (SVDD) (CBA-SVDD) to design effective descriptions of data. Alsalibi [44] proposed a novel membrane-inspired binary bat algorithm for facial feature selection. Furthermore, it outperforms recent state-of-the-art face recognition methods on three benchmark databases.…”

Section: (Iii) Applicationmentioning

confidence: 99%

A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization

et al. 2019

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A bat algorithm (BA) is a heuristic algorithm that operates by imitating the echolocation behavior of bats to perform global optimization. The BA is widely used in various optimization problems because of its excellent performance. In the bat algorithm, the global search capability is determined by the parameter loudness and frequency. However, experiments show that each operator in the algorithm can only improve the performance of the algorithm at a certain time. In this paper, a novel bat algorithm with multiple strategies coupling (mixBA) is proposed to solve this problem. To prove the effectiveness of the algorithm, we compared it with CEC2013 benchmarks test suits. Furthermore, the Wilcoxon and Friedman tests were conducted to distinguish the differences between it and other algorithms. The results prove that the proposed algorithm is significantly superior to others on the majority of benchmark functions.

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Section: (Iii) Applicationmentioning

confidence: 99%

A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization

et al. 2019

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“…Alsalibi et al study different bio-inspired approaches for the problem of face recognition in [3] and consider bridging membrane computing and evolutionary computing [182]. The problem of face recognition is also considered in [2] where a bat algorithm [177] under the framework of membrane computing (MC) is employed. Image registration is a research area which aims to find a transformation between two or more images under different conditions [188].…”

Section: Final Conclusionmentioning

confidence: 99%

“…Such GPU are especially well suited to address problems that can be expressed as data-parallel computations and, therefore, they are appropriate for the simulation of membrane computing devices. In fact, we can nowadays find many effective solutions to real-life problems inspired by membrane computing principles 2 (e.g., applications in computational economics [166], engineering [99], selfconfigurable robots [9] or fault diagnosis of power systems [120]).…”

Section: Introductionmentioning

confidence: 99%

Membrane computing and image processing: a short survey

2019

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Membrane computing is a well-known research area in computer science inspired by the organization and behavior of live cells and tissues. Their computational devices, called P systems, work in parallel and distributed mode and the information is encoded by multisets in a localized manner. All these features make P systems appropriate for dealing with digital images. In this paper, some of the open research lines in the area are presented, focusing on segmentation problems, skeletonization and algebraic-topological aspects of the images. An extensive bibliography about the application of membrane computing to the study of digital images is also provided.

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“…in a feasible time. Moreover, these models have been used to tackle real application problems [17], for example, knowledge representation [18], [19], numerical optimization [20], [21], image and signal processing [22]- [26], fault diagnosis [27]- [29], ecology and system biology [30]- [32].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Numerical P Systems With Application in Data Clustering Problems

et al. 2020

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This paper proposes an extension of numerical P (NP) systems, called stochastic numerical P (StNP) systems. In StNP systems, a novel stochastic production function-repartition protocol is developed to evolve the variables. Compared with usual production function-repartition protocol in NP systems, there are two differences in stochastic production function-repartition protocol: (i) some stochastic/randomized factors are introduced in production function and/or repartition protocol; (ii) data updating way is relaxed and can be flexibly used. An StNP system has a nested structure and contains some variables and a group of programs. The programs consist of stochastic production function-repartition protocols and/or usual production function-repartition protocols. Therefore, StNP systems are a class of distributed parallel computing models with stochastic and dynamic characteristics. Data clustering problems are used as an application to demonstrate the availability and effectiveness of StNP systems. Based on StNP systems, a novel partition clustering algorithm is presented. Experimental results demonstrate advantage of StNP systems for data clustering problems.

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A membrane-inspired bat algorithm to recognize faces in unconstrained scenarios

Cited by 25 publications

References 66 publications

A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization

A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization

Membrane computing and image processing: a short survey

Stochastic Numerical P Systems With Application in Data Clustering Problems

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