Asynchronous accelerating multi-leader salp chains for feature selection

Aljarah, Ibrahim; Mafarja, Majdi; Heidari, Ali Asghar; Faris, Hossam; Zhang, Yong; Mirjalili, Seyedali

doi:10.1016/j.asoc.2018.07.040

Cited by 184 publications

(38 citation statements)

References 63 publications

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“…Additionally, in [20], Aljarah et al proposed a feature selection approach based on asynchronous accelerating multi-leader salp chain with KNN and number of features fitness to avoid trapping in local solution when applied to high-dimensional datasets. The proposed approach improved the BSSA via adding 3 different asynchronous updating rules and a novel leadership structure.…”

Section: A Feature Selection/fusion Methodsmentioning

confidence: 99%

Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

2020

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The emerging technology of Structural Health Monitoring (SHM) paved the way for spotting and continuous tracking of structural damage. One of the major defects in historical structures is cracking, which represents an indicator of potential structural deterioration according to its severity. This paper presents a novel crack severity recognition system using a hybrid filter-wrapper with multi-objective optimization feature selection method. The proposed approach comprises two main components, namely, (1) feature extraction based on hand-crafted feature engineering and CNN-based deep feature learning and (2) feature selection using hybrid filter-wrapper with a multi-objective improved salp swarm optimization. The proposed approach is trained and validated by utilizing 10 representative UCI datasets and 4 datasets of crack images. The obtained experimental results show that the proposed system enhances the performance of crack severity recognition with ≈ 37% and ≈ 31% increase in recognition average accuracy and F-measure, respectively. Also, a reduction rate of ≈ 67% is achieved in the extracted feature set with all the tested datasets compared to the conventional classification approaches using the whole set of features. Moreover, the proposed approach outperforms other approaches with classical feature selection methods in terms of feature reduction rate and computational time. It is noticed that using VGG16 learned features outperforms using the fused hand-crafted features by 17.7%, 15.9%, and 23.5% for fine, moderate, and severe crack recognition, respectively. The significance of this paper is to investigate and highlight the impact of applying multi-feature dimensionality reduction through adopting hybrid filter-wrapper with multi-objective optimization methods for feature selection considering the case study of crack severity recognition for SHM.

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Section: A Feature Selection/fusion Methodsmentioning

confidence: 99%

Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

2020

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“…The population will be evolved iteratively by replacing the current population with newly generated ones using some often-stochastic operators [25], [26]. The optimization process is proceeded until satisfying a stopping criterion (e.g., the maximum number of iterations [27], [28]). With inspiration, population-based algorithms are categorized into: 1) Evolutionary-based group mimics biological evolutionary behaviors such as recombination, mutation, and selection; 2) Physics-based group is inspired by the physical laws; 3) Human-based group mimics certain human behaviors; 4) Swarm-based group mimics the social behaviors, e.g., decentralized and self-organized systems of organisms living in swarms, flocks, or herds.…”

Section: B Population-based Meta-heuristic Algorithmsmentioning

confidence: 99%

Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

et al. 2020

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The works presented in this paper addresses the robust population-based global optimization that is influenced by the simplicity and efficiency principles introduced in two new generation optimization algorithms. Galactic Swarm Optimization is inspired by the motion of stars, galaxies, and superclusters of galaxies under the influence of gravity. It acts well as a global controller of the whole optimization process by employing multiple flexible cycles of exploration and exploitation phases to find new, better solutions. However, the optimization process still suffers poverty in the exploitation phase, which is improved in this work by its hybridization with our evolution version of the Whale Optimization Algorithm. Concretely, the exploitation phase of Galactic Swarm Optimization is replaced by our Evolution Whale Optimization Algorithm to avoid early convergence. The Whale Optimization Algorithm mimics the unusual social behaviors and the hunting activities of humpback whales. However, it is not optimized for global optimization when the number of dimensions is increased. Hence its is evolved in our works by Levy-Flight trajectory for faster local search with adaptive step lengths and two-point crossover operator to reduce bias in the offspring creation procedure. The achieved results through extensive and careful experiments showed that our hybridization and evolution enhancements bring outstanding performance in terms of accuracy, convergence speed, and stability.

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“…The SSA has demonstrated its performance in several fields; for example, the SSA is used to determine the suitable parameters of the solar cell [53,54] and parameters of PEM fuel cells [23]. In addition, SSA is applied to improve classification through selecting relevant features [24,25,[55][56][57]. In [58], the authors presented a method based on SSA for practical considerations in radial distribution systems that determine optimal conductor and hosting capacity.…”

Section: Related Workmentioning

confidence: 99%

Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

Alresheedi

Elaziz

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOS-SASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the

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Asynchronous accelerating multi-leader salp chains for feature selection

Cited by 184 publications

References 63 publications

Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

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