Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

Tubishat, Mohammad; Idris, Norisma; Shuib, Liyana; Abushariah, Mohammad A. M.; Mirjalili, Seyedali

doi:10.1016/j.eswa.2019.113122

Cited by 269 publications

(131 citation statements)

References 55 publications

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“…Moreover, SSA is also an optimization algorithm proposed by Mirjalili et al [48] inspired by the behavior of salp chains. In recent years, the SSA was utilized to solve different optimization problems, such as feature selection [49,50], data classification [51], image segmentation [52], and others [53,54].…”

Section: Introductionmentioning

confidence: 99%

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Al-qaness

Ewees

Fan

et al. 2020

JCM

365

314

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In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination (R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

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Section: Introductionmentioning

confidence: 99%

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Al-qaness

Ewees

Fan

et al. 2020

JCM

365

314

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“…[25] hybridised Grey Wolf Optimizer (GWO) with WOA for the FS problem. [1,[26][27][28] improved Salp Swarm Algorithm (SSA) for the FS problem. [15] used Social Spider Algorithm (SSA) for the FS problem.…”

Section: Related Workmentioning

confidence: 99%

“…In the BOA algorithm, the fragrance is defined as a function of the stimulus intensity and can be determined by using equation (1) as follows:…”

Section: A the Original Butterfly Optimisation Algorithm (Boa)mentioning

confidence: 99%

“…Feature selection is a crucial step in many applications such as sentiment analysis, cancer detection, medical applications, image classification, intrusion detection, software fault prediction, text classification, spam classification, and many more. In these applications, the use of all features which are included in the dataset will result in performance degradation and increase the computational cost [1].…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, there are several studies that applied optimisation algorithms for feature selection problem. For example, [3,4] improved whale optimisation algorithm (WOA) and used it for feature selection, [5] improved particle swarm optimisation (PSO) and used it for feature selection, [6] used Grasshopper optimisation algorithm (GOA) for feature selection, [7] used Firefly algorithm (FFA) for feature selection, [8] used Differential Evolution (DE) for feature selection, [1] improved Salp Swarm Algorithm (SSA)and used it for feature selection, [9] used Genetic Algorithm (GA) for feature selection, [10] used Grey Wolf Optimization (GWO) for feature selection, [11] improved Gravitational Search algorithm (GSA) and used it for feature selection, [12] used fish swarm optimisation (FSO) for feature selection, [13] improved crow search algorithm (CSA) and used it for feature selection, [14] improved Dragonfly Algorithm (DA) and used it for feature selection, [15] improved social spider algorithm (SSA) and used it for feature selection, [1,16] improved Salp Swarm Algorithm (SSA) and used it for feature selection, [2] improved Harris hawks optimisation (HHO) and used it for feature selection.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Butterfly Optimization Algorithm for Feature Selection

et al. 2020

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Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimisation algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimisation algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimisation process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics. INDEX TERMS Butterfly optimisation algorithm, feature selection, local search algorithm based on mutation.

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An Intelligent Tunicate Swarm Algorithm Based MPPT Control Strategy for Multiple Configurations of PV Systems Under Partial Shading Conditions

Mansoor

Mirza

Feng³

et al. 2021

Advcd Theory and Sims

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Solar energy is the most techno-economically viable renewable source of energy and can be effectively converted into electrical power by Photovoltaic (PV) systems. As partial shading (PS) may reduce the harvested energy of PV systems, this paper proposes a novel tunicate swarm algorithm (TSA) based MPPT (maximum power point tracking) strategy to tackle the PS issue. More specifically, the simple and effective modeling of TSA with a search and skipping (SAS) scheme is utilized to minimize the tracking time and search area. The SAS scheme can discard the voltage range lacking global maximum power point (GMPP) and significantly reduce computation time. Consequently, power tracking, tracking time, and robustness can be greatly enhanced. The performance of the proposed TSA strategy is comprehensively analyzed against state-of-the-art techniques, including incremental conductance (InC), improved particle swarm optimization (IPSO), grey wolf optimization (GWO), and cuckoo search algorithm (CSA), through detailed case studies, which include standard array configurations, PS conditions, varying irradiance patterns, fast-changing temperature, and the field atmospheric data. TSA is further validated on a low-cost hardware setup, confirming its superior performance. The results provide insightful validation of the practicality of the proposed TSA strategy in the real-world applications.

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Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

Cited by 269 publications

References 55 publications

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Dynamic Butterfly Optimization Algorithm for Feature Selection

An Intelligent Tunicate Swarm Algorithm Based MPPT Control Strategy for Multiple Configurations of PV Systems Under Partial Shading Conditions

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