RETRACTED ARTICLE: A new binary salp swarm algorithm: development and application for optimization tasks

Rizk‐Allah, Rizk M.; Hassanien, Aboul Ella; Elhoseny, Mohamed; Gunasekaran, M.

doi:10.1007/s00521-018-3613-z

Cited by 112 publications

(42 citation statements)

References 45 publications

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“…The swarm is composed of relatively simple and unsophisticated agents that collectively exhibit intelligent behavior and characteristics. According to the survey of modern literature sources, swarm algorithms have many implementations for the cloud computing domain [28,29].…”

Section: Review Of Swarm Intelligence Metaheuristics and Its Applicatmentioning

confidence: 99%

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Strumberger

Tuba

Bačanin

et al. 2019

JSAN

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Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.

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Section: Review Of Swarm Intelligence Metaheuristics and Its Applicatmentioning

confidence: 99%

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Strumberger

Tuba

Bačanin

et al. 2019

JSAN

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“…Since processing time increases exponentially with increasing numbers of thresholds, traditional techniques will take considerable time to search for the optimal threshold. Consequently, meta-heuristic algorithms have been used as excellent stochastic meta-heuristic techniques to overcome the high processing time and accuracy problems [23]- [25]. Recently, many meta-heuristic algorithms have been proposed for image segmentation, such as genetic algorithm (GA) [26], particle swarm optimization (PSO) [27]- [29], ant-colony optimization algorithm [30], whale optimization algorithm (WOA) [31], honey bee mating (HBM) optimization [32], multi-verse optimizer [33], cuckoo search (CS) [34], symbiotic organisms search (SOS) [35], Harris hawks optimization algorithm (HHA) [36], and mothflame optimization algorithm (MFA) [31], flower pollination algorithm (FPA) [37], crow search algorithm [38], grey wolf optimizer [39], bee colony algorithm (BCA) [40], locust search algorithm (LSA) [41] and firefly optimization algorithm (FFA) [42].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy

et al. 2020

Self Cite

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Many countries are challenged by the medical resources required for COVID-19 detection which necessitates the development of a low-cost, rapid tool to detect and diagnose the virus effectively for a large numbers of tests. Although a chest X-Ray scan is a useful candidate tool the images generated by the scans must be analyzed accurately and quickly if large numbers of tests are to be processed. COVID-19 causes bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. In this work, we aim to extract rapidly from chest X-Ray images the similar small regions that may contain the identifying features of COVID-19. This paper therefore proposes a hybrid COVID-19 detection model based on an improved marine predators algorithm (IMPA) for X-Ray image segmentation. The ranking-based diversity reduction (RDR) strategy is used to enhance the performance of the IMPA to reach better solutions in fewer iterations. RDR works on finding the particles that couldn't find better solutions within a consecutive number of iterations, and then moving those particles towards the best solutions so far. The performance of IMPA has been validated on nine chest X-Ray images with threshold levels between 10 and 100 and compared with five state-of-art algorithms: equilibrium optimizer (EO), whale optimization algorithm (WOA), sine cosine algorithm (SCA), Harris-hawks algorithm (HHA), and salp swarm algorithms (SSA). The experimental results demonstrate that the proposed hybrid model outperforms all other algorithms for a range of metrics. In addition, the performance of our proposed model was convergent on all numbers of thresholds level in the Structured Similarity Index Metric (SSIM) and Universal Quality Index (UQI) metrics.

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“…Within the proposed technique, the SSA's position within the search space available is updated using the positions of the sine-cosine algorithm; thus, the best solutions are reserved based on sine or cosine. The authors of [35] implemented a new variant of the SSA based on Arctan transformation. This variant consists of two functional characteristics, namely mobility and multiplicity, which improves the exploitation and exploration ability of SSA.…”

Section: Introductionmentioning

confidence: 99%

A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems

Premkumar¹,

Kumar²,

Sowmya

et al. 2020

Automatika

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The photovoltaic (PV) systems must work at the maximum power point (MPP) to derive the highest possible power with the higher performance during a change in operating conditions. The primary objective is to implement a novel hybrid tracking algorithm to extract the maximum output power from the solar PV panel or array under partial shading conditions (PSCs). This hybrid MPP tracking algorithm is based on the salp swarm algorithm (SSA), which finds the initial global peak (GP) operating point and is followed by the perturb and observation (P&O) algorithm in the last stage to realize a faster convergence rate. Thus, the computational burden met by the conventional methods such as standalone P&O, hybrid grey-wolf-optimization (HGWO), and hybrid whale-optimization algorithm (HWOA) algorithm reported in the literature is overcome by the proposed hybrid SSA algorithm called HSSA. The P&O algorithm searches the MPP in the projected search space by the SSA algorithm. The proposed hybrid algorithm is simulated using MATLAB/Simulink simulation tool to validate the effectiveness of tracking the MPP. The hybrid SSA is compared with the standalone P&O, hybrid WOA, and hybrid GWO, and from the simulation results, it is proved that the hybrid tracking algorithm exhibits a high tracking performance.

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RETRACTED ARTICLE: A new binary salp swarm algorithm: development and application for optimization tasks

Cited by 112 publications

References 45 publications

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy

A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems

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