Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems

Mirjalili, Seyedali; Gandomi, Amir H.; Mirjalili, Seyedeh Zahra; Saremi, Shahrzad; Faris, Hossam; Mirjalili, Seyed Mohammad

doi:10.1016/j.advengsoft.2017.07.002

Cited by 3,565 publications

(1,517 citation statements)

References 78 publications

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“…The FPA was employed in various optimization applications, for example to estimate solar PV parameter [39,40], solving sudoku puzzles [41], feature selection [42], antenna design [43], and other applications [44][45][46][47]. 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

364

310

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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

364

310

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“…The novelty of the out-of-equilibrium nature of the flocking transition -the system is out of equilibrium since agents constantly consume energy to propel themselves -piqued the minds of physicists and other theorists, which in turn helped give rise to the field of active matter [26][27][28], a field perhaps more vibrant than the field of synchronization. In a final mirroring of the sync story, the bio-inspired community has also mimicked the minimalism of Vicsek's and other models of swarming to design novel algorithms for optimization [29][30][31][32][33][34] and robotic swarms [33,[35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

A review of swarmalators and their potential in bio-inspired computing

O’Keeffe

Bettstetter

2019

Micro- And Nanotechnology Sensors, Systems, and Applications XI

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From fireflies to heart cells, many systems in Nature show the remarkable ability to spontaneously fall into synchrony. By imitating Nature's success at self-synchronizing, scientists have designed costeffective methods to achieve synchrony in the lab, with applications ranging from wireless sensor networks to radio transmission. A similar story has occurred in the study of swarms, where inspiration from the behavior flocks of birds and schools of fish has led to 'low-footprint' algorithms for multi-robot systems. Here, we continue this 'bio-inspired' tradition, by speculating on the technological benefit of fusing swarming with synchronization. The subject of recent theoretical work, minimal models of so-called 'swarmalator' systems exhibit rich spatiotemporal patterns, hinting at utility in 'bottom-up' robotic swarms. We review the theoretical work on swarmalators, identify possible realizations in Nature, and discuss their potential applications in technology.

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“…As an a posteriori multi-objective optimization, MOSSA [54] is similar to some swarm multi-objective optimization algorithm such as MOPSO [31], MOACO [33] and MOGO [35]. By simulating the biological behavior of ecological communities, the optimal solution is achieved.…”

Section: Multi-objective Salp Swarm Algorithm (Mossa)mentioning

confidence: 99%

A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting

2018

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Effective and reliable load forecasting is an important basis for power system planning and operation decisions. Its forecasting accuracy directly affects the safety and economy of the operation of the power system. However, attaining the desired point forecasting accuracy has been regarded as a challenge because of the intrinsic complexity and instability of the power load. Considering the difficulties of accurate point forecasting, interval prediction is able to tolerate increased uncertainty and provide more information for practical operation decisions. In this study, a novel hybrid system for short-term load forecasting (STLF) is proposed by integrating a data preprocessing module, a multi-objective optimization module, and an interval prediction module. In this system, the training process is performed by maximizing the coverage probability and by minimizing the forecasting interval width at the same time. To verify the performance of the proposed hybrid system, half-hourly load data are set as illustrative cases and two experiments are carried out in four states with four quarters in Australia. The simulation results verified the superiority of the proposed technique and the effects of the submodules were analyzed by comparing the outcomes with those of benchmark models. Furthermore, it is proved that the proposed hybrid system is valuable in improving power grid management.Energies 2018, 11, 1561 2 of 30 time. Among these categories, short-term load forecasting (STLF) is an essential tool for the planning and operation [1,2] of energy systems and it has thus been a major area of research during the past few decades.According to existing research, concern mostly focuses on the point forecasting of STLF. Additionally, the relative algorithms can be mainly classified into three major categories: traditional statistical techniques, computational intelligent methods, and multimodule hybrid models [3].In the early stages of research, traditional statistical techniques were extensively employed for point forecasting of STLF, such as linear regression methods [4,5], exponential smoothing [6], Kalman filters [7], and other linear time-series models. In general, most of the traditional statistical approaches have been involved in linear analysis and have mainly considered linear factors in time series. However, the short-term load series are a mixture of multiple components which include linear and non-linear factors. Therefore, the traditional statistical approaches encounter difficulties when dealing with the STLF, and the forecasting accuracy is often unsatisfactory. With the development of machine learning and artificial intelligence, an increased number of non-linear computational intelligent methods have been applied to STLF, such as neural network models (NN) [8,9], expert systems [10] and support vector machines (SVM) [11,12]. These approaches have been proved to have advantages in dealing with the non-linear problems of STLF compared to traditional statistical methods, thereby eliciting improved performanc...

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Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems

Cited by 3,565 publications

References 78 publications

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

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

A review of swarmalators and their potential in bio-inspired computing

A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting

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