Sperm whale algorithm: An effective metaheuristic algorithm for production optimization problems

Ebrahimi, A.; Khamehchi, Ehsan

doi:10.1016/j.jngse.2016.01.001

Cited by 72 publications

(31 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Sperm whales, for the most part, go in gatherings of 6-9 and the males and females live in a similar gathering. The Sperm Whale Algorithm [6] is a way for the sperm whale to live in nature. A new effective multitarget is obtained by mathematical optimization algorithm.…”

Section: Sperm Whale Algorithm (Swa)mentioning

confidence: 99%

An Optimized Artificial Neural Network Approach Based on Sperm Whale Optimization Algorithm for Predicting Fertility Quality

El-Shafeiy¹,

El-Desouky²,

Elghamrawy³

2024

SIC

View full text Add to dashboard Cite

This research introduces a new approach for predicting reproductive health by using the Sperm Whale Optimization algorithm (SWA) with Artificial Neural Networks (ANN-SWA). SWA is an emerging method with a powerful potential in tackling optimization difficulties based on its adaptability in searching mechanisms. ANN-SWA consists of four phases. The first phase is conditioned by the fertility disease which is a complex and multifactorial condition with increasing incidence. The fertility data is trained (90 cases) and the approach is then used to test findings in the test set (10 cases). In the second phase, the multilayer perceptron (MLP) is used to determine the maximum fitness function by getting the global minimum and hence, it revokes the ANN trapped in local. In the third phase, it optimizes and controls the parameters (weights and biases) to ensure rapid convergence with accuracy. In the fourth phase, ANN-SWA is used to predict the fertility quality and determine the accuracy. The results are verified by comparing them with optimization and classification algorithms. The quantitative and qualitative outcomes show that the proposed approach is able to outperform the current algorithms on the fertility dataset in the convergence rate of classification. The results demonstrate that an artificial neural network based on SWA achieved more than 99.96% of the accuracy of the approach.

show abstract

Section: Sperm Whale Algorithm (Swa)mentioning

confidence: 99%

An Optimized Artificial Neural Network Approach Based on Sperm Whale Optimization Algorithm for Predicting Fertility Quality

El-Shafeiy¹,

El-Desouky²,

Elghamrawy³

2024

SIC

View full text Add to dashboard Cite

show abstract

“…The search used by the SWA mimics the hunting behavior of sperm whales, which live alone or in small groups at the bottom of the sea and must come to the surface to hunt and breathe [43]. In each iteration, the population of the SWA is split into smaller search groups consisting in uniformly distributed better and worse adapted members ('sperm whales').…”

Section: The Sperm-whale Algorithmmentioning

confidence: 99%

Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms

et al. 2019

View full text Add to dashboard Cite

Energy losses and bus voltage levels are key parameters in the operation of electricity distribution networks (EDN), in traditional operating conditions or in modern microgrids with renewable and distributed generation sources. Smart grids are set to bring hardware and software tools to improve the operation of electrical networks, using state-of the art demand management at home or system level and advanced network reconfiguration tools. However, for economic reasons, many network operators will still have to resort to low-cost management solutions, such as bus reactive power compensation using optimally placed capacitor banks. This paper approaches the problem of power and energy loss minimization by optimal allocation of capacitor banks (CB) in medium voltage (MV) EDN buses. A comparison is made between five metaheuristic algorithms used for this purpose: the well-established Genetic Algorithm (GA); Particle Swarm Optimization (PSO); and three newer metaheuristics, the Bat Optimization Algorithm (BOA), the Whale Optimization Algorithm (WOA) and the Sperm-Whale Algorithm (SWA). The algorithms are tested on the IEEE 33-bus system and on a real 215-bus EDN from Romania. The newest SWA algorithm gives the best results, for both test systems.

show abstract

“…Deriving from the life style of sperm whales, a new efficient multi-objective optimization algorithm called sperm whale algorithm (SWA) [25] is mathematically modeled. In SWA, each answer denotes a sperm whale.…”

Section: Sperm Whale Algorithmmentioning

confidence: 99%

“…T max t−i , i = 1, · · · , 7 represents the t − ith day's maximum temperature C 8 , · · · , C 14 T avg t−i , i = 1, · · · 7 represents the t − ith day's average temperature C 15 , · · · , C 21 T min t−i , i = 1, · · · 7 represents the t − ith day's minimum temperature C 22 R t represents the tth day's precipitation C 23 W t represents the wind speed on day t C 24 H t represents the humidity on day t C 25 Cld t represents the cloud situation on day t C 26 M t represents the month in which day t is located C 27 Sea t represents the season in which day t is located C 28 Hol t represent whether day t is holiday, 0 is holiday, 1 is not holiday. C 29 Wk t represent whether day t is weekend, 0 is weekend, 1 is not weekend.…”

Section: Forecasting System With W-lssvm-swa and Dwt-irmentioning

confidence: 99%

“…The sperm whale algorithm (SWA), first proposed by Ebrahimi and Khamehchi in 2016, is a brand new swarm intelligent optimization algorithm, which is mainly to simulate the whale's life style [25]. One of the great advantages of SWA over others is that it simultaneously uses the best and the worst answers to obtain the global optimal values.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection

Liu

2017

Sustainability

View full text Add to dashboard Cite

Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR) are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting. regression analysis was introduced to forecast short-term power load. Zhao et al. [6] presented an improved grey model optimized by a new optimization algorithm Ant Lion Optimizer for annual power load forecasting and the rolling mechanism was introduced to improve the forecasting accuracy. Coelho et al. [7] used a novel hybrid evolutionary fuzzy model with parameter optimization for load forecasting in a MG scenario. As can be seen from the above references, this kind of forecasting method has a mature theoretical basis, and its calculation process is simpler and easier to understand; however, this kind of method is unable to deal with non-linear forecasting problem due to theoretical limitation, its application object is often relatively simple, and the prediction accuracy usually fails to meet the expectations.With the development of modern computer technology and the continuous improvement of mathematical theory, intelligent forecasting methods are favored by many scholars for deep research. Artificial neural network (ANN) and support vector machine (SVM) are usually attractive to many scholars for solving short-term power load forecasting problems. As shown in paper [8], an artificial neural network (ANN) model with a back-propagation algorithm was presented for short-term load forecasting in micro-grid power systems. In paper [9], several univariate approaches based on neural networks were proposed and compared, which included multi-layer perceptron, radial basis function neural network, generalized regression neural network, fuzzy counter propagation neural networks, and self-organizing maps. Rana et al. [10] introduced an advanced wavelet neural networks for very short-term load forecasting, and the complex electricity load data was decomposed into different frequencies by the proposed method to separately realize the load forecasting. In paper [11], an effective short-term load forecasting model based on the generalized regression neural network with decreasing step fru...

show abstract

Sperm whale algorithm: An effective metaheuristic algorithm for production optimization problems

Cited by 72 publications

References 32 publications

An Optimized Artificial Neural Network Approach Based on Sperm Whale Optimization Algorithm for Predicting Fertility Quality

An Optimized Artificial Neural Network Approach Based on Sperm Whale Optimization Algorithm for Predicting Fertility Quality

Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms

The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection

Contact Info

Product

Resources

About