Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications

In this paper, a novel hybrid metaheuristic optimization algorithm which is based on Particle Swarm Optimization (PSO) and recently developed Spotted Hyena Optimizer (SHO) named as Hybrid Particle Swarm and Spotted Hyena Optimizer (HPSSHO) is presented. The main concept of this algorithm is to improve the hunting strategy of Spotted Hyena Optimizer using particle swarm algorithm. The proposed algorithm is compared with four metaheuristic algorithms (i.e., SHO, PSO, DE, and GA) and benchmarked it on thirteen well-known benchmark test functions which include unimodal and multimodal. The convergence analysis of the proposed as well as other metaheuristics has also been analyzed and compared. The algorithm is tested on 25-bar real-life constraint engineering design problem to demonstrate its applicability. The experimental results reveal that the proposed algorithm performs better than other metaheuristic algorithms.

Spotted Hyena Optimizer (SHO) is a population-based metaheuristic algorithm inspired by the spotted hyenas’ social behavior, and it has been developed to solve global optimization problems. SHO has shown superior performance over its competitive metaheuristic algorithms in solving benchmark function optimization and engineering design problems. However, it suffers from getting stuck in local optima due to its lack of exploration while solving multi-modal optimization problems. This article proposes an improved SHO, quantum SHO (QSHO), inspired by quantum computing. The QSHO implements a quantum computing mechanism to promote its exploration ability. The novel method is tested on well-known IEEE CEC2013 and IEEE CEC2017 benchmark suits with 30 and 50 dimensions and four real-world engineering optimization problems. The results of QSHO are compared with that of Classical SHO, improved SHO (ISHO), Modified SHO (MSHO), Oppositional SHO with mutation operator (OBL-MO-SHO), SHO with space transformation search (STS-SHO), Quantum Salp Swarm Algorithm (QSSA), and Chimp Optimization Algorithm (ChOA). The results are analyzed using the Wilcoxon Signed Rank Test (WSRT) and Friedman Test. The empirical results show that QSHO statistically outperforms other compared algorithms for benchmark problem suits with 30 and 50 dimensions. According to Friedman Test statistics, the QSHO algorithm ranked first and second in solving CEC2013 30D and 50D, respectively, whereas it ranked first in both solving CEC2017 30D and 50D. In addition, we have assessed the QSHO in four real-world engineering optimization problems, and the QSHO statistically outperforms the competitive algorithms.

Using spotted hyena optimizer for training feedforward neural networks

This paper presents a recently developed metaheuristic optimization algorithm named as Spotted Hyena Optimizer (SHO) which is inspired by the social behaviors of spotted hyenas. The three basic steps of SHO are searching for prey, encircling, and attacking prey which are mathematically modeled and discussed. The main concept of this work is to applied the SHO algorithm on two very challenging real-life constrained engineering design problems (i.e., 25-bar truss design and multiple disk clutch brake design) and compared it with other various metaheuristic algorithms. The experimental results of engineering design problems reveal that SHO algorithm performs better than the other competitor metaheuristic algorithms.

This paper presents the contemporary metaheuristic optimization algorithm named the Spotted Hyena Optimizer (SHO). The proposed technique is based on the law of gravitation and simulates the social behavior of spotted hyenas. The three basic steps of SHO, namely, searching for prey, encircling, and attacking prey, are mathematically modelled and discussed. The main concept of this work is to apply the recently developed SHO algorithm to two real-life design problems, namely optical buffer design and airfoil design. Experimental results reveal the supremacy of the SHO algorithm for solving the engineering design problems as compared to other competitor algorithms.

This research is dedicated to slope stability analysis using novel intelligent models. By coupling a neural network with spotted hyena optimizer (SHO), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), and league champion optimization algorithm (LCA) metaheuristic algorithms, four predictive ensembles are built for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The data used to develop the ensembles are provided from a vast finite element analysis. After creating the proposed models, it was observed that the best population size for the SHO, SSA, SFLA, and LCA is 300, 400, 400, and 200, respectively. Evaluation of the results showed that the combination of metaheuristic and neural approaches offers capable tools for estimating the FOS. However, the SSA (error = 0.3532 and correlation = 0.9937), emerged as the most reliable optimizer, followed by LCA (error = 0.5430 and correlation = 0.9843), SFLA (error = 0.8176 and correlation = 0.9645), and SHO (error = 2.0887 and correlation = 0.8614). Due to the high accuracy of the SSA in properly adjusting the computational parameters of the neural network, the corresponding FOS predictive formula is presented to be used as a fast yet accurate substitution for traditional methods.

Drought is a complex natural phenomenon, so, precise prediction of drought is an effective mitigation tool for measuring the negative consequences on agriculture, ecosystems, hydrology, and water resources. The purpose of this research was to explore the potential capability of support vector regression (SVR) integrated with two meta-heuristic algorithms i.e., Grey Wolf Optimizer (GWO), and Spotted Hyena Optimizer (SHO), for meteorological drought (MD) prediction by utilizing EDI (effective drought index). For this objective, the two-hybrid SVR–GWO, and SVR–SHO models were constructed at Kumaon and Garhwal regions of Uttarakhand State (India). The EDI was computed in both study regions by using monthly rainfall data series to calibrate and validate the advanced hybrid SVR models. The autocorrelation function (ACF) and partial-ACF (PACF) were utilized to determine the optimal inputs (antecedent EDI) for EDI prediction. The results produced by the hybrid SVR models were compared with the calculated (observed) values by employing the statistical indicators and through graphical inspection. A comparison of results demonstrates that the hybrid SVR–GWO model outperformed to the SVR–SHO models for all study stations located in Kumaon and Garhwal regions. Also, the results highlighted the better suitability, supremacy, and convergence behavior of meta-heuristic algorithms (i.e., GWO and SHO) for meteorological drought prediction in the study regions.

Robotic manipulators are extremely nonlinear complex and, uncertain systems. They have multi-input multi-output (MIMO) dynamics, which makes controlling manipulators difficult. Robotic manipulators have wide applications in many industries like processes, medicine, and space. Effective control of these manipulators is extremely important to perform these industrial tasks. Researchers are working on the control of robotic manipulators using conventional and intelligent control methods. Conventional control methods are proportional integral and derivative (PID), Fractional order proportional integral and derivative (FOPID), sliding mode control (SMC), and optimal & robust control while intelligent control method includes Artificial Neural network (ANN), Fuzzy logic control (FLC) and metaheuristic optimization algorithms based control schemes. This paper presents the trajectory control of a robotic manipulator using a PID controller. Four different meta-heuristic algorithms namely Sooty tern optimization (STO), Spotted Hyena optimizer (SHO), Atom Search optimization (ASO), and Arithmetic Optimization algorithm (AOA) are used to optimize the gains of PID controller for trajectory control of a two-link robotic manipulator and a novel hybrid sooty tern and particle swarm optimization (STOPSO) has been designed. These optimization techniques are nature-inspired algorithms that give the optimal gain values while minimizing the performance indices. A performance index comprising Integral time absolute error (ITAE) having weights for both links has been considered to achieve the desired trajectory. These optimization techniques are stochastic in nature so statistical analysis and Freidman’s ranking test has been performed to evaluate the effectiveness of these algorithms. The proposed hybrid STOPSO provided a fitness value of 0.04541 and showed a standard deviation of 0.0002. A comparative study of these optimization techniques is presented and as a result, hybrid STOPSO provides the best results with minimum fitness value followed by STO, AOA, ASO, and SHO algorithms.

When it comes to road network maintenance and rehabilitation (M&R) work, a lack of funds is the main challenge faced by decision makers. At present, how to develop a scientific and reasonable M&R program to maximize the effects of road network maintenance with limited maintenance funds has been the focus of research in the field of road maintenance. In this regard, this study establishes a hierarchical maintenance decision-making (DM) model based on bi-level optimization to enhance the pavement performance of the road network as the maintenance objective. It divides the large-scale road network into sub-networks according to the road network characteristics and maintenance needs to realize the scientific allocation of maintenance resources and accurate M&R of the road network. To demonstrate the effectiveness of the model in maintaining the road network, four population-based metaheuristic algorithms, namely the genetic algorithm (GA), particle swarm optimization (PSO), the seagull optimization algorithm (SOA), and the spotted hyena optimizer (SHO), are selected to compute the real road network. The results show that SHO performed the best. Based on the initial road network, the objective function growth rate of SHO is improved by 10.13%, 2.45%, and 5.22% compared with GA, PSO, and SOA. Meanwhile, when compared with the traditional DM model without sub-network delineation, this model presents obvious hierarchical maintenance effects on different sub-networks, and the total pavement quality index (PQI) and the average PQI during the road network maintenance planning period are improved by 14.0% and 134%, respectively.

This paper introduces advanced computational methods for stock price prediction, integrating Fast Recurrent Neural Networks (FastRNN) with meta-heuristic algorithms such as the Horse Herd Optimization Algorithm (HOA) and the Spotted Hyena Optimizer (SHO). By challenging the Efficient Market Hypothesis (EMH) and Random Walk Hypothesis, our research demonstrates the effectiveness of these hybrid models in semi-strong or weak-form efficient markets. The study leverages data from five listed Iranian companies (2011–2021) and 25 factors encompassing technical, fundamental, and economic considerations. Our findings highlight the superior accuracy of the FastRNN optimised by HOA, SHO, and a Generative Adversarial Network (GAN) in forecasting stock prices compared to conventional FastRNN models. This research contributes to the multidisciplinary field of computational economics, emphasising advanced computing capabilities to address complex economic problems through innovative econometrics, optimisation, and machine learning approaches.

In this study, for the issue of shallow circular footing’s bearing capacity (also shown as Fult), we used the merits of artificial neural network (ANN), while optimized it by two metaheuristic algorithms (i.e., ant lion optimization (ALO) and the spotted hyena optimizer (SHO)). Several studies demonstrated that ANNs have significant results in terms of predicting the soil’s bearing capacity. Nevertheless, most models of ANN learning consist of different disadvantages. Accordantly, we focused on the application of two hybrid models of ALO–MLP and SHO–MLP for predicting the Fult placed in layered soils. Moreover, we performed an Extensive Finite Element (FE) modeling on 16 sets of soil layer (soft soil placed onto stronger soil and vice versa) considering a database that consists of 703 testing and 2810 training datasets for preparing the training and testing datasets. The independent variables in terms of ALO and SHO algorithms have been optimized by taking into account a trial and error process. The input data layers consisted of (i) upper layer foundation/thickness width (h/B) ratio, (ii) bottom and topsoil layer properties (for example, six of the most important properties of soil), (iii) vertical settlement (s), (iv) footing width (B), where the main target was taken Fult. According to RMSE and R2, values of (0.996 and 0.034) and (0.994 and 0.044) are obtained for training dataset and values of (0.994 and 0.040) and (0.991 and 0.050) are found for the testing dataset of proposed SHO–MLP and ALO–MLP best-fit prediction network structures, respectively. This proves higher reliability of the proposed hybrid model of SHO–MLP in approximating shallow circular footing bearing capacity.

Spotted hyena optimizer (SHO) is a novel heuristic optimization algorithm based on the behavior of spotted hyena and their collaborative behavior in nature. In this paper, we design a spotted hyena optimizer for feedforward neural networks (FNNs). Training feedforward neural networks is regard as a challenging task, because it is easy to fall into local optima. Our objective is to apply heuristic optimization algorithm design to tackle these problems better than the mathematical and deterministic methods, in order to confirm that SHO algorithm training FNN is more effective. a classification datasets about Heart is applies to benchmark the performance of the proposed method. The more basic SHO is compared to other acclaimed state-of-the-art optimization algorithm, the results show that the proposed algorithm can provide better results.

Cloud computing provides internet users with quick and efficient tools to access and share the data. One of the most important research problems that need to be addressed is the effective performance of cloud-based task scheduling. Different cloud-based task scheduling algorithms based on metaheuristic optimization techniques like genetic algorithm (GA) and particle swarm optimization (PSO) scheduling algorithms are demonstrated and analyzed. In this chapter, cloud computing based on the spotted hyena optimizer (SHO) is proposed with a novel task scheduling technique. SHO algorithm is population-based and inspired by nature's spotted hyenas to achieve global optimization over a given search space. The findings show that the suggested solution performs better than other competitor algorithms.

This paper presents the application of recently developed metaheuristic optimization algorithm, the spotted hyena optimizer, for solving both convex and non-convex economic dispatch problems. The proposed algorithm has been tested on various test systems (i.e. 6, 10, 20, and 40 generators systems) and compared with other well-known approaches to demonstrate its effectiveness and efficiency. The results show that the proposed algorithm is able to solve economic load power dispatch problem and converge toward the optimum with low computational efforts.

Microarray gene expression data holds the potential for diagnosis and prognosis of various genetic diseases. It is also used extensively in designing cancer classification techniques. But the enormity of genomic features and the lesser number of samples data make cancer classification a tedious task. This paper presents a novel hybrid metaheuristic optimization algorithm which is based on Differential Evolution (DE) and recently developed Spotted Hyena Optimizer (SHO) named as Hybrid Differential Evolutions and Spotted Hyena Optimizer (HDESHO) for cancer classification. The main contribution of this algorithm is to improve the mutation strategy of differential evolution using the spotted hyena optimizer algorithm. After the initial gene selection different machine learning algorithms were employed for performing cancer classification. The results state that the proposed approach outperforms as compared to the method discussed in the literature.