Recent advances in multi-objective grey wolf optimizer, its versions and applications

Makhadmeh, Sharif Naser; Alomari, Osama Ahmad; Mirjalili, Seyedali; Al-Betar, Mohammed Azmi; Elnagar, Ashraf

doi:10.1007/s00521-022-07704-5

Cited by 42 publications

(10 citation statements)

References 101 publications

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“…The α , β , and δ classes are organized from the highest to the lowest fitness, giving rise to three primary wolf ranks, with ω wolves filling the remaining positions. The hunting strategy of gray wolves involves tracking the prey, moving closer, surrounding it, and finally initiating the attack 50 …”

Section: Methodsmentioning

confidence: 99%

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Güven,

Mengi

2024

Optim Control Appl Methods

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Time‐delayed systems frequently appear, especially in sectors such as fluid flow processes, chemical procedures, and the food industry. This paper addresses the optimization of parameters for a fractional order PID (FOPID) controller, which is used to control a time‐delayed system, using five distinct algorithms inspired by nature. These algorithms are NewBAT, Cuckoo search (CS), Firefly (FF), Gray Wolf Optimizer (GWO), and Whale optimization algorithm (WOA). The FOPID controller parameters, namely KP, KI, KD, λ and μ, have been optimized using these algorithms. During the optimization process, the integral of the time absolute error (ITAE) was considered as the primary measurement criterion. In addition to this value, the maximum overshoot, settling time, time to reach the maximum value, and error values were examined. Simulations conducted with the obtained parameters tested the system's resilience to disturbances introduced at the output, and the controller responses were also evaluated during these tests. The reactions of the determined parameters to different reference inputs were analyzed, and the results are presented in graphs and tables. The efficiency and reliability of the optimization algorithms were substantiated by comprehensive statistical analyses. These analyses play a critical role in algorithm selection and objective evaluation of the results. Simulation studies were conducted in the Matlab and Simulink environments. The FOMCON Toolbox was used for fractional‐order processes.

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Section: Methodsmentioning

confidence: 99%

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Güven,

Mengi

2024

Optim Control Appl Methods

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“…The population of wolves in the GWO algorithm symbolises various solutions to an optimisation issue. The position of each wolf correlates to a proposed solution, and their fitness affects their hunting capacity (Makhadmeh et al, 2022). The programme iteratively updates the locations of the wolves using a set of rules to find the best option.…”

Section: Grey-wolf Optimizermentioning

confidence: 99%

“…The programme iteratively updates the locations of the wolves using a set of rules to find the best option. The Grey Wolf Optimizer algorithm's pseudo code is as follows (Abualigah et al, 2020;Makhadmeh et al, 2022):…”

Section: Grey-wolf Optimizermentioning

confidence: 99%

“…Furthermore, GWO exhibits durability and flexibility, allowing it to handle dynamic situations and changing restrictions while maintaining constant optimisation performance even in the midst of uncertainty. Its adaptability is demonstrated by successful implementations in a variety of optimisation settings, emphasising its potential for resolving the complexities and constraints inherent in biodiesel production (Makhadmeh et al, 2022;Veza et al, 2022). Hence, in the present study, an attempt is made to optimize the biodiesel production process from Nahar (Ceylon ironwood) feedstock.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of biodiesel production from Nahar oil using Box-Behnken design, ANOVA and grey wolf optimizer

Nguyen

Sharma²,

Kumar³

et al. 2023

Int. J. Renew. Energy Dev.

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Biodiesel manufacturing from renewable feedstocks has received a lot of attention as a viable alternative to fossil fuels. The Box-Behnken design, analysis of variance (ANOVA), and the Grey Wolf Optimizer (GWO) algorithm were used in this work to optimise biodiesel production from Nahar oil. The goal was to determine the best operating parameters for maximising biodiesel yield. The Box-Behnken design is used, with four essential parameters taken into account: molar ratio, reaction duration and temperature, and catalyst weight percentage. The response surface is studied in this design, and the key factors influencing biodiesel yield are discovered. The gathered data is given to ANOVA analysis to determine the statistical significance. ANOVA analysis is performed on the acquired data to determine the statistical significance of the components and their interactions. The GWO algorithm is used to better optimise the biodiesel production process. Based on the data provided, the GWO algorithm obtains an optimised yield of 91.6484% by running the reaction for 200 minutes, using a molar ratio of 7, and a catalyst weight percentage of 1.2. As indicated by the lower boundaries, the reaction temperature ranges from 50 °C. The results show that the Box-Behnken design, ANOVA, and GWO algorithm were successfully integrated for optimising biodiesel production from Nahar oil. This method offers useful insights into process optimisation and indicates the possibilities for increasing the efficiency and sustainability of biodiesel production. Further study can broaden the use of these strategies to various biodiesel production processes and feedstocks, advancing sustainable energy technology.

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“…To accomplish this task, it mimics the wolves’ behaviors, such as searching the prey, encircling, and attacking the prey. The GWO offers a robust optimization framework for dealing with complicated issues by emulating these behaviors ( Makhadmeh et al, 2023 ). An innovative strategy for parameter selection for GARCH and ARIMA models using the GWO algorithm has not been studied in the literature.…”

Section: Introductionmentioning

confidence: 99%

Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction

Bagalkot,

Naik

2024

PeerJ Computer Science

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Stock price data often exhibit nonlinear patterns and dynamics in nature. The parameter selection in generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA) models is challenging due to stock price volatility. Most studies examined the manual method for parameter selection in GARCH and ARIMA models. These procedures are time-consuming and based on trial and error. To overcome this, we considered a GWO method for finding the optimal parameters in GARCH and ARIMA models. The motivation behind considering the grey wolf optimizer (GWO) is one of the popular methods for parameter optimization. The novel GWO-based parameters selection approach for GARCH and ARIMA models aims to improve stock price prediction accuracy by optimizing the parameters of ARIMA and GARCH models. The hierarchical structure of GWO comprises four distinct categories: alpha (α), beta (β), delta (δ) and omega (ω). The predatory conduct of wolves primarily encompasses the act of pursuing and closing in on the prey, tracing the movements of the prey, and ultimately launching an attack on the prey. In the proposed context, attacking prey is a selection of the best parameters for GARCH and ARIMA models. The GWO algorithm iteratively updates the positions of wolves to provide potential solutions in the search space in GARCH and ARIMA models. The proposed model is evaluated using root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE). The GWO-based parameter selection for GARCH and ARIMA improves the performance of the model by 5% to 8% compared to existing traditional GARCH and ARIMA models.

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Recent advances in multi-objective grey wolf optimizer, its versions and applications

Cited by 42 publications

References 101 publications

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Optimization of biodiesel production from Nahar oil using Box-Behnken design, ANOVA and grey wolf optimizer

Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction

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