The Aquila Optimizer (AO) is a new bio-inspired meta-heuristic algorithm inspired by Aquila’s hunting behavior. Adaptive Aquila Optimizer Combining Niche Thought with Dispersed Chaotic Swarm (NCAAO) is proposed to address the problem that altho...
Oil production forecasting is one of the essential processes for organizations and governments to make necessary economic plans. This paper proposes a novel hybrid intelligence time series model to forecast oil production from two different oil field...
In recent years, integration of production scheduling and machine maintenance has gained increasing attention in order to improve the stability and efficiency of flowshop manufacturing systems. This paper proposes a Q-learning-based aquila optimizer...
To enhance the accuracy and robustness of lithium-ion battery state-of-health (SOH) prediction, this study proposes a dual-mode intelligent optimization BP neural network model (AO–AVOA–BP) which integrates the Aquila Optimizer (AO) and t...
Multilevel inverters (MLIs) are capable of producing high-quality output voltage and handling large amounts of power. This reduces the size of the filter while also simplifying the circuitry. As a result, they have a wide range of applications in ind...
This paper presents a system for medical image diagnosis that uses transfer learning (TL) and feature selection techniques. The main aim of TL on pre-trained models such as MobileNetV3 is to extract features from raw images. Here, a novel feature sel...
The Aquila Optimizer (AO), inspired by the hunting behavior of Aquila birds, is a recent nature-inspired metaheuristic algorithm recognized for its simplicity and low computational cost. However, the conventional AO often suffers from premature conve...
The Aquila Optimizer (AO) is a metaheuristic algorithm that is inspired by the hunting behavior of the Aquila bird. The AO approach has been proven to perform effectively on a range of benchmark optimization issues. However, the AO algorithm may suff...
The Aquila Optimizer (AO) is a novel and efficient optimization algorithm inspired by the hunting and searching behavior of Aquila. However, the AO faces limitations when tackling high-dimensional and complex optimization problems due to insufficient...
The Aquila Optimizer (AO) algorithm is a meta-heuristic algorithm with excellent performance, although it may be insufficient or tend to fall into local optima as as the complexity of real-world optimization problems increases. To overcome the shortc...
In this paper, Chaotic Aquila Optimization has been proposed for the solution of the demand response program of a grid-connected residential microgrid (GCRMG) system. Here, the main objective is to optimize the scheduling pattern of connected applian...
This paper presents a new methodology for determining the optimal coefficients of a PID controller for a four-switch buck–boost (FSBB) converter. The main objective of this research is to improve the performance of FSBB converters by fine-tunin...
As sensors are distributed among wireless sensor networks (WSNs), ensuring that the batteries and processing power last for a long time, to improve energy consumption and extend the lifetime of the WSN, is a significant challenge in the design of net...
Swarm intelligence-based metaheuristic algorithms have attracted the attention of the research community and have been exploited for effectively solving different optimization problems of engineering, science, and technology. This paper considers the...
One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly str...
Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases th...
Background: Melanoma is a highly aggressive form of skin cancer, necessitating early and accurate detection for effective treatment. This study aims to develop a novel classification system for melanoma detection that integrates Convolutional Neural...
This study proposes a multi-strategy improved Aquila optimizer (MIAO) to address the key limitations of the original Aquila optimizer (AO). First, a phasor operator is introduced to eliminate excessive control parameters in the X2 phase, transforming...
This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stocha...
Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance o...
This paper proposes a hybrid optimization method to optimize the topological structure of an offshore-wind-farm power collection system, in which the cable connection, cable selection and substation location are optimally designed. Firstly, the optim...
With the rapid popularization and development of renewable energy, solar photovoltaic power generation systems have become an important energy choice. Convolutional neural network (CNN) models have been widely used in photovoltaic power forecasting,...
Recently, a new optimizer, called the Aquila Optimizer (AO), was developed to solve different optimization problems. Although the AO has a significant performance in various problems, like other optimization algorithms, the AO suffers from certain li...
In this paper, we propose a modified hybrid Salp Swarm Algorithm (SSA) and Aquila Optimizer (AO) named IHSSAO for UAV path planning in complex terrain. The primary logic of the proposed IHSSAO is to enhance the performance of AO by introducing the le...
Aquila Optimizer (AO) and Harris Hawks Optimizer (HHO) are recently proposed meta-heuristic optimization algorithms. AO possesses strong global exploration capability but insufficient local exploitation ability. However, the exploitation phase of HHO...
It is necessary to study different aspects of renewable energy generation, including wind energy. Wind power is one of the most important green and renewable energy resources. The estimation of wind energy generation is a critical task that has recei...
Aquila Optimizer (AO) and Artificial Rabbits Optimization (ARO) are two recently developed meta-heuristic optimization algorithms. Although AO has powerful exploration capability, it still suffers from poor solution accuracy and premature convergence...
Statistical distribution approaches have been developed to describe wind data due to the intermittent and unpredictable nature of wind speed. The Weibull distribution with two parameters is thought to be the most accurate distribution for modeling wi...
For the sake of conducting distribution network reliability prediction in an accurate and efficient manner, a model for distribution network reliability prediction (IAO-LSSVM) based on an improved Aquila Optimizer (IAO) optimized mixed-kernel Least S...
This paper presents ocean wave energy control using the Aquila optimization (AO) technique. An oscillating water column (OWC)-type wave energy converter has been considered that is fitted with a Wells turbine and doubly fed induction generator (DFIG)...
Blockchain has become one of the key techniques for the security of the industrial internet. However, the blockchain is vulnerable to FAW (Fork after Withholding) attacks. To protect the industrial internet from FAW attacks, this paper proposes a nov...
This study proposed a separation method to identify the temperature-induced response from the long-term monitoring data with noise and other action-induced effects. In the proposed method, the original measured data are transformed using the local ou...
Feature selection (FS) is applied to reduce data dimensions while retaining much information. Many optimization methods have been applied to enhance the efficiency of FS algorithms. These approaches reduce the processing time and improve the accuracy...
Monitoring the condition of the grinding wheel in real-time during the grinding process is crucial as it directly impacts the precision and quality of the workpiece. Deep learning technology plays a vital role in analyzing the changes in sensor signa...
This paper proposes a controller to track the maximum power point (MPP) of a photovoltaic (PV) system using a fractional-order proportional integral derivative (FOPID) controller. The employed MPPT is operated based on a dp/dv feedback approach. The...
Wind energy is particularly significant in the power system today since it is a cheap and clean power source. The unpredictability of wind speed leads to uncertainty in devolved power which increases the difficulty in wind energy system operation. Th...
The Mayfly Optimization Algorithm (MOA), as a new biomimetic metaheuristic algorithm with superior algorithm framework and optimization methods, plays a remarkable role in solving optimization problems. However, there are still shortcomings of conver...
Partial discharge (PD) is a primary factor leading to the deterioration of insulation in electrical equipment. However, it is hard for traditional methods to precisely extract PD signals in increasingly complex engineering environments. This paper pr...
The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling...
The accurate estimation of reference crop evapotranspiration (ET0) is essential for crop water consumption modeling and agricultural water resource management. In the present study, three bionic algorithms (aquila optimizer (AO), tuna swarm optimizat...
Sentiment analysis holds great importance within the domain of natural language processing as it examines both the expressed and underlying emotions conveyed through review content. Furthermore, researchers have discovered that relying solely on the...
China produces the largest amount of CO2 emissions since 2007 and is the second largest economy in the world since 2010, and the Yangtze River Delta (YRD) area plays a crucial role in promoting low-carbon development in China. Analyzing its evolution...
The high mobility and dynamic nature of unmanned aerial vehicles (UAVs) pose significant challenges to clustering and routing in flying ad hoc networks (FANETs). Traditional methods often fail to achieve stable networks with efficient resource utiliz...
Due to the symmetry of the rolling bearing structure and the rotating operation mode, it will cause the coupling modulation phenomenon when it is damaged in multiple places at the same time, which makes it difficult to accurately identify all kinds o...
In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using...
Wireless sensor networks (WSNs) have been developed recently to support several applications, including environmental monitoring, traffic control, smart battlefield, home automation, etc. WSNs include numerous sensors that can be dispersed around a s...
The Internet of things (IoT) has recently received a great deal of attention, and there has been a large increase in the number of IoT devices owing to its significance in current communication networks. In addition, the validation of devices is an i...
Nowadays, the electrical power system has become a more complex, interconnected network that is expanding every day. Hence, the power system faces many problems such as increasing power losses, voltage deviation, line overloads, etc. The optimization...
Existing swarm intelligence (SI) optimization algorithms applied to node localization optimization (NLO) and node coverage optimization (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comp...
Metamaterials, characterized by unique structures, exhibit exceptional properties applicable across various domains. Traditional methods like experiments and finite-element methods (FEM) have been extensively utilized to characterize these properties...
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