The moth search algorithm (MS) is a relatively new metaheuristic optimization algorithm which mimics the phototaxis and Lévy flights of moths. Being an NP-hard problem, the 0–1 multidimensional knapsack problem (MKP) is a classical multi...
Moth search (MS) algorithm, originally proposed to solve continuous optimization problems, is a novel bio-inspired metaheuristic algorithm. At present, there seems to be little concern about using MS to solve discrete optimization problems. One of th...
In this research, we study a new metaheuristic algorithm called Moth–Flame Optimization (MFO) for hyperspectral band selection. With the hundreds of highly correlated narrow spectral bands, the number of training samples required to train a sta...
A triple-band single-layer rectenna for outdoor RF energy applications is introduced in this paper. The proposed rectenna operates in the frequency bands of LoRa, GSM-1800, and UMTS-2100 networks. To obtain a triple-band operation, a modified E-shape...
This paper presents a new optimization technique based on the hybridization of two meta-heuristic methods, Jellyfish Search (JS) and Moth Flame Optimizer (MFO), to solve the Optimal Power Flow (OPF) problem. The JS algorithm offers good exploration c...
Moth search (MS) is a nature-inspired metaheuristic optimization algorithm based on the most representative characteristics of moths, Lévy flights and phototaxis. Phototaxis signifies a movement which organism towards or away from a source of...
Odor-source localization, by which one finds the source of an odor by detecting the odor itself, is an important ability to possess in order to search for leaking gases, explosives, and disaster survivors. Although many animals possess this ability,...
In this paper, an improved moth-flame optimization algorithm (IMFO) is presented to solve engineering problems. Two novel effective strategies composed of Lévy flight and dimension-by-dimension evaluation are synchronously introduced into the...
The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its moveme...
Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergen...
To solve the multi-objective optimization problem (MOP) for parameter optimization of a multiple spacecraft attitude controller, an improved multi-objective moth–flame optimization algorithm based on r-domination (rIMOMFO) is proposed. In rIMOM...
Moth-flame optimization is a typical meta-heuristic algorithm, but it has the shortcomings of low-optimization accuracy and a high risk of falling into local optima. Therefore, this paper proposes an enhanced moth-flame optimization algorithm named H...
Automatic parking path optimization is a key point for automatic parking. However, it is difficult to obtain the smooth, accurate and optimal parking path by using traditional automatic parking optimization algorithms. So, based on the automatic park...
In recent years, the demand for mobile robots to perform odor source localization tasks in dangerous environments has been increasing, and this technology has gradually become a focus of research. However, existing bio-inspired algorithms still have...
This paper proposes two bio-inspired heuristic algorithms, the Moth-Flame Optimization (MFO) algorithm and Genetic Algorithm (GA), for an Energy Management System (EMS) in smart homes and buildings. Their performance in terms of energy cost reduction...
Lévy flight is a random walk mechanism which can make large jumps at local locations with a high probability. The probability density distribution of Lévy flight was characterized by sharp peaks, asymmetry, and trailing. Its movement pa...
This article presents two new chemical plume tracing (CPT) algorithms for using on autonomous underwater vehicles (AUVs) to locate hydrothermal vents. We aim to design effective CPT navigation algorithms that direct AUVs to trace emitted hydrothermal...
The optimal configuration of flexible workshop resources is critical to production efficiency, while disturbances pose significant challenges to the effectiveness of the configuration. Therefore, this paper proposes a hybrid-driven resource dynamic c...
A comparative study is performed to design an optimal PID controller for an automatic voltage regulator system using different optimization techniques. The presented approaches are referred to as particle swarm optimization (PSO) algorithm, cuckoo se...
This paper proposes a novel method for synthesizing a discrete optimization algorithm based on the moth–flame paradigm for application to the architecture of deceptive systems incorporating decoys and traps. Unlike existing approaches that prim...
Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to...
In electrical power system engineering, the economic load dispatch (ELD) problem is a critical issue for fuel cost minimization. This ELD problem is often characterized by non-convexity and subject to multiple constraints. These constraints include v...
Due to the inflexibility of cogeneration power plants and the uncertainty of wind power production, the excess power of the distribution network brings challenges to the power grid operation. This paper introduced an improved moth-flame optimization...
In order to make a robot track a desired trajectory with high precision and steady gait, a novel intelligent controller was designed based on a new mechanical structure and optimized foot trajectory. Kinematics models in terms of the D-H method were...
Metaheuristic algorithms have gained popularity in the past decade due to their remarkable ability to address various optimization challenges. Among these, the JAYA algorithm has emerged as a recent contender that demonstrates strong performance acro...
This work addresses an evolutionary algorithmic approach to reduce the surplus pieces in selective assembly to increase success rates. A novel equal area amidst unequal bin numbers (EAUB) method is proposed for classifying the parts of the ball beari...
To address the green reentrant hybrid flow shop-scheduling problem (GRHFSP), we performed lifecycle assessments for evaluating the comprehensive impact of resources and the environment. An optimization model was established to minimize the maximum co...
This paper presents an optimization algorithm named Random Explosion Algorithm (REA). The fundamental idea of this algorithm is based on a simple concept of the explosion of an object. This object is commonly known as a particle: when exploded, it wi...
Addressing the imperative for energy transition amid depleting fossil fuels, distributed generation (DG) is increasingly integrated into distribution networks (DNs). This integration necessitates low-carbon dispatching solutions that reconcile econom...
The cactus moth, Cactoblastis cactorum (Berg) (Lepidoptera: Pyralidae), is an invasive species in North America where it threatens Opuntia native populations. The insect is expanding its distribution along the United States Gulf Coast. In the search...
Grey Wolf Optimizer (GWO) has been widely applied in many fields due to its advantages of fast convergence speed, simple parameter settings, and easy implementation. However, with the deepening of algorithm research, GWO has also exposed some problem...
Recently, perovskite solar cells (PSCs) have been widely investigated as an efficient alternative for silicon solar cells. In this work, a proposed modified triple-diode model (MTDM) for PSCs modeling and simulation was used. The Bald Eagle Search (B...
The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, t...
With the advancement of remote sensing technology, the demand for the accurate monitoring and tracking of various targets utilizing unmanned aerial vehicles (UAVs) is increasing. However, challenges such as object deformation, motion blur, and object...
This study deals with the optimization of battery energy storage system (BESS) data in terms of significant characteristics of life and efficiency, and their positive impacts on power system efficiency in the presence of wind power plants in a microg...
Integration of Renewable Distributed Generations (RDGs) such as photovoltaic (PV) systems and wind turbines (WTs) in distribution networks can be considered a brilliant and efficient solution to the growing demand for energy. This article introduces...
This paper presents the design and field evaluation of a low-cost automatic steering system for agricultural tractors. The system employs a PID controller whose gains are tuned using a metaheuristic optimization method. Core hardware includes an ESP3...
This study introduces a novel method for assessing and deriving the electrical properties of simple diode model solar cells through the utilization of the Earthworm Optimization Algorithm (EOA). Earthworms learn how to avoid barriers and maximize the...
Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud computing employ a moderate design, wherein computational resources like storage and servers a...
The Growth Optimizer algorithm (GO) is a novel metaheuristic that draws inspiration from people’s learning and introspection processes as they progress through society. Economic Load Dispatch (ELD), one of the primary problems in the power syst...
This article proposes a novel damage detection method based on the sensitivity analysis and chaotic moth-flame-invasive weed optimization (CMF-IWO), which is utilized to simultaneously identify the damage of structural elements and bearings. First, t...
Integration testing is an essential activity in software testing, especially in object-oriented software development. Determining the sequence of classes to be integrated, i.e., the class integration test order (CITO) problem, is of great importance...
The osprey optimization algorithm (OOA) is a new metaheuristic motivated by the strategy of hunting fish in seas. In this study, the OOA is applied to solve one of the main items in a power system called economic load dispatch (ELD). The ELD has two...
Due to the complexity of multi-objective engineering problems, solutions obtained by many algorithms often exhibit poor distribution, and the algorithms tend to fall into local optima. To effectively alleviate these issues, an improved multi-objectiv...
Conservation researchers require low-cost access to acoustic monitoring technology. However, affordable tools are often constrained to short-term studies due to high energy consumption and limited storage. To enable long-term monitoring, energy and s...
Brent oil holds a significant position in the global energy market, as oil prices in many regions are indexed to it. Therefore, forecasting the future price of Brent oil is of great importance. In recent years, artificial intelligence techniques have...
The penetration of intermittent wind turbines in power systems imposes challenges to frequency stability. In this light, a new control method is presented in this paper by proposing a modified fractional order proportional integral derivative (FOPID)...
Proportional integral (PI) control is still the most widely deployed controller in the industrial drives due to its simplicity and the fact that it is easy to understand and implement. Nevertheless, they are successes applied to systems with a comple...
Odor source localization in turbulent environments remains a major challenge for autonomous robots, as odor plumes are highly intermittent, spatially fragmented, and often lack stable concentration gradients. Here, we propose a bio-inspired navigatio...
Software defect prediction (SDP) is crucial in the early stages of defect-free software development before testing operations take place. Effective SDP can help test managers locate defects and defect-prone software modules. This facilitates the allo...
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