This report considers optimization of aircraft maneuvers in the vertical plane based on direct control methods. It proposes an object model for longitudinal motion suitable for optimal control, algorithms for control approximation and a numerical sol...
Multi-robot rescue path planning (MRRPP) is critical for ensuring the rapid and effective completion of post-disaster rescue tasks. Most studies focus on minimizing the length of rescue paths, the number of robots, and rescue time, neglecting the tas...
Different Population-based Algorithms (PbAs) have been used in recent years to solve all types of optimization problems related to water resource issues. However, the performances of these techniques depend heavily on correctly setting some specific...
Osteoarthritis (OA) is the most common joint disease associated with pain and disability. OA patients are at a high risk for venous thrombosis (VTE). Here, we developed an interpretable machine learning (ML)-based model to predict VTE risk in patient...
Population-based optimization algorithms have been widely applied to optimization problems in many fields and have achieved encouraging computational results. To improve the performance of population-based optimization algorithms, it is necessary to...
To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is di...
Population-based optimization algorithms are one of the most widely used and popular methods in solving optimization problems. In this paper, a new population-based optimization algorithm called the Teamwork Optimization Algorithm (TOA) is presented...
Recently, a population-based intelligent optimization algorithm research has been combined with multiple algorithms or algorithm components in order to improve the performance and robustness of an optimization algorithm. This paper introduces the ide...
Memetic algorithms achieve strong optimization performance by combining population-based global search with local refinement operators, yet their effectiveness critically depends on the design and management of memes. Local search strategies are typi...
Solar energy is used worldwide to alleviate the daily increasing demands for electric power. Photovoltaic (PV) cells, which are used to convert solar energy into electricity, can be represented as equivalent circuit models, in which a series of elect...
This paper introduces the hybrid population-based hill-climbing (HPHC) algorithm, a novel approach for generating cryptographically strong S-boxes that combines the efficiency of hill climbing with the exploration capabilities of population-based met...
When no-wait constraint holds in job shops, a job has to be processed with no waiting time from the first to the last operation, and the start time of a job is greatly restricted. Using key elements of the iterated greedy algorithm, this paper propos...
The JAYA algorithm is a population-based meta-heuristic algorithm proposed in recent years which has been proved to be suitable for solving global optimization and engineering optimization problems because of its simplicity, easy implementation, and...
The identifying code problem for a given graph involves finding a minimum subset of vertices such that each vertex of the graph is uniquely specified by its nonempty neighborhood within the identifying code. The combinatorial optimization problem has...
This paper delves into the scheduling of the two-machine flow-shop problem with step-learning, a scenario in which job processing times decrease if they commence after their learning dates. The objective is to optimize resource allocation and task se...
The electrical storm optimization (ESO) algorithm, inspired by the dynamic nature of electrical storms, is a novel population-based metaheuristic that employs three dynamically adjusted parameters: field resistance, field intensity, and field conduct...
Finding dominating sets in graphs is very important in the context of numerous real-world applications, especially in the area of wireless sensor networks. This is because network lifetime in wireless sensor networks can be prolonged by assigning sen...
Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms...
Optimization problems in various fields of science and engineering should be solved using appropriate methods. Stochastic search-based optimization algorithms are a widely used approach for solving optimization problems. In this paper, a new optimiza...
In recent decades, many optimization algorithms have been proposed by researchers to solve optimization problems in various branches of science. Optimization algorithms are designed based on various phenomena in nature, the laws of physics, the rules...
In the scenario where renewable energy sources (RESs) are connected to the power system (PS) through a flexible high-voltage direct current (HVDC) transmission system, their output becomes highly intermittent and volatile due to meteorological factor...
With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonl...
The science of biogeography was described through mathematical equations in 1967 by Robert MacArthur and Edward Wilson. In 2008, Dan Simon presented an algorithm called biogeography-based optimization, or BBO, which used some of the principles and de...
The transition to decentralized renewable energy systems has highlighted the role of AC microgrids and battery energy storage systems in achieving operational efficiency and sustainability. This study proposes an improved energy management system for...
This research investigates the effectiveness of four metaheuristic algorithms, the Population-Based Genetic Algorithm, Particle Swarm Optimization, JAYA, and Generalized Normal Distribution Optimizer, for managing the energy production of wind-based...
There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Populat...
With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their p...
With the increasing demand for earth observation in various fields, remote satellites play an important role in ground information assurance. Apparently, the effective scheduling and utilization of multi-satellite resources determine the quality and...
A situation where the set of initial solutions lies near the position of the true optimality (most favourable or desirable solution) by chance can increase the probability of finding the true optimality and significantly reduce the search efforts. In...
The massive nature of modern university programming courses increases the burden on academic workers. The Digital Teaching Assistant (DTA) system addresses this issue by automating unique programming exercise generation and checking, and provides mea...
SPARQ is an advanced global optimization algorithm, the direct evolution of an earlier method called ARQ2. It searches efficiently for the best solution to problems where the space of options is too vast to check exhaustively, such as tuning antenna...
This paper aims to study the comparative performance of original multi-objective population-based incremental learning (MPBIL) and three improvements of MPBIL. The first improvement of original MPBIL is an opposite-based concept, whereas the second a...
Multilabel feature selection is an effective preprocessing step for improving multilabel classification accuracy, because it highlights discriminative features for multiple labels. Recently, multi-population genetic algorithms have gained significant...
To reduce reconstruction errors during keyframe extraction and to control the optimal compression ratio, this study proposes a method for keyframe extraction from human motion capture data based on a multiple population genetic algorithm. The fitness...
The ongoing pandemic due to novel coronavirus disease-2019 (COVID-19) has rapidly unsettled the health sector with a considerable fatality rate. The main factors that help minimize the spread of this deadly virus are the proper use of masks, social d...
Optimization is an important and fundamental challenge to solve optimization problems in different scientific disciplines. In this paper, a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) is introduce...
Automated machine learning (AutoML) is recognized for its efficiency in facilitating model development due to its ability to perform tasks autonomously, without constant human intervention. AutoML automates the development and optimization of machine...
19 September 2024
During population-based HIV impact assessments (PHIAs), some participants who self-reported testing HIV-positive (PSRP) tested negative in one or more subsequent survey HIV tests. These unexpected discrepancies between their self-reported results and...
Daily runoff series exhibit high complexity and significant fluctuations, which often lead to large prediction errors and limit the scientific basis of water resource scheduling and management. This study proposes a runoff prediction framework that i...
In this paper, we propose a metaheuristic optimization algorithm called Adaptive Gold Rush Optimizer (AGRO), a substantial evolution of the original Gold Rush Optimizer (GRO). Unlike the standard GRO, which relies on fixed probabilities in the strate...
Multi-objective evolutionary algorithms mainly include the methods based on the Pareto dominance relationship and the methods based on decomposition. The method based on Pareto dominance relationship will produce a large number of non-dominated indiv...
Rural community population forecasting has important guiding significance to rural construction and development. In this study, a novel grey Bernoulli model combined with an improved Aquila Optimizer (IAO) was used to forecast rural community populat...
Train operation strategy optimization is a multi-objective optimization problem affected by multiple conditions and parameters, and it is difficult to solve it by using general optimization methods. In this paper, the parallel structure and double-po...
Aiming to resolve the problems of the traditional hierarchical clustering algorithm that cannot find clusters with uneven density, requires a large amount of calculation, and has low efficiency, this paper proposes an improved hierarchical clustering...
The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for suc...
In recent years, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed to tackle constrained multi-objective optimization problems (CMOPs). However, most of them still struggle to achieve a good balance among conver...
The economic load dispatch (ELD) problem is an optimization problem of minimizing the total fuel cost of generators while satisfying power balance constraints, operating capacity limits, ramp-rate limits and prohibited operating zones. In this paper,...
The Particle swarm optimization (PSO) algorithm has strong universality and fast convergence speed, but when solving complex multimodal optimization problems, it is prone to fall into local optimum due to insufficient population diversity. To address...
Due to their complexity and nonlinearity, metaheuristic algorithms have become the standard in problem solving for problems that cannot be solved by standard computational solutions. However, the global performance of these algorithms is strongly lin...
The conformal antenna is a precision device installed on the wing of an aircraft, and its components are distributed on a curved surface. Quality detection is required after assembly. In solving the path planning problem for conformal antenna surface...
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