The ant colony algorithm is an approach for path planning that is used in multiple industries. This paper proposes an improved robot path planning method, referred to as Improved-ACO. First, the heuristic information calculation is optimized to incre...
This study proposes a variable-scale optimization strategy for land-levelling path planning to overcome the limitations of conventional traversal-based operations, including poor coordination, insufficient planning, low operational efficiency, and th...
Free space optical communication (FSO) is widely deployed to transmit high data rates for rapid communication traffic increase. Asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) modulation is a very efficient FSO co...
The urban logistics delivery problem is a complex path planning task that must balance logistics costs and time-window-based quality of service under time-varying traffic conditions. Most urban logistics models assume constant travel speeds, thereby...
This paper addresses the optimal path selection problem for economic corridors, which is a significant issue in the field of geo-economics. The paper has utilized the spatiotemporal characteristics of geo-economics and identified the development need...
Learning the Bayesian networks (BNs) structure from data has received increasing attention. Many heuristic algorithms have been introduced to search for the optimal network that best matches the given training data set. To further improve the perform...
Under the “Double Carbon” background, the development of green agricultural machinery is very fast. An important factor that determines the performance of electric farm machinery is the endurance capacity, which is directly related to the...
In this paper, a hybrid optimization controller that combines a genetic algorithm (GA) and ant colony optimization (ACO) called GA-ACO algorithm is proposed. It is applied to a photovoltaic module array (PVMA) to carry out maximum power point trackin...
Faced with complex, ever-changing battlefield environments and diverse attacks, enabling warship combat systems to recover rapidly and effectively after damage is key to enhancing resilience and sustained combat capability. We construct a representat...
Fruit quality is determined by multiple metabolites, which are mainly affected by plant hormones. In this study, two genes, ethylene-related gene SlACO1 and gibberellin-related gene SlGARP, were overexpressed (OE) and knocked down through RNAi in tom...
Ant-inspired metaheuristic algorithms known as ant colony optimization (ACO) offer an approach that has the ability to solve complex problems in both discrete and continuous domains. ACOs have gained significant attention in the field of water resour...
Background and Objectives: The electrocardiogram (ECG)–based STEMI/NSTEMI classification determines the urgency of invasive management in acute myocardial infarction. However, it often underestimates the presence of acute coronary occlusion (AC...
Opposition-based learning (OBL) has been widely used to improve many swarm intelligent optimization (SI) algorithms for continuous problems during the past few decades. When the SI optimization algorithms apply OBL to solve discrete problems, the con...
This paper proposes an improved ant colony optimization-based path planning method for UAV inspection in substations. Considering the equipment partition characteristics and no-fly zone constraints, a two-dimensional inspection scenario model is cons...
Background. Patients with nonvalvular atrial fibrillation (NVAF) have five times higher risk of stroke than the general population. Anticoagulation (ACO) in NVAF is a class I indication after assessing the CHA2DS2-VASc and HAS-BLED scores. However, i...
The plant hormone ethylene elicits crucial regulatory effects on plant growth, development, and stress resistance. As the enzyme that catalyzes the final step of ethylene biosynthesis, 1-Aminocyclopropane-1-carboxylic acid oxidase (ACO) plays a key r...
Cloud computing is a distributed computing model which renders services for cloud users around the world. These services need to be rendered to customers with high availability and fault tolerance, but there are still chances of having single-point f...
The impacts of nanoparticles-zerovalent iron (NP-ZVI) on anaerobic co-digestion (AcoD) were assessed. The production of biogas and methane (CH4), as well as the removal efficiency of volatile solids (VS) and contaminants were investigated in the AcoD...
Solving the constraint satisfaction problem (CSP) is to find an assignment of values to variables that satisfies a set of constraints. Ant colony optimization (ACO) is an efficient algorithm for solving CSPs. However, the existing ACO-based algorithm...
This study proposes a hybrid ant colony optimization–backstepping control (ACO-BC) strategy for a multi-machine wind energy conversion system based on five-phase permanent magnet synchronous generators (PMSGs). The proposed strategy is develope...
This paper proposes a long short-term memory (LSTM) network to predict the power degradation of proton exchange membrane fuel cells (PEMFCs), and in order to promote the performance of the LSTM network, the ant colony algorithm (ACO) is introduced to...
Anaerobic co-digestion (AcoD) of food waste (FW) and lignocellulose waste is a promising technology for methane production. This work investigated the methane generation from AcoD of FW, sugarcane leaves (SLs), and cow dung (CD) under mesophilic cond...
Ant Colony Optimization (ACO) encompasses a family of metaheuristics inspired by the foraging behaviour of ants. Since the introduction of the first ACO algorithm, called Ant System (AS), several ACO variants have been proposed in the literature. Owi...
Asthma and COPD overlap (ACO) is characterized by patients presenting with persistent airflow limitation and features of both asthma and COPD. It is associated with a higher frequency and severity of exacerbations, a faster lung function decline, and...
This study addresses the Multi-Skill Resource-Constrained Project Scheduling Problem (MSRCPSP) by investigating the simultaneous effects of workforce skill proficiency (depth) and skill versatility (breadth) on project makespan reduction. Two state-o...
For accurate estimation of broiler chicken weight (CW), a novel hybrid method was developed in this study where several benchmark methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Differentia...
The physiological performance of early-maturing nectarine trees in response to water deficits was studied during the postharvest period. Two deficit irrigation treatments were applied, moderate and severe, and these were compared with a control treat...
The telecommunications industry is greatly concerned about customer churn due to dissatisfaction with service. This industry has started investing in the development of machine learning (ML) models for churn prediction to extract, examine and visuali...
Trajectory tracking is a crucial task for autonomous mobile robots, requiring smooth and safe execution in dynamic environments. This study uses a nonlinear model predictive controller (MPC) to ensure accurate trajectory tracking of a four-wheel driv...
Background: Amyotrophic lateral sclerosis (ALS) is a multisystemic, progressive, neurodegenerative disorder. Despite it being generally fatal within a period of 2–4 years, it is highly heterogeneous; as a result, survival periods may vary great...
Ant Colony Optimization (ACO) is a practical and well-studied bio-inspired algorithm to generate feasible solutions for combinatorial optimization problems such as the Traveling Salesman Problem (TSP). ACO is inspired by the foraging behavior of ants...
In order to reduce fuel consumption and reduce the deviation between the final battery state-of-charge (SOC) value and the target value at the same time, a novel double-layer multi-objective optimization method is proposed, which adopts an improved a...
This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Optimization (ACO) technique for TSP due to its versatile and easily adap...
Load Balancing (LB) is one of the most important tasks required to maximize network performance, scalability and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), LB for SDN has become a very important issue. SDN decouple...
The expansion of the tourism industry has led to the development of various methods to find optimal tourism paths. However, planning tourism paths in hilly areas remains complex and has specific challenges. Different algorithms have been used to plan...
Ant Colony Optimization (ACO) is a widely adopted metaheuristic for solving complex combinatorial problems; however, performance is often deteriorated by premature convergence and limited exploration in later iterations. Eclipse Randomness–Ant...
In rural Ghana, limited access to affordable, clean cooking fuels drives the need for decentralised waste-to-energy solutions. Anaerobic co-digestion (AcoD) offers a viable route for transforming organic residues into renewable energy, with the added...
Ambulatory health care provider organizations participating in Accountable Care Organizations (ACOs) organizations assume costs beyond typical practice operations that are directly associated with value-based care initiatives. Identifying these varia...
This paper presents an enhanced Ant Colony Optimization (ACO) algorithm tailored for optimizing CMOS physical design in VLSI circuits. As device complexity escalates, traditional placement techniques struggle with multiobjective goals such as minimiz...
Concrete is one of the most important, versatile, and widely used building materials worldwide. Thus, an optimized delivery schedule of ready-mixed concrete (RMC) is a critical issue that can reduce CO2 emission from RMC delivery vehicles. RMC is the...
Traditional ant colony optimization (ACO) often suffers from insufficient initial pheromone information, premature convergence, and limited adaptability across problem scales when solving the traveling salesman problem (TSP). This study proposes a mu...
Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the objective of intelligently combining heuristic methods to solve hard optimization problems. Ant colony optimization (ACO) algorithms have been proven to deal w...
In this paper, we proposed and developed Function-Oriented Networking (FON), a platform for network users. It has a different philosophy as opposed to technologies for network managers of Software-Defined Networking technology, OpenFlow. It is a tech...
Automated blood-cell detection can support microscopic analysis, but detector performance depends strongly on the training configuration. This study investigates a topology-guided Ant Colony Optimization (ACO) strategy for jointly selecting 21 hyperp...
Background: Serious secondary disasters caused by extreme natural weather conditions occur frequently, making it essential to establish a scientific and efficient modern emergency management system to maximize life-saving efforts. Methods: This study...
To address the safety and efficiency challenges in planning emergency evacuation routes for personnel in complex environments, this study proposes an integrated and improved ant colony optimization (ACO) with a genetic algorithm (GA). First, an emerg...
Finding reliable and efficient routes is a persistent problem in megacities. To address this problem, several algorithms have been proposed. However, there are still areas of research that require attention. Many traffic-related problems can be resol...
Tourist flow prediction plays a crucial role in enhancing the efficiency of scenic area management, optimizing resource allocation, and promoting the sustainable development of the tourism industry. To improve the accuracy and real-time performance o...
This study outlines the applicability of four metaheuristic algorithms, namely, whale optimization algorithm (WOA), league champion optimization (LCA), moth–flame optimization (MFO), and ant colony optimization (ACO), for performance improvemen...
Unmanned aerial vehicles (UAVs) have become increasingly popular in the civil field, and building inspection is one of the most promising applications. In a rural area, the UAVs are assigned to inspect the surface of buildings, and an unmanned ground...
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