The method of collecting aerial images or videos by unmanned aerial vehicles (UAVs) for object search has the advantages of high flexibility and low cost, and has been widely used in various fields, such as pipeline inspection, disaster rescue, and f...
Object tracking refers to the relocation of specific objects in consecutive frames of a video sequence. Presently, this visual task is still considered an open research issue, and the computer science community attempted solutions from the standpoint...
Neural architecture search (NAS) is a popular branch of automatic machine learning (AutoML), which aims to search for efficient network structures. Many prior works have explored a wide range of search algorithms for classification tasks, and have ac...
Advancements in optical satellite hardware and lowered costs for satellite launches raised the high demand for geospatial intelligence. The object recognition problem in multi-spectral satellite imagery carries dataset properties unique to this probl...
With the development of remote sensing earth observation technology, object tracking has gained attention for its broad application prospects in computer vision. However, object tracking is challenging owing to the background clutter, occlusion, and...
This paper presents the investigations on the improvement of search object detection during search and rescue (SAR) action at sea using thermal imaging and radar sensors. The introduction of new materials in the construction of lifesaving appliances...
This paper proposes and evaluates two neural network-based approaches for object classification in automotive radar systems, comparing the performance impact of grid search and genetic algorithm (GA) hyperparameter optimization strategies. The task i...
With the development of computer vision and Unmanned Aerial Vehicles (UAVs) technology, visual object tracking has become an indispensable core technology for UAVs, and it has been widely used in both civil and military fields. Visual object tracking...
Unmanned aerial vehicles are increasingly used in civil Search and Rescue operations due to their rapid deployment and wide-area coverage capabilities. However, detecting missing persons from aerial imagery remains challenging due to small object siz...
Multiple-Object Tracking (MOT) has become more popular because of its commercial and academic potential. Though various techniques were devised for managing this issue, it becomes a challenge because of factors such as severe object occlusions and ab...
Remote sensing object detection plays a major role in satellite imaging and is required in various scenarios such as transportation, forestry, and the ocean. Deep learning techniques provide efficient performance in remote sensing object detection. T...
In maritime search and rescue small object detection, existing high-accuracy detection models face deployment challenges on UAV platforms due to limited computational capabilities, while existing lightweight models often fail to meet performance requ...
Object detection in aerial images can provide great support in traffic planning, national defense reconnaissance, hydrographic surveys, infrastructure construction, and other fields. Objects in aerial images are characterized by small pixel–are...
Waste management is one of the challenges facing countries globally, leading to the need for innovative ways to design and operationalize smart waste bins for effective waste collection and management. The inability of extant waste bins to facilitate...
Rotation search and point cloud registration are two fundamental problems in robotics, geometric vision, and remote sensing, which aim to estimate the rotation and transformation between the 3D vector sets and point clouds, respectively. Due to the p...
Neural Architecture Search (NAS) is critical for developing efficient and robust perception models for UAV and drone-based applications, where real-time small object detection and computational constraints are major challenges. Existing NAS methods,...
Strong sun glint noise is an inevitable obstruction for tiny human object detection in maritime search and rescue (SAR) tasks, which can significantly deteriorate the performance of local contrast method (LCM)-based algorithms and cause high false al...
The multi-objective optimization problem is difficult to solve with conventional optimization methods and algorithms because there are conflicts among several optimization objectives and functions. Through the efforts of researchers and experts from...
This paper presents a novel, multi-objective scatter search algorithm (MOSS) for a bi-objective, dynamic, multiprocessor open-shop scheduling problem (Bi-DMOSP). The considered objectives are the minimization of the maximum completion time (makespan)...
For multimodal multi-objective optimization problems (MMOPs), there are multiple equivalent Pareto optimal solutions in the decision space that are corresponding to the same objective value. Therefore, the main tasks of multimodal multi-objective opt...
The multiple objective version of the tabu search (TS algorithm was initially developed by Baykasoglu et al [1-3]. The idea of applying tabu search to multiple objective optimization, inspired from its solution structure, in which tabu search works...
The multi-objective minimum weighted vertex cover problem aims to minimize the sum of different single type weights simultaneously. In this paper, we focus on the bi-objective minimum weighted vertex cover and propose a multi-objective algorithm inte...
Dynamic Multi-Objective Optimization Problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization field that have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid...
In this work, a new version of the Harmony Search algorithm for solving multi-objective optimization problems is proposed, MOHSg, with pitch adjustment using genotype. The main contribution consists of adjusting the pitch using the crowding distance...
In this study, a new meta-heuristic optimization method inspired by the behavioral choices of animals and hunger-driven activities, called hunger games search (HGS), is suggested to solve and formulate the single- and multi-objective optimal power fl...
Enterprise investment decision-making should not only consider investment profits, but also investment risks, which is a complex nonlinear multi-objective optimization problem. However, traditional investment decisions often only consider profit as a...
The optimization of seismic isolation parameters is essential for balancing displacement demand and acceleration control in base-isolated structures. While numerous studies have applied metaheuristic algorithms to isolator tuning, the influence of ob...
Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously....
The success of any software system highly depends on the quality of architectural design. It has been observed that over time, the quality of software architectural design gets degraded. The software system with poor architecture design is difficult...
Deep neural networks have played a crucial role in the field of deep learning, achieving significant success in practical applications. The architecture of neural networks is key to their performance. In the past few years, these architectures have b...
Aiming at the low recognition effect of apple images captured in a natural scene, and the problem that the OTSU algorithm has a single threshold, lack of adaptability, easily caused noise interference, and over-segmentation, an apple image recognitio...
In recent years, multi-objective cuckoo search (MOCS) has been widely used to settle the multi-objective (MOP) optimization issue. However, some drawbacks still exist that hinder the further development of the MOCS, such as lower convergence accuracy...
We propose a method to accelerate evolutionary multi-objective optimization (EMO) search using an estimated convergence point. Pareto improvement from the last generation to the current generation supports information of promising Pareto solution are...
Target search for moving and invisible objects has always been considered a challenge, as the floating objects drift with the flows. This study focuses on target search by multiple autonomous underwater vehicles (AUV) and investigates a multi-agent t...
Many recent studies have shown that various multi-objective evolutionary algorithms have been widely applied in the field of search-based software engineering (SBSE) for optimal solutions. Most of them either focused on solving newly re-formulated pr...
Unsupervised learning is a type of machine learning that learns from data without human supervision. Unsupervised feature selection (UFS) is crucial in data analytics, which plays a vital role in enhancing the quality of results and reducing computat...
In recent years, air emergency rescue capabilities have become increasingly important as an indicator of national comprehensive strength and development status. Air emergency rescue performs an indispensable role in addressing social emergencies by v...
To address the dual challenges of reducing carbon emissions and operating costs in municipal solid waste collection, this paper investigates the vehicle routing problem (VRP) for municipal solid waste collection and transportation, which significantl...
The optimization of guide vane closing schemes (OGVCS) of pumped storage hydro units (PSHUs) is a cooperative control and optimal operation research field in renewable energy power generation technology. This paper presents an OGVCS model of PSHUs co...
This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla, which is a rural tourist...
To solve the time-consuming, laborious, and inefficient problems of traditional methods using classical optimization algorithms combined with electromagnetic simulation software to design antennas, an efficient design method of the multi-objective an...
In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system....
Influence maximization is a key topic of study in social network analysis. It refers to selecting a set of seed users from a social network and maximizing the number of users expected to be affected. Many related research works on the classical influ...
Learning environments ensure successful implementation of the learning process but not always the effective design of the e-learning objects (ELOs) and moreover, search and adaptation. A technological solution for the design of the learning objects,...
This paper presents a novel approach to address the challenges of complex terrain, dynamic wind fields, and multi-objective constraints in multi-UAV collaborative path planning for forest firefighting missions. An extensible algorithm, termed Paralle...
Adaptive weight-vector adjustment has been explored to compensate for the weakness of the evolutionary many-objective algorithms based on decomposition in solving problems with irregular Pareto-optimal fronts. One essential issue is that the distribu...
In recent years, neural architecture search (NAS) has been proposed for automatically designing neural network architectures, which searches for network architectures that outperform novel human-designed convolutional neural network (CNN) architectur...
In intensity-modulated radiation therapy, treatment planners aim to irradiate the tumour according to a medical prescription while sparing surrounding organs at risk as much as possible. Although this problem is inherently a multi-objective optimisat...
To address real-world engineering design optimization problems, this study proposes a reinforcement learning enhanced multi-objective social network search algorithm (QMOSNS), which represents a novel approach for solving multi-objective optimization...
The optimal scheduling of DES is to solve a multi-objective optimization problem (MOP) with complex constraints. However, the potential contradiction between multiple optimization objectives leads to the diversity of feasible solutions, which has a s...
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