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...
The efficiency of heuristic search algorithms is a critical factor in the realm of cryptographic primitive construction, particularly in the generation of highly nonlinear bijective permutations, known as substitution boxes (S-boxes). The vast search...
Dynamic lotsizing and scheduling on multiple lines to meet the customer due dates is significant in multi-line production environments. Therefore, this study investigates dynamic lotsizing and scheduling problems in multiple flexible machining lines...
A data-driven nonlinear control approach, called error dynamics-based dual heuristic dynamic programming (ED-DHP), is proposed for air vehicle attitude control. To solve the optimal tracking control problem, the augmented system is defined by the der...
This paper investigates an adaptive dynamic programming (ADP)-based energy management control strategy for a series-parallel hybrid electric vehicle (HEV). This strategy can further minimize the equivalent fuel consumption while satisfying the batter...
The growing affordability, autonomy, and swarming of small unmanned aerial vehicles (UAVs) turn low-altitude defense from single-shot interception into a multi-node cooperative decision problem, in which the loss of sensing, coordination, or engageme...
Task scheduling in data relay satellite networks (DRSNs) is subject to dynamic disruptions such as resource failures, sudden surges in task demands, and variations in service duration requirements. These disturbances may degrade the performance of pr...
We propose in this paper two heuristic strategies to solve the regenerator placement problem (RPP) in translucent networks under dynamic traffic. The heuristics are based on both the forecast of the offered load and estimates of blocking probabilitie...
The development of the computing power network has brought about a revolutionary effect on network routing architecture. As a result, the computing-aware network routing problem has been raised to explore routing various computational tasks to approp...
In this paper, we propose a dynamic displacement estimation method for large-scale civil infrastructures based on a two-stage Kalman filter and modified heuristic drift reduction method. When measuring displacement at large-scale infrastructures, a n...
The internet of things (IoT) concept has been extremely investigated in many modern smart applications, which enable a set of sensors to either process the collected data locally or send them to the cloud for remote processing. Unfortunately, cloud d...
Many real-life combinatorial optimization problems are subject to a high degree of dynamism, while, simultaneously, a certain level of synchronization among agents and events is required. Thus, for instance, in ride-sharing operations, the arrival of...
In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure a...
Aiming at the accurate prediction of the inception of instability in a compressor, a dynamic system stability model is proposed based on a sparrow-inspired meta-heuristic optimization algorithm in this article. To achieve this goal, a spatial mode is...
Dyna-Q is a reinforcement learning method widely used in AGV path planning. However, in large complex dynamic environments, due to the sparse reward function of Dyna-Q and the large searching space, this method has the problems of low search efficien...
Network slicing has become an unavoidable requirement for allocating 5G mobile network resources when sharing resources among devices that have varying needs. As a result, the virtual network slices get resources from the shared physical infrastructu...
As a well-known NP-hard problem, the dynamic job shop scheduling problem has significant practical value, so this paper proposes an Improved Heuristic Kalman Algorithm to solve this problem. In Improved Heuristic Kalman Algorithm, the cellular neighb...
In the event of a disaster, the road network is often compromised in terms of its capacity and usability conditions. This is a challenge for humanitarian operations in the context of delivering critical medical supplies. To optimise vehicle routing f...
The accurate identification of water bodies in hyperspectral images (HSIs) remains challenging due to hierarchical representation imbalances in deep learning models, where shallow layers overly focus on spectral features, boundary ambiguities caused...
Many real-life applications of the vehicle routing problem (VRP) occur in scenarios subject to uncertainty or dynamic conditions. Thus, for instance, traveling times or customers’ demands might be better modeled as random variables than as dete...
This study integrates a Radial Basis Function Neural Network (RBFNN) with Adaptive Dynamic Programming (ADP) to develop an Action-Dependent Heuristic Dynamic Programming (ADHDP) framework for the cost-effective control of an Ice Storage Air-Condition...
The weapon-target assignment problem is a crucial decision support in a Command and Control system. As a typical operational scenario, the major asset-based dynamic weapon target assignment (A-DWTA) models and solving algorithms are challenging to re...
In this paper, the theoretical stability of batch offline action-dependent heuristic dynamic programming (BOADHDP) is analyzed for deep neural network (NN) approximators for both the action value function and controller which are iteratively improved...
Personal Identification Numbers (PIN) and unlock patterns are two of the most often used smartphone authentication mechanisms. Because PINs have just four or six characters, they are subject to shoulder-surfing attacks and are not as secure as other...
The existing variants of the rapidly exploring random tree (RRT) cannot be effectively applied in local path planning of the autonomous vehicle and solve the coherence problem of paths between the front and back frames. Thus, an improved heuristic Bi...
A speed controller for permanent magnet synchronous motors (PMSMs) under the field oriented control (FOC) method is discussed in this paper. First, a novel adaptive neuro-control approach, single artificial neuron goal representation heuristic dynami...
This paper provides an initial introduction to, and definition of, the ‘Dynamically Powered Relays for a Flexible Optical Network’ (DPR-FON) problem for opto-electro-optical (OEO) regenerators used in optical networks. In such networks, optical trans...
In this paper, the generalized quadratic assignment problem (GQAP) is extended to consider multiple time periods and is called the dynamic GQAP (DGQAP). This problem considers assigning a set of facilities to a set of locations for multiple periods i...
Object tracking and detection is one of the most significant research areas for wireless sensor networks. Existing indoor trajectory tracking schemes in wireless sensor networks are based on continuous localization and moving object data mining. Indo...
Accurate and adaptive bus dispatching is vital for medium-sized urban centers, where static schedules often fail to accommodate fluctuating passenger demand. In this work, we propose a dynamic heuristic that integrates machine learning-based demand f...
Efficient task–channel allocation in satellite communication networks becomes particularly challenging when tasks are subject to both time and frequency constraints, and when resource failures or environmental changes invalidate an initially fe...
This paper develops a novel dynamic three-phase symmetric distribution network reconfiguration (DNR) approach based on hierarchical clustering with timing constraints, which can divide the time period according to the time-varying symmetric load dema...
Many real-world applications of the vehicle routing problem (VRP) are arising today, which range from physical resource planning to virtual resource management in the cloud computing domain. A common trait of these applications is usually the large s...
A reinforcement learning hyper-heuristic framework for multi-objective scientific workflow scheduling selects among five meta-heuristic optimisers and tunes their control parameters under a Nash Social Welfare reward over makespan, cost, security, an...
Advanced Metering Infrastructure (AMI) over 5G New Radio (NR) massive machine-type communication (mMTC) networks require efficient and adaptive communication mechanisms to support reliable data delivery for large numbers of smart meters under dynamic...
In this study, the discrete and dynamic problem of berth allocation in maritime terminals, is investigated. The suggested resolution method relies on a paradigm of optimization with two techniques: heuristic and multi-agent. Indeed, a set of techniqu...
This study is concerned with developing a robust tracking control system that merges the optimal control theory with fractional-order-based control and the heuristic optimization algorithms into a single framework for the non-minimum phase pitch angl...
This article proposes the Collective Delivery Demand Matrix (CDD Matrix), which repositions the New Agile as a correspondence property between collective delivery demands and teams’ operating regimes, aiming to form teams that are simultaneousl...
The decoupling of physical loading configurations from dynamic temperature control in cold-chain logistics exposes supply chains to severe thermal compliance risks and exponential cost penalties. To address this structural gap, this study formulated...
The detection of damage in bridges subjected to moving loads has attracted increasing attention in the field of structural health monitoring. Processing the dynamic responses induced by moving loads to characterize damage is the key to identifying da...
The coordination of heterogeneous Unmanned Aerial Vehicles (UAVs) for complex, multi-stage tasks presents a significant challenge in robotics and autonomous systems. Traditional linear models often fail to capture the emergent synergistic effects and...
The Knapsack Problem belongs to the best-studied classical problems in combinatorial optimization. The Multiple-choice Knapsack Problem (MCKP) represents a generalization of the problem, with various application fields such as industry, transportatio...
An optimal pavement maintenance strategy can keep the pavement performance at a high level under budget constraint. However, the impact of changes in traffic distribution caused by maintenance actions on user costs is rarely investigated in existing...
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...
Uncertainty in processing times is a key issue in distributed production; it severely affects scheduling accuracy. In this study, we investigate a dynamic distributed flexible job shop scheduling problem with variable processing times (DDFJSP-VPT), i...
This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to th...
The efficient operation and intelligent upgrading of public transportation can effectively enhance the attractiveness of conventional public transportation. In order to improve the delicacy management level of bus operations, this study designed a ne...
Evolutionary Population Dynamics (EPD) refers to eliminating poor individuals in nature, which is the opposite of survival of the fittest. Although this method can improve the median of the whole population of the meta-heuristic algorithms, it suffer...
The traditional mantis search algorithm (MSA) suffers from limitations such as slow convergence and a high likelihood of converging to local optima in complex optimization scenarios. This paper proposes an improved mantis search algorithm (IMSA) to o...
Smart grids face significant challenges in coordinating demand-side management (DSM), dynamic pricing, data aggregation, and network feasibility in real time. To address this, we propose H-EMOS-Lite, an adaptive, multi-layer heuristic framework that...
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