Modeling solar systems necessitates the effective identification of unknown and variable photovoltaic parameters. To efficiently convert solar energy into electricity, these parameters must be precise. The research introduces the multi-strategy learn...
In response to the issues of premature convergence and insufficient parameter control in Particle Swarm Optimization (PSO) for high-dimensional complex optimization problems, this paper proposes a Multi-Strategy Topological Particle Swarm Optimizatio...
In this paper, a multi-strategy adaptive comprehensive learning particle swarm optimization algorithm is proposed by introducing the comprehensive learning, multi-population parallel, and parameter adaptation. In the proposed algorithm, a multi-popul...
Medical image registration integrates information from different types of medical images to support and improve clinical diagnosis. Existing image registration approaches are mainly classified into two categories: model-driven methods and data driven...
Owing to the rapid increase in the amount of legal text data and the increasing demand for intelligent processing, multi-label legal text recognition is becoming increasingly important in practical applications such as legal information retrieval and...
Precision irrigation is crucial for sustainable agriculture, yet conventional single-objective optimization methods struggle to balance conflicting demands such as crop yield, operational cost, and environmental sustainability. This study introduces...
The prevalent utilization of deterministic strategy algorithms in Multi-Agent Deep Reinforcement Learning (MADRL) for collaborative tasks has posed a significant challenge in achieving stable and high-performance cooperative behavior. Addressing the...
Obtaining high convergence and uniform distributions remains a major challenge in most metaheuristic multi-objective optimization problems. In this article, a novel multi-objective particle swarm optimization (PSO) algorithm is proposed based on Gaus...
The merging behavior of vehicles at entry ramps and the speed differences between ramps and mainline traffic cause merging traffic bottlenecks. Current research, primarily focusing on single traffic control strategies, fails to achieve the desired ou...
The multi-valued dependency matrix (MVD matrix) is an important testability modeling approach, which can deliver more comprehensive testability information than the traditional dependency matrix (D-matrix). However, existing testability strategy opti...
Three-dimensional UAV path planning is crucial in practical applications. However, existing metaheuristic algorithms often suffer from slow convergence and susceptibility to becoming trapped in local optima. To address these limitations, this paper p...
In the realm of precision agriculture, agricultural UAV path planning is challenging because the UAV must avoid obstacles, follow uneven terrain, and satisfy multiple flight constraints simultaneously. Differential evolution (DE) has been widely adop...
Aiming at the problem of easy-to-fall-into local convergence for automatic train operation (ATO) velocity ideal trajectory profile optimization algorithms, an improved multi-objective hybrid optimization algorithm using a comprehensive learning strat...
Household loads are becoming dominant in virtual power plants (VPP). However, their dispatch potential has not yet been explored due to the lack of detailed user power management. To solve this issue, a novel two-layer user energy management strategy...
Harris Hawks Optimization (HHO) simulates the cooperative hunting behavior of Harris hawks and it has the advantages of fewer control parameters, simple principles, and excellent exploitation ability. However, HHO also has the disadvantages of slow c...
Coverage control is a fundamental and critical issue in plentiful wireless sensor network (WSN) applications. Aiming at the high-dimensional optimization problem of sensor node deployment and the complexity of the monitoring area, an orthogonal learn...
To address the problems of the single evolutionary approach, decreasing diversity, inhomogeneity, and meaningfulness in the destruction process when the teaching–learning-based optimization (TLBO) algorithm solves the no-wait flow-shop-scheduli...
Unmanned aerial vehicles (UAVs) have been widely used in surveying and mapping, inspection, emergency rescue, and environmental monitoring. However, effective path planning remains a key challenge in complex three-dimensional terrain, where UAVs must...
This paper presents a Multi-Strategy Market Dynamics Analysis (MSMDA) framework for agent-based economic modeling with reinforcement learning. The primary methodological contribution is an integrated strategy–stability–macro inference pip...
Low-altitude terrain-following flight is essential for obtaining high-quality data in unmanned aerial vehicle (UAV) aeromagnetic surveys, but achieving efficient and safe path planning within complex terrains remains challenging. To address this issu...
To address the limitations of the traditional Teaching–Learning-Based Optimization (TLBO) algorithm when solving high-dimensional, multimodal, strongly nonlinear, and constrained global optimization problems—such as single search directio...
Natural gas is one of the most important sources of energy in modern society. However, its strong volatility highlights the importance of accurately forecasting natural gas price trends and movements. The nonlinear nature of the natural gas price ser...
To solve the problems of the original sparrow search algorithm’s poor ability to jump out of local extremes and its insufficient ability to achieve global optimization, this paper simulates the different learning forms of students in each ranki...
Feature selection is a multi-objective problem, which can eliminate irrelevant and redundant features and improve the accuracy of classification at the same time. Feature selection is a great challenge to balance the conflict between the two goals of...
Large background noise, difficulty in feature extraction, and low parameter-optimization efficiency of diagnosis models are key challenges in rolling bearing fault diagnosis. To address these issues, this paper proposes a fault diagnosis framework th...
Particle swarm optimization (PSO) is a simple and efficient metaheuristic algorithm that has been widely applied to solving various practical problems. However, PSO has some inherent limitations, such as a tendency to get trapped in local optima and...
To address the limitations of the standard Escape Algorithm (ESC), including its tendency to become trapped in local optima and its slow convergence speed in complex optimization scenarios, this paper proposes an Improved Escape Algorithm (IESC) for...
This paper proposes a bionic social learning strategy pigeon-inspired optimization (BSLSPIO) algorithm to tackle cooperative path planning for multiple unmanned aerial vehicles (UAVs) with cooperative detection. Firstly, a modified pigeon-inspired op...
The global development and enhancement of electricity financial markets aim to mitigate price risk in the electricity spot market. Power producers utilize financial derivatives for both hedging and speculation, necessitating careful selection of port...
With the development of unmanned combat air vehicles (UCAVs) and artificial intelligence (AI), within visual range (WVR) air combat confrontations utilizing intelligent UCAVs are expected to be widely used in future air combats. As controlling highly...
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in environmental monitoring, logistics, and precision agriculture. Efficient and reliable path planning is particularly critical for UAV systems operating in 3D continuous environments with mu...
This paper investigates the formation control problem of multi-unmanned aerial vehicle (UAV) systems with multi-operating modes. While mode switching enhances the flexibility of multi-UAV systems, it also introduces dynamic model switching behaviors...
Portfolio management (PM) is a broad investment strategy aimed at risk mitigation through diversified financial product investments. Acknowledging the significance of dynamic adjustments after establishing a portfolio to enhance stability and returns...
The multi-microgrid (MMG) system has attracted more and more attention due to its low carbon emissions and flexibility. This paper proposes a multi-agent reinforcement learning algorithm for real-time energy management of an MMG. In this problem, the...
Reinforcement learning has made significant progress in single-agent applications, but it still faces various challenges in multi-agent scenarios. This study investigates the application of reinforcement learning algorithms in a competitive game scen...
As one of the promising paradigms of decentralized machine learning, multi-party learning has attracted increasing attention, owing to its capability of preventing the privacy of participants from being directly exposed to adversaries. Multi-party le...
To improve the performance of the sparrow search algorithm in solving complex optimization problems, this study proposes a novel variant called the Improved Beetle Antennae Search-Based Sparrow Search Algorithm (IBSSA). A new elite dynamic opposite l...
Multi-access edge computing (MEC) has been widely recognized as a promising solution for alleviating the computational burden on edge devices, particularly in supporting fast and real-time processing of resource-intensive applications. In this paper,...
In addressing the optimal motion planning issue for multi-arm rock drilling robots, this paper introduces a high-precision motion planning method based on Multi-Strategy Sampling RRT* (MSS-RRT*). A dual Jacobi iterative inverse solution method, coupl...
The optimization of clutch engagement strategies is of great significance for improving vehicle power performance, fuel economy, and driving comfort. Traditional control strategies are difficult to adapt to complex working conditions and lack coordin...
Temporal grounding involves identifying the target moment based on the provided sentence in an untrimmed video. In weakly supervised temporal grounding studies, existing temporal sentence grounding methods face challenges in (1) learning semantic ali...
The Pathfinder Algorithm (PFA) is a bionic swarm intelligence optimization algorithm inspired by simulating the cooperative movement of animal groups in nature to search for prey. Based on fitness, the algorithm classifies search individuals into lea...
Mobile edge computing is a new computing model, which pushes cloud computing power from centralized cloud to network edge. However, with the sinking of computing power, user mobility brings new challenges: since it is usually unstable, services shoul...
Based on the principles of biomimicry, evolutionary algorithms (EAs) have been widely applied across diverse domains to tackle practical challenges. However, the inherent limitations of these algorithms call for further refinement to strike a delicat...
Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL) leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what ac...
With the boom in mobile internet services, computationally intensive applications such as virtual and augmented reality have emerged. Mobile edge computing (MEC) technology allows mobile devices to offload heavy computational tasks to edge servers, w...
Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design....
With the development of wireless sensor network technology, the routing strategy has important significance in the Internet of Things. An efficient routing strategy is one of the fundamental technologies to ensure the correct and fast transmission of...
In this paper, we consider the cooperative decision-making problem for multi-target tracking in multi-agent systems using multi-agent deep reinforcement learning algorithms. Multi-agent multi-target pursuit has faced new challenges in practical appli...
With the rapid development of financial research theory and artificial intelligence technology, quantitative investment has gradually entered people’s attention. Compared with traditional investment, the advantage of quantitative investment lie...
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