There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which mak...
In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checki...
A novel orthogonal multi-swarm cooperative particle swarm optimization (PSO) algorithm with a particle trajectory knowledge base is presented in this paper. Different from the traditional PSO algorithms and other variants of PSO, the proposed orthogo...
As a powerful tool in optimization, particle swarm optimizers have been widely applied to many different optimization areas and drawn much attention. However, for large-scale optimization problems, the algorithms exhibit poor ability to pursue satisf...
The complexity of unmanned aerial vehicle (UAV) missions is increasing with the rapid development of UAV technology. Multiple UAVs usually cooperate in the form of teams to improve the efficiency of mission execution. The UAVs are equipped with multi...
Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot o...
With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel predict...
Renewable energy communities have gained popularity as a means of reducing carbon emissions and enhancing energy independence. However, determining the optimal sizing for each production and storage unit within these communities poses challenges due...
Facing energy shortage and severe environmental pollution, manufacturing companies need to urgently energy consumption, make rational use of resources and improve economic benefits. This paper formulates a multi-objective optimization model for lathe...
At present, the common practice in the power system of China is to represent the small and medium hydropower generator group as a negative load. This paper presents a method to build a dynamic equivalent model of the hydropower generator group using...
The path planning of unmanned aerial vehicles (UAVs) in the threat and countermeasure region is a constrained nonlinear optimization problem with many static and dynamic constraints. The fruit fly optimization algorithm (FOA) is widely used to handle...
Particle swarm optimization (PSO) is a population-based heuristic algorithm that is widely used for optimization problems. Phasor PSO (PPSO), an extension of PSO, uses the phase angle
This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global...
In recent years, significant progress has been made in the multi-target tracking (MTT) of unmanned aerial vehicle (UAV) swarms. Most existing MTT approaches rely on the ideal assumption of a pre-set target trajectory. However, in practice, the trajec...
Simulation platforms are critical and indispensable tools for application developments of unmanned aerial vehicles (UAVs) because the UAVs are generally costly, have certain requirements for the test environment, and need professional licensed operat...
In sensory swarms, minimizing energy consumption under performance constraint is one of the key objectives. One possible approach to this problem is to monitor application workload that is subject to change at runtime, and to adjust system configurat...
Collaborative multi-UAV swarms are central to many missions. This review covers the most recent two years. It organizes the literature with a scenario-aligned taxonomy. The taxonomy has 12 cells (Path/Distribution/Coverage × offline/online &tim...
Unmanned aerial vehicle (UAV) swarms are widely applied in various fields, including military and civilian domains. However, due to the scarcity of spectrum resources, UAV swarm clustering technology has emerged as an effective method for achieving s...
Accurately modeling and representing the collective dynamics of large-scale robotic systems remains one of the fundamental challenges in swarm robotics. Within the context of agricultural robotics, swarm-based coordination schemes enable scalable and...
In this study, the modification of the quantum multi-swarm optimization algorithm is proposed for dynamic optimization problems. The modification implies using the search operators from differential evolution algorithm with a certain probability with...
A multi-swarm-evolutionary structure based on the parasitic relationship in the biosphere is proposed in this paper and, according to the conception, the Para-PSO-ABC algorithm (ParaPA), combined with merits of the modified particle swarm optimizatio...
This study aims to improve the accuracy of fire source detection, the efficiency of path planning, and the precision of firefighting operations in drone swarms during fire emergencies. It proposes an intelligent firefighting technology for drone swar...
Portfolio optimization is a multi-objective optimization problem (MOOP) with risk and profit, or some form of the two, as competing objectives. Single-objective portfolio optimization requires a trade-off coefficient to be specified in order to balan...
It is a challenge for the particle swarm optimization algorithm to effectively control population diversity and select and design efficient learning models. To aid in this process, in this paper, we propose multiple learning strategies and a modified...
Multi-Robot Task Assignment (MRTA) is a critical and inherently multi-objective problem in diverse real-world applications, demanding the simultaneous optimization of conflicting objectives such as minimizing total travel distance and balancing robot...
Multiswarm comprehensive learning particle swarm optimization (MSCLPSO) is a multiobjective metaheuristic recently proposed by the authors. MSCLPSO uses multiple swarms of particles and externally stores elitists that are nondominated solutions found...
With the increasing demand for intelligent custom clothing, the development of highly accurate human body dimension prediction tools using artificial neural network technology has become essential to ensuring high-quality, fashionable, and personaliz...
Swarm Intelligence (SI) represents a paradigm shift in artificial intelligence, leveraging the collective behavior of decentralized, self-organized systems to solve complex problems. This study provides a comprehensive review of SI, focusing on its a...
Aiming at the formation problem of the multi-rotor UAV swarm, this paper adopts a multi-rotor UAV swarm formation control method based on a stress matrix to ensure the stability of multi-rotor UAV swarm formation. On the basis of achieving the target...
An efficient control of large-scale unmanned aerial vehicle (UAV) swarm to establish a complex formation is one of the most challenging tasks. This paper investigates a novel multi-layer topology network and consensus control approach for a large-sca...
Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominat...
Distributed artificial intelligence is increasingly being applied to multiple unmanned aerial vehicles (multi-UAVs). This poses challenges to the distributed reconfiguration (DR) required for the optimal redeployment of multi-UAVs in the event of veh...
The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms exte...
Microgrids have been widely used due to their advantages, such as flexibility and cleanliness. This study adopts the hierarchical control method for microgrids containing multiple energy sources, i.e., photovoltaic (PV), wind, diesel, and storage, an...
The goal of this study is to explore the effectiveness of applying multi-objective particle swarm optimization (MOPSO) algorithms in the design of infinite impulse response (IIR) filters. Given the widespread application of IIR filters in digital sig...
It is generally known that the balance between convergence and diversity is a key issue for solving multi-objective optimization problems. Thus, a chaotic multi-objective particle swarm optimization approach incorporating clone immunity (CICMOPSO) is...
The present work aims to optimize the magnetomotive force and the end-winding leakage inductance from a discrete distribution of conductors in electrical machines through multi-objective particle swarm heuristics. From the development of an applicati...
The rational allocation of tasks is a critical issue in enhancing the mission execution capability of unmanned aerial vehicle (UAV) swarms, which is difficult to solve exactly in polynomial time. Evolutionary-algorithm-based approaches are among the...
Research on unmanned aerial vehicle (UAV) path planning technology in urban operation scenarios faces the challenge of multi-objective collaborative optimization. Currently, mainstream path planning algorithms, including the multi-objective particle...
An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used...
The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has shown remarkable effectiveness in solving multi-objective problems (MOPs). In this paper, we integrate the quantum-behaved particle swarm optimization (QPSO) algorithm with t...
In order to improve the dynamic performance during the startup process of hydropower units, while considering the efficient and stable speed increase and effective suppression of water pressure fluctuations and mechanical vibrations, optimization alg...
Solar energy has many advantages, such as being abundant, clean and environmentally friendly. Solar power generation has been widely deployed worldwide as an important form of renewable energy. The solar thermal power generation is one of a few popul...
Particle swarm optimization (PSO) is one of the most popular, nature inspired optimization algorithms. The canonical PSO is easy to implement and converges fast, however, it suffers from premature convergence. The comprehensive learning particle swar...
Various aspects of task execution load balancing of Internet of Things (IoTs) networks can be optimised using intelligent algorithms provided by software-defined networking (SDN). These load balancing aspects include makespan, energy consumption, and...
The unmanned aerial vehicle (UAV) swarm is regarded as having a significant role in modern warfare. The demand for UAV swarms with the capability of attack-defense confrontation is urgent. The existing decision-making methods of UAV swarm confrontati...
The localization of sensor nodes is an important problem in wireless sensor networks. The DV-Hop algorithm is a typical range-free algorithm, but the localization accuracy is not high. To further improve the localization accuracy, this paper designs...
Protein structure refinement is a crucial step for more accurate protein structure predictions. Most existing approaches treat it as an energy minimization problem to intuitively improve the quality of initial models by searching for structures with...
Uncrewed Aerial Vehicles (UAVs) are increasingly deployed across various domains due to their versatility in navigating three-dimensional spaces. The utilization of UAV swarms further enhances the efficiency of mission execution through collaborative...
The problem of safe navigation of a human-multi-robot system is addressed in this paper. More precisely, we propose a novel distributed algorithm to control a swarm of unmanned ground robots interacting with human operators in presence of obstacles....
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