A novel hybrid particle swarm optimization for multi-UAV cooperate path planning

W He, X Qi, L Liu - Applied Intelligence, 2021 - Springer
The path planning of unmanned aerial vehicle (UAV) in three-dimensional (3D) environment
is an important part of the entire UAV's autonomous control system. In the constrained …

[HTML][HTML] Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications

HN Ghafil, K Jármai - Applied Soft Computing, 2020 - Elsevier
This work proposes a novel optimization algorithm which can be used to solve a wide range
of mathematical optimization problems where the global minimum or maximum is required …

Mathematical model and simulated annealing algorithm for setup operator constrained flexible job shop scheduling problem

FM Defersha, D Obimuyiwa, AD Yimer - Computers & Industrial …, 2022 - Elsevier
In the vast majority of the published article on flexible job shop scheduling problems (FJSP),
machines are the only resources with limited capacities. There are also a sizable number of …

A modified self-adaptive marine predators algorithm: framework and engineering applications

Q Fan, H Huang, Q Chen, L Yao, K Yang… - Engineering with …, 2022 - Springer
The application of metaheuristic algorithms is one of the most promising approaches for
solving real-world problems. The marine predators algorithm (MPA) is a recently proposed …

A modified equilibrium optimizer using opposition-based learning and novel update rules

Q Fan, H Huang, K Yang, S Zhang, L Yao… - Expert Systems with …, 2021 - Elsevier
Equilibrium Optimizer (EO) is a newly developed physics-based metaheuristic algorithm that
is based on control volume mass balance models, and has shown competitive performance …

A feature-thresholds guided genetic algorithm based on a multi-objective feature scoring method for high-dimensional feature selection

S Deng, Y Li, J Wang, R Cao, M Li - Applied Soft Computing, 2023 - Elsevier
The classical genetic algorithm utilizes random population initialization, an unguided
crossover operator, and an unguided mutation operator for feature selection. However, this …

Comparative study on single and multiple chaotic maps incorporated grey wolf optimization algorithms

Z Xu, H Yang, J Li, X Zhang, B Lu, S Gao - IEEE Access, 2021 - ieeexplore.ieee.org
As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf
optimizer (GWO) has a wide range of applications in practical problems. As a kind of local …

Stacked autoencoder-based deep reinforcement learning for online resource scheduling in large-scale MEC networks

F Jiang, K Wang, L Dong, C Pan… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
An online resource scheduling framework is proposed for minimizing the sum of weighted
task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision …

Homonuclear molecules optimization (HMO) meta-heuristic algorithm

A Mahdavi-Meymand, M Zounemat-Kermani - Knowledge-Based Systems, 2022 - Elsevier
This study introduces a novel meta-heuristic algorithm known as Homonuclear Molecules
Optimization (HMO) for optimizing complex and nonlinear problems. HMO is inspired by the …

[HTML][HTML] A new hyper-heuristic based on adaptive simulated annealing and reinforcement learning for the capacitated electric vehicle routing problem

E Rodríguez-Esparza, AD Masegosa, D Oliva… - Expert Systems with …, 2024 - Elsevier
Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution
and global warming due to the increasing number of freight vehicles. However, there are still …