An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges
As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
Autonomous driving system: A comprehensive survey
Automation is increasingly at the forefront of transportation research, with the potential to
bring fully autonomous vehicles to our roads in the coming years. This comprehensive …
bring fully autonomous vehicles to our roads in the coming years. This comprehensive …
[HTML][HTML] Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms
Metaheuristics are popularly used in various fields, and they have attracted much attention
in the scientific and industrial communities. In recent years, the number of new metaheuristic …
in the scientific and industrial communities. In recent years, the number of new metaheuristic …
Coverage path planning of heterogeneous unmanned aerial vehicles based on ant colony system
J Chen, F Ling, Y Zhang, T You, Y Liu, X Du - Swarm and Evolutionary …, 2022 - Elsevier
Unmanned aerial vehicle (UAV) has been extensively studied and widely adopted in
practical systems owing to its effectiveness and flexibility. Although heterogeneous UAVs …
practical systems owing to its effectiveness and flexibility. Although heterogeneous UAVs …
The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study
Optimization algorithms are used to improve model accuracy. The optimization process
undergoes multiple cycles until convergence. A variety of optimization strategies have been …
undergoes multiple cycles until convergence. A variety of optimization strategies have been …
A benchmark-suite of real-world constrained multi-objective optimization problems and some baseline results
Abstract Generally, Synthetic Benchmark Problems (SBPs) are utilized to assess the
performance of metaheuristics. However, these SBPs may include various unrealistic …
performance of metaheuristics. However, these SBPs may include various unrealistic …
Global-and-local attention-based reinforcement learning for cooperative behaviour control of multiple UAVs
Due to the strong adaptability and high flexibility, unmanned aerial vehicles (UAVs) have
been extensively studied and widely applied in both civil and military applications. Although …
been extensively studied and widely applied in both civil and military applications. Although …
A critical problem in benchmarking and analysis of evolutionary computation methods
J Kudela - Nature Machine Intelligence, 2022 - nature.com
Benchmarking is a cornerstone in the analysis and development of computational methods,
especially in the field of evolutionary computation, where theoretical analysis of the …
especially in the field of evolutionary computation, where theoretical analysis of the …
An improved tunicate swarm algorithm with best-random mutation strategy for global optimization problems
FS Gharehchopogh - Journal of Bionic Engineering, 2022 - Springer
Abstract The Tunicate Swarm Algorithm (TSA) inspires by simulating the lives of Tunicates at
sea and how food is obtained. This algorithm is easily entrapped to local optimization …
sea and how food is obtained. This algorithm is easily entrapped to local optimization …
[HTML][HTML] A prescription of methodological guidelines for comparing bio-inspired optimization algorithms
Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a
growing research topic with many competitive bio-inspired algorithms being proposed every …
growing research topic with many competitive bio-inspired algorithms being proposed every …