.png){kind=icon}
.png){kind=icon}
Return
Nature-inspired optimization algorithms: Challenges and open problems
.png){kind=icon}
OA
AI
DOI：10.1016/j.jocs.2020.101104.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there are some key issues concerning nature-inspired computation and swarm intelligence. This paper provides an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations. Some challenging issues are identified and five open problems are highlighted, concerning the analysis of algorithmic convergence and stability, parameter tuning, mathematical framework, role of benchmarking and scalability. These problems are discussed with the directions for future research. (c) 2020 Elsevier B.V. All rights reserved.
Keywords:
Algorithm
Bat algorithm
Convergence
Cuckoo search
Differential evolution
Firefly algorithm
Flower pollination algorithm
Metaheuristic
Nature-inspired computation
Optimization
Particle swarm optimization
Stability
Swarm intelligence
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
18.3
Papers:
3.1K
Citations:
4.0K