Nature-inspired optimization algorithms: Challenges and open problems

• Published in: Journal of computational science Vol. 46; p. 101104
• Main Author: Yang, Xin-She
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2020
• Subjects: Algorithm; Bat algorithm; Convergence; Cuckoo search; Differential evolution; Firefly algorithm; Flower pollination algorithm; Metaheuristic; Nature-inspired computation; Optimization; Particle swarm optimization; Stability; Swarm intelligence; Cuckoo search; Stability; Flower pollination algorithm; Algorithm; Particle swarm optimization; Optimization; Convergence; Nature-inspired computation; Metaheuristic; Bat algorithm; Firefly algorithm; Differential evolution; Swarm intelligence
• ISSN: 1877-7503; 1877-7511
• DOI: 10.1016/j.jocs.2020.101104

•An in-depth review of nature-inspired algorithms for optimization.•Analyze different algorithms from different perspectives.•Highlight challenging issues and open problems. 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.