Nature-Inspired Optimization Algorithms: Challenges and Open Problems
It addresses methodological issues in optimization for researchers and practitioners, but is incremental as it focuses on reviewing and identifying problems rather than proposing new solutions.
This paper reviews recent nature-inspired optimization algorithms, identifying key challenges and highlighting five open problems related to convergence, stability, parameter tuning, mathematical frameworks, benchmarking, and scalability.
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.