Nature-Inspired Optimization Algorithms: Challenges and Open Problems
arXiv:2003.03776 · doi:10.1016/j.jocs.2020.101104
Abstract
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.
15 pages
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- Review of Parameter Tuning Methods for Nature-Inspired Algorithms
- A Generalized Evolutionary Metaheuristic (GEM) Algorithm for Engineering Optimization
- Nature-Inspired Algorithms in Optimization: Introduction, Hybridization and Insights
- Noise-induced network topologies
- Inverse Design of Non-Equilibrium Steady-States: A Large Deviation Approach
- Ten New Benchmarks for Optimization
- A 2020 taxonomy of algorithms inspired on living beings behavior
- Building Stable Off-chain Payment Networks
- Analysis of Parameter Settings for the Bat Algorithm Using Variance Evolution