Nature-Inspired Algorithms
A. Vinotha Vasuki · 2020
Nature-inspired optimization algorithms are bioinspired computational intelligence techniques since they incorporate intelligence in the algorithms. The nature-inspired algorithms are novel in attaining effective solutions easily with the least computational resources. The majority of nature-inspired algorithms are broadly classified under evolutionary algorithms and swarm intelligence algorithms. Nature-inspired algorithms generate solutions that are close to the optimum in a finite reasonable amount of time, as opposed to traditional algorithms that are intractable for NP-hard problems. The evolution process in nature has been taking place for millions of years, and new ingenious solutions have been invented in the ever-changing environment. The performance of nature-inspired optimization algorithms depends on the setting of the parameters associated with the problem. Choosing the appropriate values for the parameters initially and maintaining them throughout the run of the algorithm is parameter tuning. Nature-inspired algorithms are flexible, adaptive, self-organized, and population-based with simple interactions among individuals and efficient computations.