Comparative Study of Nature-Inspired Algorithms

• Published in: Re-Imagining Diffusion and Adoption of Information Technology and Systems: a Continuing Conversation Vol. 617; pp. 353 - 361
• Main Authors: Tahir, Mohammad Abdullah, Khan, Hasan Faraz, Khan, Mohammad Mohsin
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2020; Springer International Publishing
• Series: IFIP Advances in Information and Communication Technology
• Subjects: Deterministic algorithms; Memory based algorithms; Memory-less algorithms; Nature-inspired algorithms; Stochastic algorithms
• DOI: 10.1007/978-3-030-64849-7_32
• ISBN: 3030648486; 9783030648480
• ISSN: 1868-4238

Nature-inspired algorithms are problem-solving tactics and methodologies and have been gaining much recognition for their competence. These algorithms have gained massive acclaim in the last few years to solve puzzling real-world (Nondeterministic polynomial-hard and Nondeterministic polynomial-complete) problems and solve complex optimization problems and functions whose exact solution doesn’t exist. These are the algorithms that are inspired by natural processes and phenomena. The nature-inspired algorithm can be categorized based on some biological processes or any other phenomena which happen in nature. So, in this paper, we have classified some popular nature-inspired algorithms such as Genetic algorithm, Simulated Annealing, harmony search, Black hole, and many more. Based on four parameters from which the first parameter is Subject from which they were inspired. The second parameter is the optimization technique i.e. Stochastic or Deterministic. The third parameter of our classification is the number of solutions they maintain i.e. population-based or trajectory-based. And the last parameter of this categorization is memory i.e. algorithm is memory-based or memory less.