Nature inspired meta heuristic algorithms for optimization problems

Abstract

Optimization and decision making problems in various fields of engineering have a major impact in this current era. Processing time and utilizing memory is very high for the currently available data. This is due to its size and the need for scaling from zettabyte to yottabyte. Some problems need to find solutions and there are other types of issues that need to improve their current best solution. Modelling and implementing a new heuristic algorithm may be time consuming but has some strong primary motivation - like a minimal improvement in the solution itself can reduce the computational cost. The solution thus obtained was better. In both these situations, designing heuristics and meta-heuristics algorithm has proved it’s worth. Hyper heuristic solutions will be needed to compute solutions in a much better time and space complexities. It creates a solution by combining heuristics to generate automated search space from which generalized solutions can be tuned out. This paper provides in-depth knowledge on nature-inspired computing models, meta-heuristic models, hybrid meta heuristic models and hyper heuristic model. This work’s major contribution is on building a hyper heuristics approach from a meta-heuristic algorithm for any general problem domain. Various traditional algorithms and new generation meta heuristic algorithms has also been explained for giving readers a better understanding.

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Acknowledgements

The authors would like to thank the Government for India for providing Copyrights (IPR) for the Nature Inspired Algorithm developed by the authors (Registration Nos.: L-74114/2018, L-65846/2017, L-62609/2015, and L-60823/2014). The authors also extend their thanks to all the Machine Intelligent Research group, who have been a constant support during the various phases of analysis and implementation of different nature inspired algorithms.

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Correspondence to Vinod Chandra S. S..

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S. S., V.C., H. S., A. Nature inspired meta heuristic algorithms for optimization problems. Computing (2021). https://doi.org/10.1007/s00607-021-00955-5

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Keywords

  • Nature inspired computing
  • Meta heuristics
  • Hyper heuristics
  • Evolutionary computing
  • Bio inspired computing
  • Hybrid meta heuristics

Mathematics Subject Classification

  • 90C59