Nature inspired meta heuristic algorithms for optimization problems

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...

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Veröffentlicht in:Computing Jg. 104; H. 2; S. 251 - 269
Hauptverfasser: Vinod Chandra, S S, Anand, H S
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Vienna Springer Vienna 01.02.2022
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Computer Appl. in Administrative Data Processing
Computer Communication Networks
Computer Science
Decision making
Heuristic
Heuristic methods
Information Systems Applications (incl.Internet)
Optimization
Software Engineering
Survey Article
Evolutionary computing
Nature inspired computing
Meta heuristics
Hybrid meta heuristics
Hyper heuristics
90C59
Bio inspired computing
ISSN:0010-485X, 1436-5057
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-021-00955-5