Abstract
Area of computational intelligence is gaining researcher’s attention in ongoing trend of technology and evolution due to their high capability to deliver near-optimal solutions. A new hierarchy of algorithms has been proposed in the paper, and they have been organized on the basis of their inspiration sources. The broad two domains of the algorithms are modeling of human mind and nature-inspired intelligence. Nature-inspired computational algorithms being heuristic algorithms are robust and have optimization capability to solve obscure and substantiated problems. The heuristic techniques aim on finding the best possible solution to the query in a satisfiable amount of time. The computational intelligence methods inspired from nature have further been categorized into artificial immune systems, evolutionary algorithms, swarm intelligence, artificial neural networks and geoscience-based algorithms. Geoscience-based domain is the least explored domain in which the algorithms can be developed based on geographic phenomenon taking place on the earth’s surface. An extensive tabular comparison is done among algorithms of all the domains on the basis of various attributes. Also, variants of the algorithms and their implementation in a specific application have been examined. The efficiency and performance of selected algorithms have been compared on clustering and traveling salesman problem for better understanding.
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Acknowledgements
I would like to thank Mr. Satyarth Vaidya and Ms. Arshveer Kaur at Birla Institute of Technology and Science (BITS), Pilani for helping me in the initial stages of the work done in this paper. I would also like to thank my post graduate students at Malaviya National Institute of Technology (NIT), Jaipur who have helped me in the final stages of the work done in this paper.
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Goel, L. An extensive review of computational intelligence-based optimization algorithms: trends and applications. Soft Comput 24, 16519–16549 (2020). https://doi.org/10.1007/s00500-020-04958-w
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DOI: https://doi.org/10.1007/s00500-020-04958-w