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New Caledonian crow learning algorithm: A new metaheuristic algorithm for solving continuous optimization problems
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DOI：10.1016/j.asoc.2020.106325.png)
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Abstract
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Several metaheuristic algorithms have been introduced to solve different optimization problems. Such algorithms are inspired by a wide range of natural phenomena or behaviors. We introduced a new metaheuristic algorithm called New Caledonian (NC) crow learning algorithm (NCCLA), inspired by efficient social, asocial, and reinforcement mechanisms that NC-crows use to learn behaviors for developing tools from Pandanus trees to obtain food. Such mechanisms were modeled mathematically to develop NCCLA, whose performance was subsequently evaluated and statistically analyzed using 23 classical benchmark functions and 4 engineering problems. The results verify NCCLA's performance efficiency and highlight its accelerated convergence and ability to escape from local minima. An extensive comparative study was conducted to demonstrate that the solution accuracy and convergence rate of NCCLA were better than those of other state-of-the-art metaheuristics. The results also indicate that NCCLA is a promising algorithm that can be applied to solve other optimization and real-world problems. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Social learning
Swarm intelligence
Observational learning
Nature-inspired computing
Socio-inspired optimization
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