Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems
By Malik Braik. Expert systems with applications, 2021
453 citations recorded by the source corpus.
Abstract This paper presents a novel meta-heuristic algorithm named Chameleon Swarm Algorithm (CSA) for solving global numerical optimization problems. The base inspiration for CSA is the dynamic behavior of chameleons when navigating and hunting for food sources on trees, deserts and near swamps. This algorithm mathematically models and implements the behavioral steps of chameleons in their search for food, including their behavior in rotating their eyes to a nearly 360°scope of vision to locate prey and grab prey using their sticky tongues that launch at high speed. These foraging mechanisms practiced by chameleons eventually lead to feasible solutions when applied to address optimization problems. The stability of the proposed algorithm was assessed on sixty-seven benchmark test functions and the performance was examined using several evaluation measures. These test functions involve unimodal, multimodal, hybrid and composition functions with different levels of complexity. An extensive comparative study was conducted to demonstrate the efficacy of CSA over other meta-heuristic algorithms in terms of optimization accuracy. The applicability of the proposed algorithm in reliably addressing real-world problems was demonstrated in solving five constrained and computationally expensive engineering design problems. The overall results of CSA show that it offered a favorable global or near global solution and better performance compared to other meta-heuristics.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/d6b5edf6a1b9f80a0cb6e6cb178815a27a09dffa
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In Artificial Intelligence
In Computational Theory and Mathematics
Citations in the corpus
- Zebra Optimization Algorithm: A New Bio-inspired Optimization Algorithm for Solving Optimization Algorithm (2022, methodology): “…organisms when hunting and trapping prey has been the main idea of various metaheuristic algorithms such as Grey Wolf Optimizer (GWO) [10], Spotted Hyena Optimizer (SHO) [11], Whale Optimization Algorithm (WOA) [12], Chameleon Swarm Algorithm (CSA) [13], and Marine Predator Algorithm (MPA) [2].”