Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems

In this paper, a new metaheuristic algorithm called the Coati Optimization Algorithm (COA) is introduced, which mimics coati behavior in nature. The simulation results indicate that COA has an evident superiority over the compared algorithms by balancing exploration in global search and exploitation in local search, and is far more competitive.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 The fundamental idea of COA is the simulation of the two natural behaviors of coatis: (i) their behavior when attacking and hunting iguanas and (ii) their escape from predators.
• 2 The implementation steps of COA are described and mathematically modeled in two phases of exploration and exploitation.
• 3 COA performance is evaluated on fifty-one objective functions, including twenty-nine functions from the IEEE CEC-2017 test suite and twenty-two real-world applications from the IEEE CEC-2011 test suite.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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