In this paper, a novel metaheuristic called the Nudibranch Optimization Algorithm (NUOA) is developed. The algorithm is derived from the various modes that these nudibranchs use to look for their next meal and, in turn, ways of avoiding any threats. However, this algorithm evaluates a benchmark set of functions, encompassing all the executed analysis tasks pertinent to the CEC2019 test suites, as well as the integrated classical functions. These findings demonstrate that, in general, NUOA provides a better opportunity to explore and exploit than all of the proposed algorithms for NUOA. Therefore, the evaluation of statistical data and proofs validates the performance improvement, demonstrating NUOA's superiority over the other algorithms by at least one order of magnitude. Upon closer examination of the parameters, it becomes clear that NUOA maintains its functionality across various optimization tasks. We also use some of the most popular optimization algorithms, including particle swarm optimization (PSO), artificial bee colony (ABC), pelican optimization algorithm (POA), and fitness dependent optimizer (FDO), and compare them with NUOA.
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