Nostoc commune Vaucher algorithm(ANOA) : a novel metaheuristic algorithm inspired by the growth of Nostoc

Abstract Metaheuristic algorithms are increasingly applied to solve complex optimization problems due to their ability to effectively explore the solution space. This paper proposes a novel metaheuristic optimization algorithm—Nostoc commune Vaucher Optimization Algorithm (ANOA), which is inspired by the growth characteristics of Nostoc in nature. ANOA primarily simulates the following biological features .quorum sensing, where the elite population guides the swarm to explore promising regions of the solution space; asexual reproduction and chain-like cell search mechanism (based on the filamentous morphology of Nostoc commune Vaucher), which generate offspring during the search process and conduct detailed exploitation through intercellular communication; photoautotrophy, which adaptively adjusts pigment content according to light intensity to obtain sufficient energy, corresponding to random exploration and exploitation of the solution space; and heterocyst differentiation and cell aging-death mechanism, which aim to escape local optima and enhance the robustness of the search .To evaluate the exploration and exploitation capability of the algorithm, we tested it on the CEC2017 and CEC2022 benchmark test suites. Comparative analyses with 12 well-known optimization algorithms (such as White Shark Optimizer, WSO) and 6 recently developed algorithms (such as Artificial Lemming Algorithm, ALA) demonstrate that ANOA achieved first place in terms of average fitness ranking on the CEC2017 test set across 10, 30, 50, and 100 dimensions, and ranked first and second on the CEC2022 test suite for 10D and 20D problems, respectively. ANOA exhibited superior and more stable performance across all functions in both test suites. Finally, we extended the evaluation to nine widely used engineering design optimization problems, where ANOA consistently achieved the best rankings, highlighting its competitive advantage and strong potential for solving real-world engineering applications .The source code of ANOA is publicly available at https://github.com/13Qinjiu/ANOA-a-novel-metaheuristic-algorithm-inspired-by-the-growth-of-Nostoc.