The Pine Cone Optimization Algorithm (PCOA) is a novel nature-inspired optimization algorithm designed to solve scientific and engineering problems. Inspired by the reproductive mechanisms of pine trees, including pollination and pine cone dispersal by gravity and animals, PCOA employs powerful operators to simulate these mechanisms. The performance of PCOA is evaluated using classic benchmark functions (CEC017 and CEC2019) and engineering design problems (CEC2006 and CEC2011). Results show that PCOA outperforms well-known algorithms (PSO, DE, and WOA) and new algorithms (AVOA, RW_GWO, HHO, and GBO) in terms of accuracy. It is also competitive with state-of-the-art algorithms (LSHADE and EBOwithCMAR). In terms of convergence speed and time complexity, PCOA performs reasonably well. According to the Friedman test, PCOA ranks 1.68 and 9.42 percent better than EBOwithCMAR and LSHADE, respectively. The authors recommend PCOA for solving complex optimization problems in science, engineering, and industry. optimization; nature-inspired; pine tree; pine cone; mathematical benchmark functions; engineering problems; swarm intelligenceThe Pine Cone Optimization Algorithm (PCOA) is a novel nature-inspired optimization algorithm designed to solve scientific and engineering problems. Inspired by the reproductive mechanisms of pine trees, including pollination and pine cone dispersal by gravity and animals, PCOA employs powerful operators to simulate these mechanisms. The performance of PCOA is evaluated using classic benchmark functions (CEC017 and CEC2019) and engineering design problems (CEC2006 and CEC2011). Results show that PCOA outperforms well-known algorithms (PSO, DE, and WOA) and new algorithms (AVOA, RW_GWO, HHO, and GBO) in terms of accuracy. It is also competitive with state-of-the-art algorithms (LSHADE and EBOwithCMAR). In terms of convergence speed and time complexity, PCOA performs reasonably well. According to the Friedman test, PCOA ranks 1.68 and 9.42 percent better than EBOwithCMAR and LSHADE, respectively. The authors recommend PCOA for solving complex optimization problems in science, engineering, and industry. optimization; nature-inspired; pine tree; pine cone; mathematical benchmark functions; engineering problems; swarm intelligence