The Pine Cone Optimization Algorithm (PCOA) is a nature-inspired optimization algorithm designed based on the reproductive mechanisms of pine trees, including pollination and cone dispersal by gravity and animals. The algorithm uses new operators to simulate these biological processes. PCOA was tested on various mathematical and engineering benchmark functions, including CEC017, CEC2019, CEC2006, and CEC2011. The results showed that PCOA outperformed well-known algorithms like PSO, DE, and WOA, as well as new algorithms such as AVOA, RW_GWO, HHO, and GBO. PCOA was also competitive with state-of-the-art algorithms like LSHADE and EBOwithCMAR. In terms of convergence speed and time complexity, PCOA performed reasonably well. According to the Friedman test, PCOA ranked first with a 1.68 and 9.42 percent improvement over EBOwithCMAR and LSHADE, respectively. The algorithm was recommended for solving complex optimization problems in science, engineering, and industry. PCOA's unique approach combines swarm-based and evolutionary operators, and it uses clustering and adaptive weight mechanisms to balance exploration and exploitation. The algorithm's performance was evaluated on 62 benchmark functions and 21 engineering design problems, demonstrating its effectiveness in solving both mathematical and engineering optimization tasks.The Pine Cone Optimization Algorithm (PCOA) is a nature-inspired optimization algorithm designed based on the reproductive mechanisms of pine trees, including pollination and cone dispersal by gravity and animals. The algorithm uses new operators to simulate these biological processes. PCOA was tested on various mathematical and engineering benchmark functions, including CEC017, CEC2019, CEC2006, and CEC2011. The results showed that PCOA outperformed well-known algorithms like PSO, DE, and WOA, as well as new algorithms such as AVOA, RW_GWO, HHO, and GBO. PCOA was also competitive with state-of-the-art algorithms like LSHADE and EBOwithCMAR. In terms of convergence speed and time complexity, PCOA performed reasonably well. According to the Friedman test, PCOA ranked first with a 1.68 and 9.42 percent improvement over EBOwithCMAR and LSHADE, respectively. The algorithm was recommended for solving complex optimization problems in science, engineering, and industry. PCOA's unique approach combines swarm-based and evolutionary operators, and it uses clustering and adaptive weight mechanisms to balance exploration and exploitation. The algorithm's performance was evaluated on 62 benchmark functions and 21 engineering design problems, demonstrating its effectiveness in solving both mathematical and engineering optimization tasks.