পদ্ধতির তুলনা করুন

	অ্যাকুইলা অপ্টিমাইজার	হ্যারিস হকস অপ্টিমাইজেশন	পার্টিকেল সোয়ার্ম অপ্টিমাইজেশন (PSO)
ক্ষেত্র	অনুকূলকরণ	অনুকূলকরণ	অনুকূলকরণ
পরিবার	Machine learning	Machine learning	Process / pipeline
উদ্ভবের বছর	2021	2019	1995
প্রবর্তক	Laith Abualigah	Ali Asghar Heidari	—
ধরন	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm	Population-based metaheuristic / swarm intelligence
মৌলিক উৎস	Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers and Industrial Engineering, 157, 107250.	Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
অপর নাম	AO	HHO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
সম্পর্কিত	3	4	6
সারসংক্ষেপ	The Aquila Optimizer (AO) is a nature-inspired metaheuristic algorithm presented by Abualigah et al. in 2021, modeled after the hunting behavior and sensory abilities of golden eagles (aquila chrysaetos). The algorithm captures the exploration and exploitation phases of eagle hunting, including high-altitude soaring, exploration with high-precision vision, and rapid diving attacks. AO is designed to solve both constrained and unconstrained optimization problems.	Harris Hawks Optimization (HHO) is a metaheuristic algorithm introduced by Heidari et al. in 2019, inspired by the hunting strategies of Harris's hawks. The algorithm models the cooperative hunting behavior and escape strategies of these raptors to solve complex optimization problems. HHO balances exploration through perching and exploitation through dynamic pursuit, making it effective for multimodal and high-dimensional optimization.	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.