เปรียบเทียบวิธี

	อัลกอริทึมการปรับให้เหมาะสมด้วยวาฬ (Whale Optimization Algorithm - WOA)	Particle Swarm Optimization (PSO)
สาขาวิชา	การหาค่าเหมาะที่สุด	การหาค่าเหมาะที่สุด
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2016	1995
ผู้ริเริ่ม	Seyedali Mirjalili & Andrew Lewis	—
ประเภท	Swarm-based metaheuristic	Population-based metaheuristic / swarm intelligence
แหล่งต้นตำรับ	Mirjalili, S. & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
ชื่อเรียกอื่น	WOA, Balina Optimizasyon Algoritması (WOA), bubble-net attacking method	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
ที่เกี่ยวข้อง	5	6
สรุป	The Whale Optimization Algorithm (WOA) is a swarm-based metaheuristic introduced by Mirjalili and Lewis in 2016. It models the bubble-net hunting strategy of humpback whales, in which a group of whales spirals around prey while gradually tightening the encirclement. The algorithm balances global exploration and local exploitation through a small set of parameters and has become widely used in continuous engineering optimisation problems.	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.