השוואת שיטות

	ממטב חיפוש מדוזות	אופטימיזציית נחיל חלקיקים (PSO)	אלגוריתם רירתי
תחום	אופטימיזציה	אופטימיזציה	אופטימיזציה
משפחה	Machine learning	Process / pipeline	Machine learning
שנת המקור	2022	1995	2020
הוגה השיטה	Xueying Shi	—	Shimin Li
סוג	Nature-inspired metaheuristic algorithm	Population-based metaheuristic / swarm intelligence	Nature-inspired metaheuristic algorithm
מקור מכונן	Shi, X., Sun, Y., Zhan, Z. H., Yuen, K. F., & Zhang, J. (2022). Jellyfish search optimizer: A new bio-inspired metaheuristic algorithm for solving optimization tasks. Neural Computing and Applications, 34(10), 7651-7673.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.	Li, S., Chen, H., Wang, M., Heidari, A. A., & Chakraborty, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300-323.
כינויים	JSO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)	SMA
קשורות	3	6	5
תקציר	The Jellyfish Search Optimizer (JSO) is a biologically-inspired metaheuristic algorithm introduced by Shi et al. in 2022, based on the movement and foraging behavior of jellyfish in ocean environments. Jellyfish exhibit two distinct behaviors: passive drifting with ocean currents (exploration) and active swimming toward food sources (exploitation). JSO captures these behaviors to create an effective balance between global search and local refinement.	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.	The Slime Mould Algorithm (SMA) is a nature-inspired metaheuristic optimization technique introduced by Li et al. in 2020. It mimics the behavior of slime moulds, which spread and contract to find optimal food sources. SMA addresses complex optimization problems by simulating the adaptive foraging and spatial distribution patterns of these organisms.