手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| アフリカハゲワシ最適化アルゴリズム× | Particle Swarm Optimization (PSO)× | |
|---|---|---|
| 分野 | 最適化 | 最適化 |
| 系統≠ | Machine learning | Process / pipeline |
| 提唱年≠ | 2020 | 1995 |
| 提唱者≠ | Hossein Moghdani | — |
| 種類≠ | Nature-inspired metaheuristic algorithm | Population-based metaheuristic / swarm intelligence |
| 原典≠ | Moghdani, H., & Salimifard, K. (2020). Volleyball player optimizer and African vultures optimization algorithms for solving global optimization problems. Applied Soft Computing, 97, 106794. link ↗ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| 別名≠ | AVOA | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| 関連≠ | 4 | 6 |
| 概要≠ | The African Vultures Optimization Algorithm (AVOA) is a metaheuristic algorithm introduced by Moghdani and Salimifard in 2020, inspired by the search and scavenging behavior of African vultures. Vultures employ sophisticated collaborative strategies to locate carrion across vast distances, using thermal air currents and group dynamics to navigate efficiently. AVOA translates these collective hunting behaviors into an effective optimization framework. | 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. |
| ScholarGateデータセット ↗ |
|
|