Bird Swarm Algorithm (BSA) is a bioinspired evolutionary algorithm developed by Meng and colleagues in 2015. It utilizes swarm intelligence based on social interactions and behavior of bird flocks.
The algorithm integrates three aspects of bird behavior: flight, foraging, and vigilance. Individual birds employ unique strategies, leading to effective cooperation and support in optimizing solutions. Different individuals within the BSA algorithm can switch between these behaviors to balance exploration and exploitation, aiming to avoid premature convergence.
Flight behavior and foraging are dependent on global and individual fitness. BSA models flock behavior, where birds exhibit adaptations such as flocking, communication, adaptability, and leadership-following dynamics to find optimal solutions.
BSA combines these principles for an optimization ap...
#MQL5 #MT5 #birds #optimization
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The algorithm integrates three aspects of bird behavior: flight, foraging, and vigilance. Individual birds employ unique strategies, leading to effective cooperation and support in optimizing solutions. Different individuals within the BSA algorithm can switch between these behaviors to balance exploration and exploitation, aiming to avoid premature convergence.
Flight behavior and foraging are dependent on global and individual fitness. BSA models flock behavior, where birds exhibit adaptations such as flocking, communication, adaptability, and leadership-following dynamics to find optimal solutions.
BSA combines these principles for an optimization ap...
#MQL5 #MT5 #birds #optimization
Read more...
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