Artificial Fish Swarm Algorithm for Path Planning

In this article, we explore the application of Artificial Fish Swarm Algorithm (AFSA) for solving path planning problems. The core challenge involves identifying the shortest viable path between two specified points. AFSA is a biologically-inspired optimization technique that mimics fish schooling behavior, particularly foraging patterns, to discover optimal solutions through collective intelligence. The algorithm implementation comprises a main driver function coordinating multiple subfunctions, each serving distinct roles in the optimization process. Key components include initialization functions for generating artificial fish positions, behavior functions simulating preying, swarming, and following behaviors, and evaluation functions calculating fitness values based on path distance metrics. The main function orchestrates iterative updates of fish positions through parallel behavior execution and dynamic visual field adjustments, while maintaining bulletin board records of optimal solutions. Through systematic function integration and parameter tuning, the algorithm efficiently converges toward shortest-path solutions within constrained timeframes. This modular architecture not only demonstrates AFSA's mechanical principles but also provides adaptable framework for extending to other optimization domains such as resource scheduling or neural network training.