Artificial Fish Swarm Algorithm Implementation

For the basic implementation of the artificial fish swarm algorithm in node localization, we can elaborate its working mechanism in greater detail. The algorithm employs a group of virtual fish to simulate the search process. Each artificial fish possesses its own position and velocity parameters, which can be algorithmically represented as coordinate vectors and movement vectors in the code implementation. The fish dynamically adjust their swimming direction and speed based on positional and behavioral information from neighboring fish, typically implemented through functions calculating visual range and crowding factors. This collective behavior enables the swarm to gradually converge near optimal solutions during the search process. The algorithm incorporates stochastic components through randomization functions that introduce perturbation in movement calculations, enhancing search diversity and preventing premature convergence to local optima. Key algorithmic operations include prey behavior (local search), swarm behavior (convergence tendency), and follow behavior (information sharing), which are programmatically implemented through conditional statements and fitness evaluations. These mechanisms make the artificial fish swarm algorithm particularly suitable for solving complex optimization problems like node localization in wireless sensor networks, where it can efficiently determine unknown node positions through iterative population-based search.