群体智能 Resources

Showing items tagged with "群体智能"

With the rapid development of swarm intelligence optimization algorithms, Passino introduced the Bacteria Foraging Optimization Algorithm (BFOA) in 2002, simulating the foraging behavior of E. coli bacteria and adding a new member to the family of biomimetic evolutionary algorithms. This chapter focuses on introducing the fundamental BFOA to programming enthusiasts, providing implementation insights including chemotaxis, reproduction, and elimination-dispersal mechanisms. Researchers can build upon this foundation to develop enhanced versions for practical applications.

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The Gravitational Search Algorithm (GSA) is a novel heuristic optimization algorithm proposed by Esmat Rashedi and colleagues at Kerman University, Iran, in 2009. Inspired by the simulation of gravitational forces in physics, it belongs to the category of swarm intelligence optimization algorithms. GSA operates by treating search particles as celestial bodies moving through space, where gravitational interactions guide their motion according to dynamical laws. Particles with higher fitness values possess greater inertial mass, and gravitational forces drive all particles toward the heaviest mass, thus gradually converging to the optimal solution. In implementation, the algorithm calculates masses based on fitness values, updates velocities using gravitational forces, and iteratively refines particle positions. GSA demonstrates strong global search capabilities and rapid convergence for complex optimization problems.

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