Explore MATLAB source code curated for "细菌觅食算法" with clean implementations, documentation, and examples.
Bacterial Foraging Optimization Algorithm - A robust bio-inspired optimization method suitable for finding optimal solutions, with MATLAB code implementation and parameter tuning guidance.
This optimization algorithm is designed for control parameter tuning, specifically for PID controller optimization and gain value adjustment through biologically-inspired computational methods.
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.
Implementation of object clustering using the bacterial foraging algorithm, representing the foundational unmodified version of this approach.
A program integrating Bacterial Foraging Optimization and Particle Swarm Optimization algorithms for computing fitness function extrema, featuring implementation insights on population initialization, chemotaxis movement, and velocity-update mechanisms with significant reference value.
Bacterial Foraging Algorithm simulates the foraging behavior of E. coli bacteria in the human intestine, belonging to the category of bio-inspired optimization algorithms. In the BFA model, solutions to optimization problems correspond to bacterial states in the search space, represented by fitness values of the objective function. The BFA algorithm consists of three key steps: chemotaxis, reproduction, and elimination-dispersal. The implementation typically involves maintaining a population of bacteria that perform random movements (tumbles) followed by directed swims toward improving fitness regions, with periodic population updates through reproduction and random dispersal mechanisms.
Implementation of object clustering using Bacterial Foraging Algorithm with Matlab code from "Swarm Intelligence and Bionic Computing" by Yang Shuying - original unmodified base version
Implementation of Bacterial Foraging Optimization Algorithm for Object Clustering with Code-Oriented Approach
Basic Bacterial Foraging-based Clustering with Implementation Insights