ACO Ant Colony Optimization Algorithm

Ant Colony Optimization (ACO), also known as the ant algorithm, is a probabilistic technique for finding optimal paths in graphs. Proposed by Marco Dorigo in his 1992 PhD thesis, the algorithm draws inspiration from ants' path-finding behavior during food search activities. As a simulated evolutionary algorithm, initial research has demonstrated its excellent properties. When applied to PID controller parameter optimization design problems, comparative studies with genetic algorithms reveal ACO's effectiveness as a novel evolutionary optimization method. Numerical simulations confirm its practical value and superior performance characteristics.

Function Optimization Using Penalty Function-Based Particle Swarm Algorithm

Since the establishment of bionics in the mid-1950s, researchers have begun developing bio-inspired algorithms to solve complex optimization problems. These algorithms simulate evolutionary mechanisms and include Simulated Annealing (SA), Seeker Optimization Algorithm (SOA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GA). Notable contributions include Professor J.H. Holland's GA from University of Michigan, Rechenberg's Evolution Strategy, and Fogel's Evolutionary Programming.