Chaos-Enhanced Ant Colony Optimization Algorithm

The Chaos-Enhanced Ant Colony Optimization (ACO) algorithm is an optimization method that integrates chaos theory with traditional ant colony algorithms. The standard ACO mimics ant foraging behavior through swarm intelligence, using pheromone-based positive feedback mechanisms to identify optimal paths. However, when solving complex optimization problems, traditional ACO tends to converge prematurely to local optima and exhibits slow convergence rates.

The core innovation of chaos enhancement lies in leveraging the ergodicity and randomness of chaotic motion to strengthen global search capabilities. Key implementation approaches include: 1) Initializing pheromone distributions using chaotic sequences (e.g., Logistic map iterations) to prevent premature convergence; 2) Introducing chaotic perturbations during pheromone updates through functions like chaos_disturbance(pheromone_matrix) to escape local optima; 3) Dynamically adjusting heuristic factors using chaos variables via methods such as adjust_heuristic(chaos_var) to balance exploration and exploitation.

In function optimization domains, chaos-enhanced ACO proves particularly effective for multimodal, nonlinear, high-dimensional problems. Compared to standard ACO, the enhanced algorithm demonstrates superior global search performance and faster convergence. Typical applications include engineering parameter optimization, neural network training, and economic dispatch problems requiring global optimum solutions.

The algorithm's advantage stems from preserving ACO's distributed computation characteristics while overcoming premature convergence through chaotic mechanisms. Future developments may involve hybridizing with other intelligent algorithms (e.g., genetic algorithms) and extending applications to dynamic optimization problems with adaptive chaos parameter tuning.