Applying Ant Colony Optimization for Continuous Function Optimization Problems

This paper primarily applies the Ant Colony Optimization (ACO) algorithm to solve continuous function optimization problems. ACO is a heuristic optimization algorithm that simulates ant foraging behavior, where artificial ants search for optimal solutions in continuous optimization landscapes through pheromone trail deposition and path selection mechanisms. The core principle involves ants depositing pheromones to guide other colony members toward better solutions, gradually converging to near-optimal solutions through positive feedback. From an implementation perspective, the algorithm typically involves initializing candidate solutions, updating pheromone concentrations based on solution quality, and incorporating evaporation mechanisms to avoid local optima. Key functions would include solution generation using probability distributions influenced by pheromone levels, and fitness evaluation for continuous objective functions. In this paper, we provide detailed explanations of ACO's theoretical foundations and practical applications, supplemented with concrete examples demonstrating its effectiveness in continuous optimization scenarios through performance metrics and convergence analysis.