A Review of Ant Colony Optimization for Solving 0-1 Knapsack and Traveling Salesman Problems

Ant Colony Optimization (ACO) represents a widespread nature-based metaheuristic algorithm which solves combinatorial optimization problems effectively [1]. This research study examines ACO-based solutions for Traveling Salesman Problem (TSP) and 0-1 Knapsack Problem (0-1 KP) which are both identified as NP-hard problems. ACO successfully achieves near-optimal solutions because it duplicates real ants' pheromone-based foraging approach and operates between exploration and exploitation modes effectively. This review discusses ACO-based methods for solving complex problems through a discussion of modern solution methods and their evaluation results and performance benefits over basic optimization approaches. This section presents solutions for optimization challenges which include computational complexity and two additional problems through hybrid models while exploring adaptive parameter adjustments as well as quantum-inspired optimizations [2]. The development of ACO methods aims at combining this algorithm with deep learning and reinforcement learning approaches to boost its operational speed and practical performance across dynamic operational contexts. The findings suggest that ACO remains a promising optimization technique with vast potential for solving large-scale combinatorial problems in various domains [3].