On the Development and Application of a Modified Ant Colony Optimization (MACO) Algorithm for Solving Optimization Problems

Abstract

This study presents the development and application of a Modified Ant Colony Optimization (MACO) algorithm incorporating constant variables to enhance solution efficiency in optimization problems. In the traditional Ant Colony Optimization (ACO) algorithms, though effective in solving combinatorial problems, they often suffer from slow convergence and premature stagnation due to excessive dependence on pheromone and parameter stochastic update settings. The MACO algorithm introduces constant variables control that regulates pheromone evaporation rate, heuristic influence, and exploration–exploitation balance, thereby improving algorithm stability and convergence speed. Additionally, the modification integrates features from complementary metaheuristics to strengthen global search capability while maintaining local refinement efficiency. The model is formulated and tested on benchmark optimization problems, demonstrating improved performance in term of the solution quality, computational rigor, time, and robustness compared to traditional ACO approaches. Results indicate that the incorporation of constant variables reduces parameter uncertainty and enhances repeatability of outcomes. The study contributes to the development of swarm intelligence techniques by providing a more controlled, flexible, and reliable optimization structure suitable for applications in engineering, routing, and logistics design problems.