Swarm Intelligence Algorithms: Modifications and Applications - Slowik, Adam (CRC Press, 2020)
This chapter presents a nature-inspired ant colony optimization (ACO) technique, along with its modified variants. The improved versions of this optimization technique are slightly different and effective than that of its standard version. ACO has inspired from the foraging behavior of ant colony and its capability to seek the shortest path between their nest and food source. This optimization method is based on a natural phenomena known as pheromone trails, a substance laid down by ants especially when carrying food so that their fellow ants can sense and follow this path. The new ants entering into the ant system will follow the path with highest pheromone concentration. In this chapter, a brief overview of standard ACO is presented, followed by different variants of ACO since its development. The application of the ACO technique for real-life optimization problems is demonstrated by solving an optimal shunt capacitor allocation problem of 33-bus test distribution system for power loss minimization.