Unified strategy for intensification and diversification balance in ACO metaheuristic

Abstract

This intensification and diversification in Ant Colony Optimization (ACO) is the search strategy to achieve a trade-off between learning a new search experience (exploration) and earning from the previous experience (exploitation). The automation between the two processes is maintained using reactive search. However, existing works in ACO were limited either to the management of pheromone memory or to the adaptation of few parameters. This paper introduces the reactive ant colony optimization (RACO) strategy that sticks to the reactive way of automation using memory, diversity indication, and parameterization. The performance of RACO is evaluated on the travelling salesman and quadratic assignment problems from TSPLIB and QAPLIB, respectively. Results based on a comparison of relative percentage deviation revealed the superiority of RACO over other well-known metaheuristics algorithms. The output of this study can improve the quality of solutions as exemplified by RACO.