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.