Performance analysis of elitism in multi-objective ant colony optimization algorithms

This paper investigates the effect of elitism on multi-objective ant colony optimization algorithms (MACOs). We use a straightforward and systematic approach in this investigation with elitism implemented through the use of local, global, and mixed non-dominated solutions. Experimental work is conducted using a suite of multi-objective traveling salesman problems (mTSP), each with two objectives. The experimental results indicate that elitism is essential to the success of MACOs in solving multi-objective optimization problems. Further, global elitism is shown to play a particularly important role in refining the pheromone information for MACOs during the search process. Inspired by these results, we also propose an adaptation strategy to control the effect of elitism. With this strategy, the solutions most recently added to the global non-dominated archive are given a higher priority in defining the pheromone information. The obtained results on the tested mTSPs indicate improved performance in the elitist MACO when using the adaptive strategy compared to the original version.