Hybrid ant colony optimization algorithms for mixed discrete-continuous optimization problems

摘要

This paper presents three new hybrid ant colony optimization algorithms that are extended from the ACO _R developed by Socha and Dorigo for solving mixed discrete-continuous constrained optimization problems. The first two hybrids, labeled ACO _R-HJ and ACO _R-DE, differs in philosophy with the former integrating ACO _R with the effective Hooke and Jeeves local search method and the latter a cooperative hybrid between ACO _R and differentia evolution. The third hybrid, labeled ACO _R-DE-HJ, is the second cooperative hybrid enhanced with the Hooke and Jeeves local search. All three algorithms incorporate a method to handle mixed discrete-continuous variables and the Deb's parameterless penalty method for handling constraints. Fourteen problems selected from various domains were used for testing the performance of both algorithms. It was showed that all three algorithms greatly outperform the original ACO _R in finding the exact or near global optima. An investigation was also carried out to determine the relative performance of applying local search with a fixed probability or varying probability.