Ant Colony Optimization with Local Search for Continuous Functions

Abstract

Ant algorithms are a recently developed, population-based approach which was inspired by the observation of the behavior of ant colonies. Based on the ant colony optimization idea, we present a hybrid ant colony system (ACS) coupled with a pareto local search (PLS) algorithm, named PACS, and apply to the continuous functions optimization. The ACS makes firstly variable range into grid. In local search, we use the PLS to escape local optimum. Computational results for some benchmark problems demonstrate that the proposed approach has the high search superior solution ability.