Ant colony optimization for the nonlinear resource allocation problem

The nonlinear resource allocation problem addresses the important issue which seeks to find an optimal allocation of a limited amount of resource to a number of tasks for optimizing a nonlinear objective over the given resource constraint. Relevant literature has been focused on the use of mathematical programming approaches, few researches based on meta-heuristic algorithms have been conducted. In this paper we present an ant colony optimization algorithm for conquering the nonlinear resource allocation problem. To ensure the resource constraint is satisfied, we incorporate adaptive resource bounds to guide the search. The experimental results manifest that the proposed method is more effective and efficient than a genetic algorithm. Also, our method converges at a fast rate and a reliable performance guarantee is provided through a worst-case analysis.