A Shared-Memory ACO-Based Algorithm for Numerical Optimization

Numerical optimization techniques are applied to a variety of engineering problems. The objective function evaluation is an important part of the numerical optimization and is usually realized as a black-box simulator. For efficient solving the numerical optimization problem, new shared-memory approach is proposed. The algorithm is based on an ACO meta-heuristics, where indirect coordination between ants drives the search procedure towards the optimal solution. Indirect coordination offers a high degree of parallelism and therefore relatively straightforward shared-memory implementation. For the communication between processors, the Intel-OpenMP library is used. It is shown that speed-up strongly depends on the simulation time. Therefore, algorithm´s performance, according to simulator´s time complexity, is experimentally evaluated and discussed.