Abstract:
Many evolutionary computational models have
been introduced for solving engineering optimization problems
that usually intend to find the global optimum solution. These
methods, however, expose high computational effort and lack the
diversity of the population and hence remain trapped in a local
optimum. In this paper, we propose new hybrid optimization
model, where a version of niche Cultural Algorithm is integrated
with Tabu Search to guide the fittest individuals to new
promising areas, aiming to escape local optima. The proposed
approach significantly improves the performance of Cultural
Algorithm by maintaining a high diversity among the population
of problem solvers. This helps avoid premature and enhances
located solutions. The technique is tested using a set of realparameter optimization benchmark problems. The results in all
cases indicate that the proposed method is capable of obtaining
the optimal solutions with small number of function evaluations.