Parallel simulated annealing and genetic algorithms: A space of hybrid methods

Abstract

Simulated annealing and genetic algorithms represent powerful optimization methods with complementary strengths and weaknesses. Hence, there is an interest in identifying hybrid methods (which combine features of both SA and GA) that exhibit performance superior than either method alone. This paper introduces a systematic approach to identifying these hybrids by defining a space of methods as a nondeterministic generating grammar. This space includes SA, GA, previously introduced hybrids and many new methods. An empirical evaluation has been completed for 14 methods from this space applied to 9 diverse optimization problems. Results demonstrate that the space contains promising new methods. In particular, a new method that combines the recombinative power of GAs and annealing schedule of SA is shown to be one of the best methods for all 9 optimization problems explored.