Abstract: In this paper, a new hybrid of genetic algorithm (GA)
and simulated annealing (SA), referred to as GSA, is presented. In
this algorithm, SA is incorporated into GA to escape from local
optima. The concept of hierarchical parallel GA is employed to
parallelize GSA for the optimization of multimodal functions. In
addition, multi-niche crowding is used to maintain the diversity in
the population of the parallel GSA (PGSA). The performance of the
proposed algorithms is evaluated against a standard set of multimodal
benchmark functions. The multi-niche crowding PGSA and normal
PGSA show some remarkable improvement in comparison with the
conventional parallel genetic algorithm and the breeder genetic
algorithm (BGA).