A hybrid niched-island genetic algorithm applied to a nuclear core optimization problem
Cláudio Márcio do Nascimento Abreu Pereira · 2005
Diversity maintenance is a key-feature in most genetic-based optimization processes. The quest for such characteristic, has been motivating improvements in the original genetic algorithm (GA). The use of multiple populations (called islands) has demonstrating to increase diversity, delaying the genetic drift. Island Genetic Algorithms (IGA) lead to better results, however, the drift is only delayed, but not avoided. An important advantage of this approach is the simplicity and efficiency for parallel processing. Diversity can also be improved by the use of niching techniques. Niched Genetic Algorithms (NGA) are able to avoid the genetic drift, by containing evolution in niches of a single-population GA, however computational cost is increased. In this work it is investigated the use of a hybrid Niched-Island Genetic Algorithm (NIGA) in a nuclear core optimization problem found in literature. Computational experiments demonstrate that it is possible to take advantage of both, performance enhancement due to the parallelism and drift avoidance due to the use of niches. Comparative results shown that the proposed NIGA demonstrated to be more efficient and robust than an IGA and a NGA for solving the proposed optimization problem.