Comparison of computational performance of GA and PSO optimization techniques when designing similar systems - Typical PWR core case

Research highlights: → Performance of PSO and GA techniques applied to similar system design. → This work uses ANGRA1 (two loop PWR) core as a prototype. → Results indicate that PSO technique is more adequate than GA to solve this kind of problem. - Abstract: This paper compares the performance of two optimization techniques, particle swarm optimization (PSO) and genetic algorithm (GA) applied to the design a typical reduced scale two loop Pressurized Water Reactor (PWR) core, at full power in single phase forced circulation flow. This comparison aims at analyzing the performance in reaching the global optimum, considering that both heuristics are based on population search methods, that is, methods whose population (candidate solution set) evolve from one generation to the next using a combination of deterministic and probabilistic rules. The simulated PWR, similar to ANGRA 1 power plant, was used as a case example to compare the performance of PSO and GA. Results from simulations indicated that PSO is more adequate to solve this kind of problem.