Increasing parallelism of evolutionary algorithms by Nash games in design inverse flow problems
Purpose - The purpose of this paper is to compare the efficiency of four different algorithmic parallelization methods for inverse shape design flow problems. Design/methodology/approach - The included algorithms are: a parallelized differential evolution algorithm; island-model differential evolution with multiple subpopulations; Nash differential evolution with geometry decomposition using competitive Nash games; and the new Global Nash Game Coalition Algorithm (GNGCA) which combines domain and geometry decomposition into a "distributed one-shot" method. The methods are compared using selected academic reconstruction problems using a different number of simultaneous processes. Findings - The results demonstrate that the geometry decomposition approach can be used to improve algorithmic convergence. Additional improvements were achieved using the novel distributed one-shot method. Originality/value - This paper is a part of series of articles involving the GNGCA method. Further tests implemented for more complex problems are needed to study the efficiency of the approaches in more realistic cases.
evolutionary algorithms flow differential evolution parallel computing shape optimization island model nash algorithms domain decomposition methods geometry decomposition methods programming and algorithm theory