Comparison and integrated use of differential evolution and genetic algorithms for space trajectory optimisation

Abstract

An accurate analysis of the behaviour of a differential evolution (DE) algorithm and a genetic algorithm (GA) when dealing with two problems concerning space trajectory optimisation is presented. Results show that, depending on the features of the problem, the comparison may produce different results, as GA can be better than DE in terms of efficiency, i.e., capability of finding the global optimum, and number of function evaluations, or vice versa. An integrated use of DE and GA in a multi-population optimisation procedure is then performed, showing improvements in both efficiency and number of function evaluations.