An inflationary differential evolution algorithm for space trajectory optimization

TL;DR

This paper introduces an inflationary differential evolution algorithm for space trajectory optimization, leveraging a discrete dynamical system approach to improve convergence and avoid local minima, outperforming standard methods.

Contribution

It presents a novel inflationary differential evolution algorithm based on a dynamical system framework, with an extension incorporating guided restarts for enhanced performance.

Findings

The proposed algorithm outperforms standard Differential Evolution on space trajectory problems.

The dynamical system approach guarantees convergence to fixed points with probability one.

Guided restart procedures further reduce stagnation in local minima.

Abstract

In this paper we define a discrete dynamical system that governs the evolution of a population of agents. From the dynamical system, a variant of Differential Evolution is derived. It is then demonstrated that, under some assumptions on the differential mutation strategy and on the local structure of the objective function, the proposed dynamical system has fixed points towards which it converges with probability one for an infinite number of generations. This property is used to derive an algorithm that performs better than standard Differential Evolution on some space trajectory optimization problems. The novel algorithm is then extended with a guided restart procedure that further increases the performance, reducing the probability of stagnation in deceptive local minima.