MACS: An Agent-Based Memetic Multiobjective Optimization Algorithm Applied to Space Trajectory Design

TL;DR

This paper introduces MACS, an agent-based memetic algorithm for multiobjective optimization, demonstrating superior ability to explore Pareto fronts in space trajectory design compared to existing methods.

Contribution

The paper proposes a novel agent-based memetic algorithm that effectively combines heuristics for enhanced exploration in multiobjective optimization.

Findings

MACS outperforms state-of-the-art algorithms in identifying diverse Pareto optimal solutions.

MACS shows statistically better convergence in space trajectory problems.

The algorithm captures Pareto front regions missed by other methods.

Abstract

This paper presents an algorithm for multiobjective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighborhood of each agent. These heuristics are complemented with a combination of a local and global archive. The novel agent- based algorithm is tested at first on a set of standard problems and then on three specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multiobjective optimisation algorithms that use the Pareto dominance as selection criterion: NSGA-II, PAES, MOPSO, MTS. The results demonstrate that the agent-based search can identify parts of the Pareto set that the other algorithms were not able to capture. Furthermore, convergence is statistically better although the variance of the results is in some cases higher.