MACS: An Agent-Based Memetic Multiobjective Optimization Algorithm Applied to Space Trajectory Design
This incremental improvement addresses optimization challenges in space trajectory design and similar domains.
The paper tackles multiobjective optimization by introducing an agent-based memetic algorithm, which outperforms several state-of-the-art methods like NSGA-II and MOPSO in capturing more of the Pareto set and achieving better convergence, though with higher variance in some cases.
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.