Cooperative evolutionary algorithm for space trajectory optimization

Abstract

A hybrid evolutionary algorithm which synergistically exploits differential evolution, genetic algorithms and particle swarm optimization, has been developed and applied to spacecraft trajectory optimization. The cooperative procedure runs the three basic algorithms in parallel, while letting the best individuals migrate to the other populations at prescribed intervals. Rendezvous problems and round-trip Earth-Mars missions have been considered. The results show that the hybrid algorithm has better performance compared to the basic algorithms that are employed. In particular, for the rendezvous problem, a 100% efficiency can be obtained both by differential evolution and the genetic algorithm only when particular strategies and parameter settings are adopted. On the other hand, the hybrid algorithm always attains the global optimum, even though nonoptimal strategies and parameter settings are adopted. Also the number of function evaluations, which must be performed to attain the optimum, is reduced when the hybrid algorithm is used. In the case of Earth-Mars missions, the hybrid algorithm is successfully employed to determine mission opportunities in a large search space.

Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

“…The three subpopulations share the global best solution during their evolution. Another analog is the DEPSO-SC that is proposed by Sentinella and Casalino, incorporating three EAs, which include the GA, PSO, and DE, to solve spacecraft trajectory optimization problems [96], [97]. The three optimizers work in parallel and, periodically, let their best individuals migrate to other subpopulations.…”

Injection, Saturation and Feedback in Meta-Heuristic Interactions

“…Although a heterogeneous setup in meta-heuristic parameters has been shown to be effective in [13,14], heterogeneity in terms of distinct meta-heuristics has been only recently reported in [12,15]. The beneficial effect of cooperation between different meta-heuristics has also been exploited in real world applications [16]. Thus, it seems that it is beneficial to deploy multiple meta-heuristics and use them in cooperation to find the optimal solution to the problem.…”

Evolved spacecraft trajectories for low earth orbit

“…Bessette & Spencer [1] used both DE [18] and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [8] approaches to optimize multi-objective Keplerian orbital transfers in LEO; in their study, the optimal trajectory was based on the simultaneous considerations of fuel consumption and time of flight. Sentinella and Casalino [17] used both GA and DE approaches to examine interplanetary orbit trajectory optimization (e.g., as opposed to LEO maneuvers) involving intermediate planetary fly-by gravitational assists. Englander [5] posed the problem of choosing a sequence of fly-bys as an integer programming problem that was solved using a GA approach, and nested DE and particle swarm optimization inside a GA to reproduce solutions to the Galileo and Cassini missions [6].…”