Hybrid Global/Local Derivative-Free Multi-objective Optimization via Deterministic Particle Swarm with Local Linesearch

A multi-objective deterministic hybrid algorithm (MODHA) is introduced for efficient simulation-based design optimization. The global exploration capability of multi-objective deterministic particle swarm optimization (MODPSO) is combined with the local search accuracy of a derivative-free multi-objective (DFMO) line search method. Six MODHA formulations are discussed, based on two MODPSO formulations and three DFMO activation criteria. Forty-five analytical test problems are solved, with two/three objectives and one to twelve variables. The performance is evaluated by two multi-objective metrics. The most promising formulations are finally applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions and compared to MODPSO and DFMO, showing promising results.