Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization

Pellegrini, Riccardo; Serani, Andrea; Leotardi, Cecilia; Iemma, Umberto; Campana, Emilio F.; Diez, Matteo

doi:10.1016/j.asoc.2017.05.013

Cited by 33 publications

(21 citation statements)

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“…The MODPSO1 and MODPSO3 setups are defined as in [14]. The number of particles N p is set to 8N of N dv , initialized using a Hammersley sequence sampling [22] over variable domain and boundary.…”

Section: Algorithm Parameters and Setupmentioning

confidence: 99%

“…A number of 45 benchmark problems [14] is used, including convex and nonconvex, continuous and discontinuous Pareto fronts, with N of = 2, 3 and 1 ≤ N dv ≤ 12.…”

Section: Analytical Benchmark Problemsmentioning

confidence: 99%

“…Often this is not attainable in SBDO, especially if CPU-time expensive simulations provide directly objectives and constraints. Therefore, deterministic methods have been developed and assessed [4,14].…”

Section: Introductionmentioning

confidence: 99%

“…The objective of the present work is to introduce and assess a novel multiobjective deterministic hybrid algorithm (MODHA), which combines the global exploration capabilities of multi-objective deterministic particle swarm optimization (MODPSO [14]) with the local search accuracy of a deterministic derivativefree multi-objective (DFMO [15]) linesearch method.…”

Section: Introductionmentioning

confidence: 99%

“…Six formulations are proposed, based on two MODPSO formulations [14] and three DFMO activation criteria. Two of these are based on the particle velocity and one on the hypervolume metric [16].…”

Section: Introductionmentioning

confidence: 99%

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

Pellegrini

Serani

Liuzzi

et al. 2017

Lecture Notes in Computer Science

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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) lineasearch 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.

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Section: Algorithm Parameters and Setupmentioning

confidence: 99%

“…A number of 45 benchmark problems [14] is used, including convex and nonconvex, continuous and discontinuous Pareto fronts, with N of = 2, 3 and 1 ≤ N dv ≤ 12.…”

Section: Analytical Benchmark Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations