论文信息 - The Benchmark TEAM Problem for Multi-Objective Optimization Solved with CFSO

The Benchmark TEAM Problem for Multi-Objective Optimization Solved with CFSO

In this paper, new solutions to a benchmark TEAM problem for multi-objective optimization are given. The benchmark was recently proposed, and it is the only one dealing with multi-objective optimization among the TEAM benchmark problems. For solving this problem, an algorithm based on swarm intelligence, the CFSO algorithm, is used. The results are compared with those obtained with the well-known algorithm NSGA-II.

[1] Antonino Laudani,et al. Accurate design of Helmholtz coils for ELF Bioelectromagnetic interaction by means of continuous FSO , 2011 .

[2] Maria Evelina Mognaschi,et al. Field-based optimal-design of an electric motor: a new sensitivity formulation , 2017 .

[3] Ermanno Cardelli,et al. Pattern search approach to ferromagnetic material modelling , 2019 .

[4] Silvia Conforto,et al. CFSO3: A New Supervised Swarm-Based Optimization Algorithm , 2013 .

[5] Maria Evelina Mognaschi,et al. Micro biogeography-inspired multi-objective optimisation for industrial electromagnetic design , 2017 .

[6] Gabriele Maria Lozito,et al. Swarm/flock optimization algorithms as continuous dynamic systems , 2014, Appl. Math. Comput..

[7] Alessandro Salvini,et al. Closed Forms for the Fully-Connected Continuous Flock of Starlings Optimization Algorithm , 2013, 2013 UKSim 15th International Conference on Computer Modelling and Simulation.

[8] Antonino Laudani,et al. Two-dimensional magnetic modeling of ferromagnetic materials by using a neural networks based hybrid approach , 2016 .

[9] Gabriele Maria Lozito,et al. On the generalization capabilities of the ten-parameter jiles-atherton model , 2015 .

[10] Gabriele Maria Lozito,et al. A Neural-FEM tool for the 2-D magnetic hysteresis modeling , 2016 .

[11] Antonio Faba,et al. Vector hysteresis model identification for iron-silicon thin films from micromagnetic simulations , 2016 .