TY - JOUR
ID - 4313
TI - A Robust Proportion-Preserving Composite Objective Function for Scale-Invariant Multi-Objective Optimization
JO - Scientia Iranica
JA - SCI
LA - en
SN - 1026-3098
AU - Daneshmand, Morteza
AU - Tale Masouleh, Mehdi
AU - Saadatzi, Mohammad Hossein
AU - Ozcinar, Cagri
AU - Anbarjafari, Gholamreza
AD - iCV Group, Institute of Technology, University of Tartu, Tartu 50411, Estonia
AD - Human and Robot Interaction Laboratory, Faculty of New Sciences and
Technologies, University of Tehran, Tehran, Iran
AD - Mechanical Engineering Department, Colorado School of Mines, USA
AD - Telecom ParisTech, Paris, France
Y1 - 2017
PY - 2017
VL - 24
IS - 6
SP - 2977
EP - 2991
KW - Proportion-Preserving Composite Objective Function (PPCOF)
KW - multi-objective optimization
KW - Pareto-optimal set of solutions
KW - Non-dominated Sorting Genetic Algorithm II (NSGI-II)
KW - Planar Parallel Mechanisms (PPMs)
DO - 10.24200/sci.2017.4313
N2 - This paper aims at introducing a proportion-preserving composite ob-jective function for multi-objective optimization, namely, PPCOF, and validating its eciency through demonstrating its applicability to opti- mization of the kinetostatic performance of planar parallel mechanisms. It exempts the user from both specifying preference factors and conduct- ing decision-making. It consists of two terms. The rst one adds the normalized objective functions up, where the extrema are resulted from single-objective optimization. To making the composite objective func- tion steer the variations of the objective functions while preserving ra- tional proportions between them, as the main contribution of the paper, it is sought that the normalized objective functions take closely similar values, to which end, they are juxtaposed inside a vector, which is then scaled such that its Euclidean norm-2 is equal to that of the vector of all ones with the same dimensions, and then the second term is constructed as the addition of penalty factors standing for the absolute value of the dierence between each element of the foregoing vector from 1. Based on the experimental results, with a considerably smaller computational cost, the PPCOF obtains an optimal solution that is not dominated by any point from a set of Pareto-optimal solutions oered by NSGA-II.
UR - http://scientiairanica.sharif.edu/article_4313.html
L1 - http://scientiairanica.sharif.edu/article_4313_d6fd2a81400e46ec6e61fd95bf213ac6.pdf
ER -