Abstract
A key idea in manyobjective optimization is to approximate the optimal Pareto front using a set of representative nondominated solutions. The produced solution set should be close to the optimal front (convergence) and welldiversified (diversity). Recent studies have shown that measuring both convergence and diversity depends on the shape (or curvature) of the Pareto front. In recent years, researchers have proposed evolutionary algorithms that model the shape of the nondominated front to define environmental selection strategies that adapt to the underlying geometry. This paper proposes a novel method for nondominated front modeling using the NewtonRaphson iterative method for roots finding. Second, we compute the distance (diversity) between each pair of nondominated solutions using geodesics, which are generalizations of the distance on Riemann manifolds (curved topological spaces). We have introduced an evolutionary algorithm within the Adaptive Geometry Estimation based MOEA (AGEMOEA) framework, which we called AGEMOEAII. Computational experiments with 17 problems from the WFG and SMOP benchmarks show that AGEMOEAII outperforms its predecessor AGEMOEA as well as other stateoftheart manyobjective algorithms, i.e., NSGAIII, MOEA/D, VaEA, and LMEA.
Original language  English 

Title of host publication  The Genetic and Evolutionary Computation Conference 
Publisher  Association for Computer Machinery 
Publication status  Accepted/In press  25 Mar 2022 
Keywords
 Evolutionary algorithms
 Multiobjective Optimisation
 NewtonRaphson (NR) method
 Geodesic distance
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Source code of "An Improved Pareto Front Modeling Algorithm for Largescale ManyObjective Optimization"
Panichella, A. (Creator), TU Delft  4TU.ResearchData, 2022
https://zenodo.org/record/6462859
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