### Convergence Acceleration Operator for Multiobjective Optimization

Abstract

A convergence acceleration operator (CAO) is described which enhances
the search capability and the speed of convergence of the host multiobjective
optimization algorithm. The operator acts directly in the objective space to
suggest improvements to solutions obtained by a multiobjective evolutionary
algorithm (MOEA). The suggested improved objective vectors are then mapped
into the decision variable space and tested. This method improves upon prior
work in a number of important respects, such as mapping technique and solution
improvement. Further, the paper discusses implications for many-objective problems
and studies the impact of the use of the CAO as the number of objectives increases.
The CAO is incorporated with two leading MOEAs, the non-dominated sorting genetic
algorithm and the strength Pareto evolutionary algorithm and tested. Results show
that the hybridized algorithms consistently improve the speed of convergence of the
original algorithm while maintaining the desired distribution of solutions. It is shown
that the operator is a transferable component that can be hybridized with any MOEA.