Improving NSGA-II with an Adaptive Mutation Operator

TL;DR

This paper introduces an adaptive mutation operator for NSGA-II that uses diversity feedback to dynamically adjust mutation magnitude, enhancing Pareto front convergence and solution diversity.

Contribution

It proposes a novel adaptive mutation operator for NSGA-II that improves performance by using diversity-based feedback to control mutation magnitude.

Findings

Enhanced ability to reach Pareto optimal front

Improved diversity among final solutions

Adaptive mutation outperforms fixed strategies

Abstract

The performance of a Multiobjective Evolutionary Algorithm (MOEA) is crucially dependent on the parameter setting of the operators. The most desired control of such parameters presents the characteristic of adaptiveness, i.e., the capacity of changing the value of the parameter, in distinct stages of the evolutionary process, using feedbacks from the search for determining the direction and/or magnitude of changing. Given the great popularity of the algorithm NSGA-II, the objective of this research is to create adaptive controls for each parameter existing in this MOEA. With these controls, we expect to improve even more the performance of the algorithm. In this work, we propose an adaptive mutation operator that has an adaptive control which uses information about the diversity of candidate solutions for controlling the magnitude of the mutation. A number of experiments considering different problems suggest that this mutation operator improves the ability of the NSGA-II for reaching the Pareto optimal Front and for getting a better diversity among the final solutions.