KNN-Averaging for Noisy Multi-objective Optimisation

TL;DR

This paper introduces kNN-Avg, a method that mitigates the effects of noisy fitness functions in multi-objective optimisation by averaging the nearest neighbors' values, improving solution reliability without repeated evaluations.

Contribution

The paper proposes a novel kNN-Avg technique that reduces noise impact in multi-objective optimisation, especially useful for cyber-physical systems with stochastic fitness evaluations.

Findings

kNN-Avg produces solutions closer to true expected fitness values

The method performs well on benchmark problems

It reduces the need for repeated evaluations of solutions

Abstract

Multi-objective optimisation is a popular approach for finding solutions to complex problems with large search spaces that reliably yields good optimisation results. However, with the rise of cyber-physical systems, emerges a new challenge of noisy fitness functions, whose objective value for a given configuration is non-deterministic, producing varying results on each execution. This leads to an optimisation process that is based on stochastically sampled information, ultimately favouring solutions with fitness values that have co-incidentally high outlier noise. In turn, the results are unfaithful due to their large discrepancies between sampled and expectable objective values. Motivated by our work on noisy automated driving systems, we present the results of our ongoing research to counteract the effect of noisy fitness functions without requiring repeated executions of each solution. Our method kNN-Avg identifies the k-nearest neighbours of a solution point and uses the weighted average value as a surrogate for its actually sampled fitness. We demonstrate the viability of kNN-Avg on common benchmark problems and show that it produces comparably good solutions whose fitness values are closer to the expected value.