Learning from Non-Stationary Stream Data in Multiobjective Evolutionary Algorithm

TL;DR

This paper introduces an online learning approach for multiobjective evolutionary algorithms to adaptively discover the structure of Pareto optimal solutions in non-stationary streams, improving performance on complex benchmark problems.

Contribution

It proposes an online agglomerative clustering method to efficiently learn the structure of Pareto solutions, reducing computational costs and enhancing MOEA performance.

Findings

Significant improvement over five state-of-the-art MOEAs

Effective handling of non-stationary, stream-like data

Better solutions on complex benchmark problems

Abstract

Evolutionary algorithms (EAs) have been well acknowledged as a promising paradigm for solving optimisation problems with multiple conflicting objectives in the sense that they are able to locate a set of diverse approximations of Pareto optimal solutions in a single run. EAs drive the search for approximated solutions through maintaining a diverse population of solutions and by recombining promising solutions selected from the population. Combining machine learning techniques has shown great potentials since the intrinsic structure of the Pareto optimal solutions of an multiobjective optimisation problem can be learned and used to guide for effective recombination. However, existing multiobjective EAs (MOEAs) based on structure learning spend too much computational resources on learning. To address this problem, we propose to use an online learning scheme. Based on the fact that offsprings along evolution are streamy, dependent and non-stationary (which implies that the intrinsic structure, if any, is temporal and scale-variant), an online agglomerative clustering algorithm is applied to adaptively discover the intrinsic structure of the Pareto optimal solution set; and to guide effective offspring recombination. Experimental results have shown significant improvement over five state-of-the-art MOEAs on a set of well-known benchmark problems with complicated Pareto sets and complex Pareto fronts.