PAMELI: A Meta-Algorithm for Computationally Expensive Multi-Objective Optimization Problems

TL;DR

This paper introduces PAMELI, a meta-algorithm for efficiently solving computationally expensive multi-objective optimization problems by adaptively selecting surrogate models and search strategies, reducing the need for costly evaluations.

Contribution

PAMELI is a novel meta-algorithm that dynamically adapts surrogate models and navigation strategies during optimization of expensive multi-objective problems.

Findings

Outperforms a state-of-the-art surrogate-assisted evolutionary algorithm on benchmark problems.

Effectively reduces the number of expensive function evaluations needed.

Demonstrates adaptability in surrogate model selection and search strategy.

Abstract

We present an algorithm for multi-objective optimization of computationally expensive problems. The proposed algorithm is based on solving a set of surrogate problems defined by models of the real one, so that only solutions estimated to be approximately Pareto-optimal are evaluated using the real expensive functions. Aside of the search for solutions, our algorithm also performs a meta-search for optimal surrogate models and navigation strategies for the optimization landscape, therefore adapting the search strategy for solutions to the problem as new information about it is obtained. The competitiveness of our approach is demonstrated by an experimental comparison with one state-of-the-art surrogate-assisted evolutionary algorithm on a set of benchmark problems.