Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

TLDR

Test functions incorporate features such as multimodality and deception that hinder convergence to the Pareto‑optimal front. The study systematically compares six evolutionary multiobjective optimization algorithms on test functions designed to isolate such features, aiming to predict which techniques are best suited for each problem type. The authors evaluate the algorithms on six carefully chosen test functions that each emphasize a particular difficulty in evolutionary optimization. The experiments reveal a clear algorithm hierarchy, confirm that the test functions provide sufficient complexity for comparison, and demonstrate that elitism markedly improves evolutionary multiobjective search.

Abstract

In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.