Performance Study of Surrogate-Assisted Large-Scale Multiobjective Evolutionary Algorithms on GLSMOP Test Suite

Haoran Gu, Cheng He, Handing Wang

Abstract: Recently, some studies have shown that the popular large-scale multiobjective optimization problem (LSMOP) test suite cannot fairly test the performance of algorithms due to the specificity of its Pareto solution set position. Specifically, some large-scale multiobjective evolutionary algorithms (LSMOEAs) have achieved completely opposite (poor) performance on the GLSMOP test suite (the LSMOP test suite with generic Pareto solution sets). Since many real-world LSMOPs are computationally expensive, several surrogate-assisted LSMOEAs are developed and their effectiveness is verified on the LSMOP test suite. Motivated by the above, we aim to study the performance of those surrogate-assisted LSMOEAs on the GLSMOP test suite in this work. Firstly, we elaborate on the existing surrogate-assisted LSMOEAs for solving the expensive LSMOPs from different perspectives. Secondly, the basic formulation of the GLSMOP test suite and its difference from the original LSMOP test suite are shown. Finally, the performance of six surrogate-assisted LSMOEAs on the GLSMOP test suite is systematically tested. According to the experimental results, we give the current best algorithmic structure for handling expensive LSMOPs: using a decomposition-based framework, using differential evolution to search the original decision space, and fitting a scalarization function with the surrogate model. The proposed algorithmic structure is simple but is expected to guide the design of effective surrogate-assisted LSMOEAs.