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A two-stage evolutionary algorithm for large-scale sparse multiobjective optimization problems
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DOI：10.1016/j.swevo.2022.101093.png)
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There is evidence that many real-world applications can be characterized as sparse multiobjective problems (SMOPs), where most variables of their Pareto optimal solutions are zero. Existing multiobjective evolution-ary algorithms (MOEAs) have shown their competitiveness on conventional SMOPs. However, they may en-counter difficulties when tackling large-scale SMOPs (LSMOPs). This paper thereby proposes a two-stage MOEA tailored to LSMOPs, named TS-SparseEA. TS-SparseEA integrates the prior information into the evolution and enables the population to spread over the Pareto front by two stages. In the first stage, TS-SparseEA adopts a new binary weight optimization framework, transforming the original large-scale optimization problem into a low-dimensional one via a set of low-dimensional binary weights. In the second stage, TS-SparseEA employs an improved evolutionary algorithm, including a hybrid encoding and a specialized matching strategy, where each solution is reproduced by a conditional combination between two types of variables. To summarize, the proposed binary weight optimization can better address large-scale sparse variables by generating a high-quality initial population, whereas the new hybrid encoding can facilitate the offspring evolution. Extensive experiments have verified the effectiveness of TS-SparseEA on LSMOPs, by comparing it with several state-of-the-art MOEAs on both benchmark problems and real-world applications.
Keyword:
Evolutionary algorithm
Large-scale multiobjective problems
Sparse Pareto optimal solutions
Two-stage optimization
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