Dealing with Structure Constraints in Evolutionary Pareto Set Learning

• URL: http://arxiv.org/abs/2310.20426v4
• Date: Mon, 29 Apr 2024 16:09:08 GMT
• Title: Dealing with Structure Constraints in Evolutionary Pareto Set Learning
• Authors: Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang, Qingfu Zhang,
• Abstract summary: In many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set. We make the first attempt to incorporate the structure constraints into the whole solution set by a single Pareto set model. With our proposed method, the decision-makers can flexibly trade off the optimality with preferred structures among all solutions.
• Score: 11.242036067940289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run, each with its own structure. However, in many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set, which define the patterns shared among all solutions. The current population-based MOEAs cannot properly handle such requirements. In this work, we make the first attempt to incorporate the structure constraints into the whole solution set by a single Pareto set model, which can be efficiently learned by a simple evolutionary stochastic optimization method. With our proposed method, the decision-makers can flexibly trade off the Pareto optimality with preferred structures among all solutions, which is not supported by previous MOEAs. A set of experiments on benchmark test suites and real-world application problems fully demonstrates the efficiency of our proposed method.

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