Preference-Based Multi-Objective Optimization with Gaussian Process
Preference-Based Multi-Objective Optimization with Gaussian Process
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DOI：
10.1109/smc53992.2023.10394212
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Tian Huang;Ke Li
中科院分区：
文献类型：
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作者：
Tian Huang;Ke Li
传统的进化多目标优化（EMO）算法是在Pareto前沿（PF）上产生一组非支配解。然而，这种技术的福尔斯短交付的结果多目标优化问题（MOPs）包含用户的喜好。在本文中，我们提出了一种新的EMO算法，通过决策者（DM）将用户的喜好。我们的方法包括三个模块：咨询，偏好诱导和优化。DM采用高斯过程（GP）进行咨询和偏好获取，提供偏好信息.我们采用基于分解的EMO算法（即，MOEA/D）进行优化。实验包括两个阶段。首先，我们模拟的决策者模块与GP。其次，我们模拟我们提出的方法，并比较其性能与现有的交互式优化算法。我们的研究提出了一种新的基于偏好的EMO算法，解决了传统技术的缺点，并为多目标优化解锁了新的可能性。
Traditional evolutionary multi-objective optimization (EMO) algorithm is to generate a set of non-dominated solutions on the Pareto front (PF). However, this technique falls short of delivering the outcomes for multi-objective optimization problems (MOPs) containing user preference. In this paper, we present a novel EMO algorithm that incorporates user preferences via a decision maker (DM). Our approach comprises three modules: consultation, preference elicitation and optimization. The DM undertakes the consultation and preference elicitation using Gaussian process (GP) to provide preference information. We employ the decomposition-based EMO algorithm (i.e., MOEA/D) for optimization. The experiment comprises two sessions. Firstly, we simulate the decision maker module with GP. Secondly, we simulate our proposed method and compare its performance with existing interactive optimization algorithms. Our research proposes a new preference-based EMO algorithm that addresses the shortcomings of traditional techniques and unlocks new possibilities for multi-objective optimization.