Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-objective Optimization?

Li, Shuang; Li, Ké; Li, Wei

doi:10.1007/978-3-031-14721-0_9

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…Another missing, yet important issue, is how to handle constraints in the context of multiple objectives [78,79]. Things become even more challenging when the constraints are (partially) unobservable [80,81]. It is also worth noting that evolutionary computation and multi-objective optimization have been successfully applied to solve real-world problems, e.g., natural language processing [82], neural architecture search [83][84][85][86], robustness of neural networks [87][88][89][90][91][92], software engineering [93][94][95][96][97], smart grid management [2,98,99], communication networks [100][101][102][103], machine learning [104][105][106][107][108], and visualization [109].…”

Section: Discussionmentioning

confidence: 99%

An Improved Multi-objective Optimization Algorithm Based on Reinforcement Learning

Liu

Zhou

Qiu

et al. 2022

Lecture Notes in Computer Science

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Multi-objective reinforcement learning (MORL) aims to find a set of high-performing and diverse policies that address trade-offs between multiple conflicting objectives. However, in practice, decision makers (DMs) often deploy only one or a limited number of trade-off policies. Providing too many diversified trade-off policies to the DM not only significantly increases their workload but also introduces noise in multi-criterion decision-making. With this in mind, we propose a humanin-the-loop policy optimization framework for preference-based MORL that interactively identifies policies of interest. Our method proactively learns the DM's implicit preference information without requiring any a priori knowledge, which is often unavailable in real-world black-box decision scenarios. The learned preference information is used to progressively guide policy optimization towards policies of interest. We evaluate our approach against three conventional MORL algorithms that do not consider preference information and four state-of-the-art preference-based MORL algorithms on two MORL environments for robot control and smart grid management. Experimental results fully demonstrate the effectiveness of our proposed method in comparison to the other peer algorithms.

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Section: Discussionmentioning

confidence: 99%

An Improved Multi-objective Optimization Algorithm Based on Reinforcement Learning

Liu

Zhou

Qiu

et al. 2022

Lecture Notes in Computer Science

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“…This observation implies that the transfer learning approaches used in other peer algorithms might lead to negative effect at the early stage of the hyperparameter optimization at each time step. Whereas a simple restarting from scratch is more robust at the early stage while its performance might be stagnated afterwards [125][126][127][128].…”

Section: Real-world Dynamic Optimizationmentioning

confidence: 99%

A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments

Li¹,

Chen²,

Yao³

2022

Preprint

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Many real-world problems are usually computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach for tackling expensive black-box optimization problems in a static environment whereas it has rarely been studied under dynamic environments. This paper proposes a simple but effective transfer learning framework to empower data-driven evolutionary optimization to solve dynamic optimization problems. Specifically, it applies a hierarchical multi-output Gaussian process to capture the correlation between data collected from different time steps with a linearly increased number of hyperparameters. Furthermore, an adaptive source task selection along with a bespoke warm staring initialization mechanisms are proposed to better leverage the knowledge extracted from previous optimization exercises. By doing so, the data-driven evolutionary optimization can jump start the optimization in the new environment with a strictly limited computational budget. Experiments on synthetic benchmark test problems and a real-world case study demonstrate the effectiveness of our proposed algorithm against nine state-of-the-art peer algorithms.

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Evolutionary Constrained Multiobjective Optimization: Scalable High-Dimensional Constraint Benchmarks and Algorithm

Qiao

Yue

et al. 2024

IEEE Trans. Evol. Computat.

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Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-objective Optimization?

Cited by 4 publications

References 43 publications

An Improved Multi-objective Optimization Algorithm Based on Reinforcement Learning

An Improved Multi-objective Optimization Algorithm Based on Reinforcement Learning

A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments

Evolutionary Constrained Multiobjective Optimization: Scalable High-Dimensional Constraint Benchmarks and Algorithm

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