Knee Point Identification Based on Voronoi Diagram

Nie, Haifeng; Gao, Huiru; Li, Ké

doi:10.1109/smc42975.2020.9283262

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…In view of the black box nature of real-world optimization problems, the explainability have rarely been explored in the literature. It is worthwhile to investigate the interpretability of the obtained SOI along with the trade-off among conflicting objectives [110][111][112][113][114]. This can facilitate the understanding of the DM's latent preference information and further advance a better informed MCDM.…”

Section: Discussionmentioning

confidence: 99%

Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank

Li¹,

Lai²,

Yao³

2022

Preprint

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In practical multi-criterion decision-making, it is cumbersome if a decision maker (DM) is asked to choose among a set of trade-off alternatives covering the whole Pareto-optimal front. This is a paradox in conventional evolutionary multi-objective optimization (EMO) that always aim to achieve a well balance between convergence and diversity. In essence, the ultimate goal of multiobjective optimization is to help a decision maker (DM) identify solution(s) of interest (SOI) achieving satisfactory trade-offs among multiple conflicting criteria. Bearing this in mind, this paper develops a framework for designing preference-based EMO algorithms to find SOI in an interactive manner. Its core idea is to involve human in the loop of EMO. After every several iterations, the DM is invited to elicit her feedback with regard to a couple of incumbent candidates. By collecting such information, her preference is progressively learned by a learning-to-rank neural network and then applied to guide the baseline EMO algorithm. Note that this framework is so general that any existing EMO algorithm can be applied in a plug-in manner. Experiments on 48 benchmark test problems with up to 10 objectives fully demonstrate the effectiveness of our proposed algorithms for finding SOI.

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Section: Discussionmentioning

confidence: 99%

Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank

Li¹,

Lai²,

Yao³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…N t FE is the number of FEs required by one of the other peer algorithms that leverages historical information to a certain extent to achieve f (x * c , t). In practice, the corresponding knowledge transfer approach is regarded as effective when 0 < ρ t < 1 (the smaller ρ t is, the better knowledge transfer is); otherwise it is negative to the underlying baseline optimization routine [74][75][76].…”

Section: Methodsmentioning

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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“…Furthermore, trade-off alternative policies outside the region of interest can introduce irrelevant or noisy information during the decision-making process. In the sibling area of evolutionary multi-objective optimization and MCDM, our group has made a series of contributions towards preference-based methods in a priori [36][37][38], a posterior [39][40][41][42], and interactive [43][44][45] manners. In particular, our previous study has empirically demonstrated the effectiveness of leveraging user preferences in the search of solutions of interest [46].…”

Section: Irl and Preference Learningmentioning

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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Knee Point Identification Based on Voronoi Diagram

Cited by 5 publications

References 29 publications

Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank

Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank

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

An Improved Multi-objective Optimization Algorithm Based on Reinforcement Learning

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