68 research outputs found
An Effective Ensemble Framework for Multi-Objective Optimization
This work was supported by the National Natural Science Foundation of China under Grants 61876110, 61876163, and 61836005, a grant from ANR/RGC Joint Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region, China and France National Research Agency (Project No. A-CityU101/16), the Joint Funds of the National Natural Science Foundation of China under Key Program Grant U1713212, and CONACyT grant no. 221551.Peer reviewedPostprin
Large Language Model for Multi-objective Evolutionary Optimization
Multiobjective evolutionary algorithms (MOEAs) are major methods for solving
multiobjective optimization problems (MOPs). Many MOEAs have been proposed in
the past decades, of which the search operators need a carefully handcrafted
design with domain knowledge. Recently, some attempts have been made to replace
the manually designed operators in MOEAs with learning-based operators (e.g.,
neural network models). However, much effort is still required for designing
and training such models, and the learned operators might not generalize well
on new problems. To tackle the above challenges, this work investigates a novel
approach that leverages the powerful large language model (LLM) to design MOEA
operators. With proper prompt engineering, we successfully let a general LLM
serve as a black-box search operator for decomposition-based MOEA (MOEA/D) in a
zero-shot manner. In addition, by learning from the LLM behavior, we further
design an explicit white-box operator with randomness and propose a new version
of decomposition-based MOEA, termed MOEA/D-LO. Experimental studies on
different test benchmarks show that our proposed method can achieve competitive
performance with widely used MOEAs. It is also promising to see the operator
only learned from a few instances can have robust generalization performance on
unseen problems with quite different patterns and settings. The results reveal
the potential benefits of using pre-trained LLMs in the design of MOEAs
Scalarizing Functions in Decomposition-Based Multiobjective Evolutionary Algorithms
Decomposition-based multiobjective evolutionary algorithms (MOEAs) have received increasing research interests due to their high performance for solving multiobjective optimization problems. However, scalarizing functions (SFs), which play a crucial role in balancing diversity and convergence in these kinds of algorithms, have not been fully investigated. This paper is mainly devoted to presenting two new SFs and analyzing their effect in decomposition-based MOEAs. Additionally, we come up with an efficient framework for decomposition-based MOEAs based on the proposed SFs and some new strategies. Extensive experimental studies have demonstrated the effectiveness of the proposed SFs and algorithm
The MOEADr Package – A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition
Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) represent a widely used class of population-based metaheuristics for the solution of multicriteria optimization problems. We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework. This approach contributes for easier reproducibility of existing MOEA/D variants from the literature, as well as for faster development and testing of new composite algorithms. The package offers an standardized, modular implementation of MOEA/D based on this framework, which was designed aiming at providing researchers and practitioners with a standard way to discuss and express MOEA/D variants. In this paper we introduce the design principles behind the MOEADr package, as well as its current components. Three case studies are provided to illustrate the main aspects of the package
Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functions
Decomposition has become an increasingly popular technique for evolutionary
multi-objective optimization (EMO). A decomposition-based EMO algorithm is
usually designed to approximate a whole Pareto-optimal front (PF). However, in
practice, the decision maker (DM) might only be interested in her/his region of
interest (ROI), i.e., a part of the PF. Solutions outside that might be useless
or even noisy to the decision-making procedure. Furthermore, there is no
guarantee to find the preferred solutions when tackling many-objective
problems. This paper develops an interactive framework for the
decomposition-based EMO algorithm to lead a DM to the preferred solutions of
her/his choice. It consists of three modules, i.e., consultation, preference
elicitation and optimization. Specifically, after every several generations,
the DM is asked to score a few candidate solutions in a consultation session.
Thereafter, an approximated value function, which models the DM's preference
information, is progressively learned from the DM's behavior. In the preference
elicitation session, the preference information learned in the consultation
module is translated into the form that can be used in a decomposition-based
EMO algorithm, i.e., a set of reference points that are biased toward to the
ROI. The optimization module, which can be any decomposition-based EMO
algorithm in principle, utilizes the biased reference points to direct its
search process. Extensive experiments on benchmark problems with three to ten
objectives fully demonstrate the effectiveness of our proposed method for
finding the DM's preferred solutions.Comment: 25 pages, 18 figures, 3 table
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