基于交互进化分解的多目标优化方法综述。

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Evolutionary Computation Pub Date : 2025-01-14 DOI:10.1162/evco_a_00366
Giomara Lárraga, Kaisa Miettinen
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引用次数: 0

摘要

在多目标优化问题中,多个相互冲突的目标函数必须同时优化,交互式方法支持决策者找到最优解。这些方法允许决策者在求解过程中迭代地提供偏好信息,以找到感兴趣的解决方案,使他们能够了解问题中的权衡和偏好的可行性。文献中提出了几种交互式进化多目标优化方法。在进化计算界,所谓的基于分解的方法因其在具有许多目标函数的问题上的良好性能而越来越受欢迎。它们将多目标优化问题分解为多个子问题,并协同求解。已经提出了各种基于分解的交互式方法。然而,它们中的大多数都没有考虑到真正的交互式解决方案过程的理想特性,例如避免给决策者施加高认知负担,允许他们决定何时与方法交互,并支持他们选择最终解决方案。本文综述了基于交互进化分解的多目标优化方法,以及将交互性纳入其中的各种方法。此外,本文还确定了基于交互分解的多目标进化优化方法的理想特性,使其更容易应用于实际问题。
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Survey of interactive evolutionary decomposition-based multiobjective optimization methods.

Interactive methods support decision-makers in finding the most preferred solution for multiobjective optimization problems, where multiple conflicting objective functions must be optimized simultaneously. These methods let a decision-maker provide preference information iteratively during the solution process to find solutions of interest, allowing them to learn about the trade-offs in the problem and the feasibility of the preferences. Several interactive evolutionary multiobjective optimization methods have been proposed in the literature. In the evolutionary computation community, the so-called decomposition-basedmethods have been increasingly popular because of their good performance in problems with many objective functions. They decompose the multiobjective optimization problem into multiple sub-problems to be solved collaboratively. Various interactive versions of decomposition-based methods have been proposed. However, most of them do not consider the desirable properties of real interactive solution processes, such as avoiding imposing a high cognitive burden on the decision-maker, allowing them to decide when to interact with the method, and supporting them in selecting a final solution. This paper reviews interactive evolutionary decomposition-based multiobjective optimization methods and different methodologies utilized to incorporate interactivity in them. Additionally, desirable properties of interactive decomposition-based multiobjective evolutionary optimization methods are identified, aiming to make them easier to be applied in real-world problems.

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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
期刊最新文献
Quality Diversity under Sparse Interaction and Sparse Reward: Application to Grasping in Robotics. Runtime Analysis of Typical Decomposition Approaches in MOEA/D for Many-Objective Optimization Problems. Survey of interactive evolutionary decomposition-based multiobjective optimization methods. The Cost of Randomness in Evolutionary Algorithms: Crossover Can Save Random Bits. Territorial Differential Meta-Evolution: An Algorithm for Seeking All the Desirable Optima of a Multivariable Function.
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