An analysis of SAT constrained MNK-landscapes as benchmark problems for multi-objective evolutionary algorithms

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-04-22 DOI:10.1016/j.swevo.2025.101933

Felipe Honjo Ide , Hernan Aguirre , Minami Miyakawa , Darrel Whitley

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引用次数: 0

Abstract

Benchmark problems have been fundamental in advancing our understanding of the dynamics and design of multi-objective evolutionary optimization algorithms. Within the binary domain, there is a lack of multi-objective benchmark problems that can help further research on constrained optimization. This paper presents highly configurable benchmark problems for constrained binary multi-objective optimization combining SAT Constraints, constructed from satisfiability clauses, and MNK-Landscapes. The benchmark problems are scalable in the number of equality and inequality constraints, feasibility-hardness, number of objectives, number of variables, and epistasis between variables. This paper studies how SAT Constraints affect the distribution of feasible solutions in objective and decision spaces and illustrates their impact on the performance and dynamics of multi-objective evolutionary algorithms when solving SAT Constrained MNK-Landscapes.

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基于SAT约束的mnk -景观多目标进化算法基准问题分析

基准问题是促进我们对多目标进化优化算法的动力学和设计的理解的基础。在二值域内，缺乏有助于进一步研究约束优化的多目标基准问题。本文提出了一种高度可配置的约束二元多目标优化基准问题，该问题结合了由可满足性子句构造的SAT约束和mnk - landscape。基准问题在等式和不等式约束的数量、可行性-硬度、目标数量、变量数量和变量之间的上位性方面是可扩展的。本文研究了SAT约束如何影响目标空间和决策空间中可行解的分布，并说明了它们在求解SAT约束mnk - landscape时对多目标进化算法的性能和动力学的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.