群智能中的多目标优化问题综述

R. Caruana, Yin Lou
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引用次数: 2

摘要

现实生活中的各种挑战都是多目标且相互冲突的(即改变并发优化)。这意味着一个目标是基于另一个目标的成本进行优化的。多目标优化(MOO)问题既具有挑战性,又具有潜在的现实意义,由于其广泛的应用,优化挑战已经被不同学术基础的研究广泛分析。因此,这产生了减轻这些挑战的不同方法。关于处理MOO挑战的方法有广泛的文献。重要的是要记住,每种技术都有其优点和局限性,在典型的情况下,没有最佳的替代方案。MOO挑战可以在各个领域进行识别,例如路径优化、飞机设计、汽车设计和金融等。这篇文章介绍了当前MOO挑战的调查和缓解这些挑战的群体智能方法。这篇文章的主要目的是为理解MOO挑战提供一个基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey on Multi Objective Optimization Challenges in Swarm Intelligence
Various challenges in real life are multi-objective and conflicting (i.e., alter concurrent optimization). This implies that a single objective is optimized based on another’s cost. The Multi-Objective Optimization (MOO) issues are challenging but potentially realistic, and due to their wide-range application, optimization challenges have widely been analyzed by research with distinct scholarly bases. Resultantly, this has yielded distinct approaches for mitigating these challenges. There is a wide-range literature concerning the approaches used to handle MOO challenges. It is important to keep in mind that each technique has its pros and limitations, and there is no optimum alternative for cure searchers in a typical scenario. The MOO challenges can be identified in various segments e.g., path optimization, airplane design, automobile design and finance, among others. This contribution presents a survey of prevailing MOO challenges and swarm intelligence approaches to mitigate these challenges. The main purpose of this contribution is to present a basis of understanding on MOO challenges.
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