揭示多目标优化的多功能性与影响力：求解复杂现实问题的算法、应用与趋势
Unraveling the Versatility and Impact of Multi-Objective Optimization: Algorithms, Applications, and Trends for Solving Complex Real-World Problems
AI总结：
本文综述多目标优化（MOO）的近期算法、应用、趋势与问题，介绍其核心概念与现实决策价值，指出无通用最优策略，需结合问题、资源与目标选择方法，推动MOO研究发展。
摘要：
近年来，多目标优化（Multi-Objective Optimization, MOO）技术因具备求解物流、金融、环境管理、工程等多个领域现实问题的潜力而日益流行。这类技术能提供传统单目标方法无法给出的综合性解决方案。由于创新算法数量众多，研究人员往往难以选出适配自身问题的最优算法。本文梳理了近期开发的基于MOO的算法，介绍了MOO的基本概念，同时阐述了帕累托最优（Pareto optimality）与权衡分析（trade-off analysis）相关内容。在现实案例研究中，MOO算法可应对复杂的决策挑战。本文考察了多目标优化研究中的算法方法、应用场景、发展趋势及现存问题，这一详尽综述阐释了MOO算法的原理、实现方法及其在现实问题中的应用，旨在推动MOO研究的进一步发展。研究表明，不存在绝对最优的单一策略，特定方法的选择取决于实际优化问题、可用计算资源以及优化任务的具体目标。
英文摘要：
Multi-Objective Optimization (MOO) techniques have become increasingly popular in recent years due to their potential for solving real-world problems in various fields, such as logistics, finance, environmental management, and engineering. These techniques offer comprehensive solutions that traditional single-objective approaches fail to provide. Due to the many innovative algorithms, it has been challenging for researchers to choose the optimal algorithms for solving their problems. This paper examines recently developed MOO-based algorithms. MOO is introduced along with Pareto optimality and trade-off analysis. In real-world case studies, MOO algorithms address complicated decision-making challenges. This paper examines algorithmic methods, applications, trends, and issues in multi-objective optimization research. This exhaustive review explains MOO algorithms, their methods, and their applications to real-world problems. This paper aims to contribute further advancements in MOO research. No singular strategy is superior; instead, selecting a particular method depends on the natural optimization problem, the computational resources available, and the specific objectives of the optimization tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
