Ganesan T, Elamvazuthi I, Shaari Ku Zilati Ku, Vasant P
Department of Chemical Engineering, University Technology Petronas, 31750 Tronoh, Perak, Malaysia.
Department of Electrical & Electronic Engineering, University Technology Petronas, 31750 Tronoh, Perak, Malaysia.
ScientificWorldJournal. 2013 Dec 29;2013:859701. doi: 10.1155/2013/859701. eCollection 2013.
Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.
多目标（MO）优化是一个新兴领域，在全球许多领域中越来越多地被遇到。各种元启发式技术，如差分进化（DE）、遗传算法（GA）、引力搜索算法（GSA）和粒子群优化（PSO），已与加权和方法和法向边界交集（NBI）方法等标量化技术结合使用，以解决多目标问题。然而，仍然出现了许多挑战，特别是在处理具有多个目标的问题时（特别是在目标数量超过两个的情况下）。此外，在处理混合算法时会出现计算开销大的问题。本文通过提出一个替代框架来讨论这些问题，该框架利用与问题结构相关的算法概念来生成高效且有效的算法。本文提出了一个框架，用于生成计算开销最小的新的高性能多目标优化算法。
