Multiobjective firefly algorithm for continuous optimization
Multiobjective firefly algorithm for continuous optimization
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DOI：
10.1007/s00366-012-0254-1
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发表时间：
2012-01
影响因子：
8.7
通讯作者：
Xin-She Yang
中科院分区：
文献类型：
--
作者：
Xin-She Yang
工业工程中的设计问题通常涉及在复杂的非线性约束下具有多个目标的大量设计变量。多目标问题的算法可能与单目标优化的方法有很大不同。要找到非线性多目标优化问题的帕累托前沿和非支配集可能需要大量的计算工作，即使对于看似简单的问题也是如此。元启发式算法开始显示出其在处理多目标优化方面的优势。在本文中，我们扩展了最近开发的萤火虫算法来解决多目标优化问题。我们使用选定的测试函数子集来验证所提出的方法，然后将其应用于解决设计优化基准。我们将讨论我们的结果并提供进一步研究的主题。
Design problems in industrial engineering often involve a large number of design variables with multiple objectives, under complex nonlinear constraints. The algorithms for multiobjective problems can be significantly different from the methods for single objective optimization. To find the Pareto front and non-dominated set for a nonlinear multiobjective optimization problem may require significant computing effort, even for seemingly simple problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we extend the recently developed firefly algorithm to solve multiobjective optimization problems. We validate the proposed approach using a selected subset of test functions and then apply it to solve design optimization benchmarks. We will discuss our results and provide topics for further research.