Handling multiple objectives with particle swarm optimization
Handling multiple objectives with particle swarm optimization
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DOI：
10.1109/tevc.2004.826067
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发表时间：
2004-06-01
影响因子：
14.3
通讯作者：
Lechuga, MS
中科院分区：
文献类型：
--
作者：
Coello, CAC;Pulido, GT;Lechuga, MS
本文提出了一种将Pareto优势引入粒子群优化算法的方法，以使该启发式算法能够处理具有多个目标函数的问题。与当前扩展PSO来解决多目标优化问题的其他建议不同，我们的算法使用第二(即外部)粒子库，稍后其他粒子将使用该库来指导自己的飞行。我们还加入了一个特殊的变异算子，丰富了算法的探索能力。用进化多目标优化标准文献中的几个测试函数和度量对所提出的方法进行了验证。结果表明，该方法具有很强的竞争性，可以被认为是解决多目标优化问题的一种可行方案。
This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm. The proposed approach is validated using several test functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.