MOCell: A cellular genetic algorithm for multiobjective optimization
MOCell: A cellular genetic algorithm for multiobjective optimization
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DOI：
10.1002/int.20358
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发表时间：
2009-07
影响因子：
7
通讯作者：
Antonio J. Nebro;J. Durillo;F. Luna;B. Dorronsoro;E. Alba
中科院分区：
文献类型：
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作者：
Antonio J. Nebro;J. Durillo;F. Luna;B. Dorronsoro;E. Alba
本文介绍了一种新的细胞遗传算法来解决多目标连续优化问题。我们的方法的特点是使用外部档案来存储非支配解决方案和反馈机制，其中该档案中的解决方案在每次迭代后随机替换群体中的现有个体。结果是一个简单且精英主义的算法，称为 MOCell。我们的建议已经通过约束和非约束问题进行了评估，并与 NSGA-II 和 SPEA2（两种最先进的进化多目标优化器）进行了比较。对于所研究的基准，我们的实验表明 MOCell 在收敛性和超体积方面获得了有竞争力的结果，并且在 Pareto 前沿解的多样性方面，它明显优于其他两种比较算法。 © 2009 Wiley 期刊公司。
This paper introduces a new cellular genetic algorithm for solving multiobjective continuous optimization problems. Our approach is characterized by using an external archive to store nondominated solutions and a feedback mechanism in which solutions from this archive randomly replace existing individuals in the population after each iteration. The result is a simple and elitist algorithm called MOCell. Our proposal has been evaluated with both constrained and unconstrained problems and compared against NSGA‐II and SPEA2, two state‐of‐the‐art evolutionary multiobjective optimizers. For the studied benchmark, our experiments indicate that MOCell obtains competitive results in terms of convergence and hypervolume, and it clearly outperforms the other two compared algorithms concerning the diversity of the solutions along the Pareto front. © 2009 Wiley Periodicals, Inc.