Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm
Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm
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DOI：
10.1007/s11047-011-9250-4
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发表时间：
2011-02
影响因子：
2.1
通讯作者：
Karthik Sindhya;K. Deb;K. Miettinen
中科院分区：
文献类型：
--
作者：
Karthik Sindhya;K. Deb;K. Miettinen
为了提高进化优化算法收敛到最优解的速度和精度，通常在进化优化算法中引入局部搜索方法。在多目标优化问题中，局部搜索的实现是一项重要的任务，因为在存在多个冲突目标的情况下确定局部搜索的目标变得困难。在本文中，我们借用了多准则决策的概念，采用一个参考点为基础的方法，最大限度地减少成就标量化函数，并将其作为一个搜索算子与并发的方法在进化多目标算法。几个两到四个目标的问题相比，串行方法的新的并行混合算法的仿真结果，清楚地显示了本地搜索的重要性，帮助计算更快，更准确地收敛到帕累托最优前沿。
A local search method is often introduced in an evolutionary optimization algorithm, to enhance its speed and accuracy of convergence to optimal solutions. In multi-objective optimization problems, the implementation of local search is a non-trivial task, as determining a goal for local search in presence of multiple conflicting objectives becomes a difficult task. In this paper, we borrow a multiple criteria decision making concept of employing a reference point based approach of minimizing an achievement scalarizing function and integrate it as a search operator with a concurrent approach in an evolutionary multi-objective algorithm. Simulation results of the new concurrent-hybrid algorithm on several two to four-objective problems compared to a serial approach, clearly show the importance of local search in aiding a computationally faster and accurate convergence to the Pareto optimal front.