论文标题
多目标甲虫天线搜索算法
Multi-objective beetle antennae search algorithm
论文作者
论文摘要
在工程优化问题中，通常需要在高度非线性约束下进行大量变量的多个目标。需要大量的计算工作才能找到非线性多目标优化问题的帕累托正面。基于群体智能的荟萃催化算法已成功应用于解决多目标优化问题。最近，提出了一种基于智能的算法，称为甲虫天线搜索算法。事实证明，该算法在计算上更有效。因此，我们扩展了该算法以解决多目标优化问题。提出的多目标甲虫天线搜索算法使用四个精心挑选的基准功能进行了测试，并将其性能与其他多目标优化算法进行比较。结果表明，所提出的多目标甲虫天线搜索算法具有更高的计算效率，精度令人满意。
In engineering optimization problems, multiple objectives with a large number of variables under highly nonlinear constraints are usually required to be simultaneously optimized. Significant computing effort are required to find the Pareto front of a nonlinear multi-objective optimization problem. Swarm intelligence based metaheuristic algorithms have been successfully applied to solve multi-objective optimization problems. Recently, an individual intelligence based algorithm called beetle antennae search algorithm was proposed. This algorithm was proved to be more computationally efficient. Therefore, we extended this algorithm to solve multi-objective optimization problems. The proposed multi-objective beetle antennae search algorithm is tested using four well-selected benchmark functions and its performance is compared with other multi-objective optimization algorithms. The results show that the proposed multi-objective beetle antennae search algorithm has higher computational efficiency with satisfactory accuracy.
