%0 Journal Article
%T 求解约束优化问题的改进樽海鞘算法<br>An Improved Salp Swarm Algorithm for Con-strained Optimization Problems
%A 李端洋
%A 史兰艳
%J Advances in Applied Mathematics
%P 2128-2137
%@ 2324-8009
%D 2023
%I Hans Publishing
%R 10.12677/AAM.2023.125216
%X 针对约束优化问题，提出了一种运用改进樽海鞘算法求解约束优化问题的方法。通过外点法将约束优化问题转化为一系列无约束优化问题，然后运用双领导者结合败者淘汰策略的樽海鞘算法(DLSSA)，并对所得界约束优化问题进行求解以获得约束问题的解。利用6个约束优化基准测试问题对所得算法进行数值实验，实验结果表明，对于所有问题算法，PF-DLSSA所求解均优于算法PF-SSA所求解，即实验所得的最优值及其他数据都优于对比算法PF-SSA所求，所得算法能够有效地求解约束优化问题，且比对比算法效果要好。<br />
Aiming at the constrained optimization problem, an improved salp swarm algorithm was proposed to solve the constrained optimization problem. The constrained optimization problem is trans-formed into a series of unconstrained optimization problems by the outer point method, and then the salp swarm algorithm (DLSSA), which combines the two-leader and loser elimination strategy, is used to solve the unconstrained optimization problem to obtain the solution of the constrained problem. The numerical experiments of the proposed algorithm are carried out by using six con-straint optimization benchmark problems. Experimental results show that, for all problem algo-rithms, the solution of algorithm PF-DLSSA is superior to that of algorithm PF-SSA, that is, the opti-mal value and other data obtained by the experiment are superior to that obtained by the compar-ison algorithm PF-SSA. The proposed algorithm can effectively solve constrained optimization problems, and the effect is better than that of the comparison algorithm.
%K 约束优化问题，外点罚函数法，樽海鞘算法，双领导者策略，败者淘汰策略，数值实验<br>Constrained Optimization Problem
%K Outer Point Penalty Function Method
%K Salp Swarm Algorithm
%K Two-Leader Strategy
%K Loser’s Elimination Strategy
%K Numerical Experiment
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=65211