欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对目前约束优化算法易陷入局部最优和鲁棒性不好等缺点，提出基于自适应ε的约束优化算法。首先，通过改进的个体比较准则，充分利用优秀不可行个体的有效信息，加大对搜索空间的探索力度，从而提高种群多样性；其次，提出自适应￡调整策略，平衡目标函数和约束违反度之间的关系，进而更加合理地进行个体比较。对13个标准测试函数的对比实验表明，本文算法不仅能够以较高精度收敛到全局最优解，而且鲁棒性较好。
英文摘要：
Since current constrained optimization algorithms are easy to fall into the local optimum and their robustness are weak, a self-adaptive e constrained optimization algorithm is proposed. By improving the indivi- dual comparison criterion, it can make full use of effective information carried by the infeasible solution, then enhance the exploration in the search space and improve the population diversity. Simultaneously, a self-adap- tive adjustment strategy is presented to produce a suitable s for balancing the relationship between the obiective function and the constraint violation degree according to different problems, which can make more reasonable comparisons between individuals. Finally, comparative experiments on thirteen benchmark functions show that the proposed algorithm is not only able to converge the global optimal solution with higher accuracy but also has better robustness.
同期刊论文项目
同项目期刊论文
期刊信息
位置：