论文部分内容阅读
提出一种用多目标技术求解约束优化问题的算法.该算法有3个特征:1)将约束优化问题转化为等价的动态约束多目标优化问题,然后用动态约束多目标演化算法求解动态约束多目标优化问题;2)演化初始阶段,拓宽约束边界以使整个种群可行;演化过程中,约束边界微弱的收缩以确保动态约束多目标演化算法中种群的大多数个体仍是可行的,这使动态约束多目标演化算法如同多目标演化算法求解无约束问题一样有效;3)采用基于学习的机制自适应调整演化算法的参数,以提高算法效率.实验结果表明,与4个当前较为先进的约束处理算法相比,本文算法效果更优.
A multi-objective algorithm for solving constrained optimization problems is proposed.The algorithm has three features: 1) the constrained optimization problem is transformed into an equivalent dynamic constrained multi-objective optimization problem, and then the dynamic constrained multi-objective evolutionary algorithm is used to solve the dynamic constraints Multi-objective optimization problem; 2) In the initial stage of evolution, the boundary of constraint is broadened to make the whole population feasible; In the process of evolution, the constraint boundary is weakly contracted so as to ensure that most of the individuals in the population of the dynamic constrained multi-objective evolutionary algorithm are still feasible. The dynamic constrained multi-objective evolutionary algorithm is as effective as the multi-objective evolutionary algorithm to solve the unconstrained problem.3) The learning-based mechanism is used to adaptively adjust the parameters of the evolutionary algorithm to improve the efficiency of the algorithm.The experimental results show that, compared with the four currently more advanced constraints Compared with the algorithm, this algorithm is better.