正交约束差分演化算法研究

提出一种适合求解约束问题的基于正交实验设计的差分演化算法。引入一种基于正交设计的杂交算子,并结合约束统计优生法产生最好子个体,采用决策变量分块策略,以减少正交实验次数,加快算法收敛速度。给出一种简单的多样性规则,以处理约束条件。提出基于非凸理论的多父体混合自适应杂交变异算子,以增强算法的非凸搜索能力和自适应能力。通过对13个标准测试函数进行实验,结果表明,该算法在解的精度、稳定性和收敛性上表现出较好的性能。

Research on Orthogonal Constrained Differential Evolution Algorithm

This paper proposes an Orthogonal-based Differential Evolution（ODE） algorithm for the constrained optimization problems. The ODE combines the Conventional DE（CDE）, which is simple and efficient, with the orthogonal design, which can exploit the optimum offspring. It uses a robust crossover based on orthogonal design, decision variable fraction strategy is applied herel it uses simple diversity rules to handle the constraints and maintain the diversity of the population, a multi-parent hybrid adaptive-crossover-mutation operator based on the non-convex theory is proposed. The paper executes the proposed algorithm to solve 13 benchmark functions with linear or/and nonlinear constraints. Experimental results show that the performance of the ODE outperforms other evolutionary algorithms in terms of the precision, the stability and the astringency of the final solution.