The geometric constraint solving based on hybrid differential evolution and particle swarm optimization algorithm

Wan Yi, C. Cao, Changsheng Zhang
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引用次数: 2

Abstract

Geometric constraint problem is equivalent to the problem of solving a set of nonlinear equations substantially. The constraint problem can be transformed to an optimization problem. We can solve the problem with a novel hybrid algorithm DE-PSO, which is proposed combining the particle swarm optimization (PSO) algorithm with differential evolution (DE) operators. It incorporates concepts from DE and PSO, updating particle not only by DE operators but also by mechanisms of PSO. The experiment shows that it can solve geometric constraint problems efficiently.
基于混合差分进化和粒子群优化算法的几何约束求解
几何约束问题实质上等价于求解一组非线性方程的问题。约束问题可以转化为优化问题。将粒子群优化算法(PSO)与差分进化算子(DE)相结合,提出了一种新的DE-PSO混合算法。它结合了DE和PSO的概念,不仅通过DE算子更新粒子,而且通过PSO机制更新粒子。实验表明,该方法能有效地解决几何约束问题。
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