Differential Evolution with Gaussian Mutation for Economic Dispatch
Differential Evolution with Gaussian Mutation for Economic Dispatch
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DOI：
10.1007/s40031-015-0198-0
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发表时间：
2015-05
影响因子：
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通讯作者：
M. Basu;C. Jena;C. Panigrahi
中科院分区：
文献类型：
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作者：
M. Basu;C. Jena;C. Panigrahi
针对火电机组经济调度问题，考虑网损和发电机非线性约束（如禁止运行区域），提出了基于高斯变异的差分进化算法（DEGM）。差分进化是一种简单而强大的全局优化技术。它利用随机采样的目标向量对的差异，其变异过程。这种变异过程不适合复杂的多峰优化。本文提出了高斯变异DE，提高搜索效率，并保证了高概率获得全局最优值，而不显着损害DE的结构简单。在三个不同的测试系统上验证了该方法的有效性。通过与其他进化方法的比较，发现基于DEGM的方法能够提供更好的解决方案。
This paper presents differential evolution with Gaussian mutation (DEGM) to solve economic dispatch problem of thermal generating units with non-smooth/non-convex cost functions due to valve-point loading, taking into account transmission losses and nonlinear generator constraints such as prohibited operating zones. Differential evolution (DE) is a simple yet powerful global optimization technique. It exploits the differences of randomly sampled pairs of objective vectors for its mutation process. This mutation process is not suitable for complex multimodal optimization. This paper proposes Gaussian mutation in DE which improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the simplicity of the structure of DE. The effectiveness of the proposed method has been verified on three different test systems. From the comparison with other evolutionary methods, it is found that DEGM based approach is able to provide better solution.