基于改进果蝇优化算法的分布式电源优化配置

研究分布式电源（distributed generation，DG）接入配电网的优化配置问题，基于模糊隶属度技术建立综合考虑投资效益、电压指标和网损的多目标优化配置模型，有效解决了因各子目标数量级不同而导致的过度优化问题。对一种新颖的仿生智能算法--果蝇优化算法（fruit fly optimization algorithm，FOA）进行改进，效仿细菌在觅食过程中的趋化思想，在算法寻优过程中引入吸引和排斥操作，有效提高了种群多样性，降低了算法陷入局部最优的可能。IEEE 33节点系统的仿真结果表明，与传统果蝇优化算法和粒子群优化算法（particle swarm optimization，PSO）相比，改进果蝇优化算法（improved fruit fly optimization algorithm，IFOA）在寻优速度和求解精度上都具有较大优势，能快速、有效地搜索到最优配置方案，从而验证了改进算法的有效性与合理性。

Optimal Configuration of Distributed Generation Based on Improved Fruit Fly Optimization Algorithm

This paper researches the optimal allocation problem of distributed generation （DG） in distribution network， and establishes a multi-objective optimal configuration considering investment benefit， voltage quality and power loss comprehensively based on fuzzy membership technique， which can effectively solve the excessive optimization problem caused by different magnitude of targets. We improve a new bionic intelligent algorithm-fruit fly optimization algorithm （FOA） and introduce the operation of attraction and repulsion into the algorithm optimization process by following the chemotaxis of bacteria in foraging process to improve the population diversity and reduce the possibility of falling into local optimum. The simulation results of IEEE 33 node system show that， compared with the traditional FOA and particle swarm optimization （PSO） algorithm， the improved fruit fly optimization algorithm （IFOA） has a great advantage in search speed and accuracy and can quickly and effectively search the optimal configuration， which verify the validity and rationality of the improved algorithm.