Fuzzy decision making based real coded GA for multi-objective optimization

S. Parihar, M. Pandit
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引用次数: 2

Abstract

The paper proposes fuzzy decision making based real coded genetic algorithm (RCGA) to solve the complex multi objective problem of environmental economic dispatch for thermal generators of the power system. Due to the increasing public and government concern towards reducing pollution, generator dispatch need to be carried out for cost, emission and loss minimization. This multi objective problem is converted into single objective problem by introducing a term called price penalty factor (PPF), which gives a set of non-dominated solutions known as Pareto optimal solutions and then depending upon the highest rank, best compromise solution is obtained by applying fuzzy decision making technique. This proposed method is tested on the standard IEEE 30-bus, six unit system. The results show the superiority of RCGA in producing fast and feasible solutions with stable convergence characteristics.
基于模糊决策的实数编码遗传算法多目标优化
提出了一种基于模糊决策的实数编码遗传算法(RCGA),用于解决电力系统火电机组环境经济调度的复杂多目标问题。由于公众和政府对减少污染的日益关注,发电机调度需要进行成本、排放和损失最小化。将多目标问题转化为单目标问题,引入价格惩罚因子(PPF)一词,给出一组非支配解,称为帕累托最优解,然后利用模糊决策技术根据最高阶求出最优妥协解。该方法在标准的IEEE 30总线、六单元系统上进行了测试。结果表明,该算法具有快速、可行且收敛特性稳定的优点。
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