An improved partheno genetic algorithm for multi-objective economic dispatch in cascaded hydropower systems. (May 2015) - Wang, Jinlong; Huang, Weibin; Ma, Guangwen; Chen, Shijun
Highlights: This paper presents an improved partheno genetic algorithm (IPGA). We describe the realization process of IPGA for solving optimization problems. The work compares the efficiency of IPGA and other algorithms. Results demonstrate the performance of IPGA on Xiluodu and Xiangjiaba hydro plants. Abstract: The multi-objective economic dispatch (MOED) problem in cascaded hydropower systems is a complicated nonlinear optimization problem with a group of complex constraints. In this paper, an improved partheno genetic algorithm (IPGA) for resolving the MOED problem in hydropower energy systems based on the non-uniform mutation operator is proposed. In the new algorithm, the crossover operator is removed and only mutation operation is made, which makes it simpler than GA in the genetic operations and not generate invalid offspring during evolution. With the help of incorporating greedy selection idea into the non-uniform mutation operator, IPGA searches the solution space uniformly at the early stage and very locally at the later stage, which makes it avoid the random blind jumping and stay at the promising solution areas. Finally, the proposed algorithm is applied to a realistic hydropower energy system with two giant scale cascaded hydropower plants in China. Compared with other algorithms, the results obtained using IPGA verify its superiority in both efficiency and precision.