A multiobjective hybrid bat algorithm for combined economic/emission dispatch. (October 2018) - Liang, Huijun; Liu, Yungang; Li, Fengzhong; Shen, Yanjun
Highlights: A new multiobjective hybrid bat algorithm (MHBA) is proposed. The global search ability of MHBA is improved by using random black hole model. A modified comprehensive learning strategy is applied to the update of velocity. The combined economic/emission dispatch is solved for large scale systems. Practical constraints are considered, including power flow constraints, etc. Abstract: In this paper, a multiobjective hybrid bat algorithm is proposed to solve the combined economic/emission dispatch problem with power flow constraints. In the proposed algorithm, an elitist nondominated sorting method and a modified crowding-distance sorting method are introduced to acquire an evenly distributed Pareto Optimal Front. A modified comprehensive learning strategy is used to enhance the learning ability of population. Through this way, each individual can learn not only from all individual best solutions but also from the global best solutions (nondominated solutions). A random black hole model is introduced to ensure that each dimension in current solution can be updated individually with a predefined probability. This is not only meaningful in enhancing the global search ability and accelerating convergence speed, but particularly key to deal with high dimensional systems, especially large-scale power systems. In addition, chaotic map is integrated to increase the diversity of population and avoid premature convergence. Finally, numerical examples on the IEEE 30-bus, 118-bus and 300-bus systems, are provided to demonstrate the superiority of the proposed algorithm.