A Improved Genetic Algorithm for Random-Fuzzy Programming Model in Electricity Market

In the sealed-auction-based electricity markets, to fast and accurately solve the random-fuzzy programming problem, which is for building optimal bidding strategies of generation companies and can simultaneously deal with both random variable and fuzzy variable, a improved genetic algorithm (IGA) is proposed based on a genetic algorithm with hybrid Laplace crossover and power mutation (HLCPM). The IGA can fast find the optimal solution in some area and guarantee the enough search area. The performance of IGA is verified by 10 benchmark global optimization test problems and the random-fuzzy programming problem. A very good performance of IGA is shown by the results.