Multiple Hybrid of Lambda Iteration and Bee Colony Optimization Method for Solving Economic Dispatch Problem

Quantum‐Behaved Particle Swarm Optimization Based on Concentration Selection Probability Assignment Weights for Power System Economic Dispatch

“…In order to demonstrate the effectiveness of the IQPSO improvement algorithm proposed in this paper in solving ED problems, simulation experiments were conducted using four power systems of different sizes and compared with several intelligent algorithms, including the traditional QPSO algorithm [57], G-QPSO [58], GWO [44], SCA [59], PSO-BAAC [42], IMPSO [60], IDE [38], and AM-DE [61]. For the decreasing of the parameter slope K in (18), two decreasing strategies are used in the simulation experiments, one is the exponential decreasing strategy IQPSO-K1 according to (19) and the other is the quadratic decreasing strategy IQPSO-K2 according to (20). In each sample, each intelligent algorithm was set to run a total of 50 times, resulting in maximum, minimum, mean and standard deviation values for 50 separate runs, and this was used to compare the performance of each algorithm.…”

“…Classical algorithms include, for example, the gradient method [11][12][13], Guo et al [11] proposed an accelerated algorithm based on distributed the gradient algorithm, introducing an additional momentum term to the traditional distributed gradient algorithm, and proved that the algorithm could help converge to the optimal point and increase the convergence speed of the algorithm. The lambda iteration method [14][15][16][17][18], in literature [14] Takeang et al proposed a hybrid algorithm combining the lambda iteration method and the simulated annealing method, lambda iteration is used for initial value estimation, and simulated annealing is used for finding the optimal solution, and the hybrid algorithm can finally obtain greater convergence speed and higher quality solutions. The dynamic programming method [19,20], among them Shuai et al proposes an approximate dynamic programming algorithm that derives empirical knowledge data from historical data to make decisions close to the optimal solution, which reduces the impact of uncertainty introduced by renewable energy, load, and tariffs.…”

“…[11] proposed an accelerated algorithm based on distributed the gradient algorithm, introducing an additional momentum term to the traditional distributed gradient algorithm, and proved that the algorithm could help converge to the optimal point and increase the convergence speed of the algorithm. The lambda iteration method [14–18], in literature [14] Takeang et al . proposed a hybrid algorithm combining the lambda iteration method and the simulated annealing method, lambda iteration is used for initial value estimation, and simulated annealing is used for finding the optimal solution, and the hybrid algorithm can finally obtain greater convergence speed and higher quality solutions.…”