摘要
This paper presents a novel and efficient method for solving the economic dispatch (ED) problems with valve-point effects, by integrating the biased velocity of particle swarm optimization (PSO) to the chemotaxis, swarming and reproduction steps of bacterial foraging algorithm (BFA). To include valve point effects sinusoidal terms are added to the fuel cost function. This makes the ED problems highly non-linear. In order to solve such problems the best cell (or particle) biased velocity (vector) is added to the random velocity of the BFA to reduce randomness in movement (evolution) and to increase swarming. This results in the hybrid bacterial foraging algorithm (HBFA). To demonstrate the effectiveness of the proposed HBFA method, numerical studies have been performed for three different sample systems. Comparison of the results obtained by the HBFA with the BFA and other evolutionary algorithms clearly show that the proposed method outperforms other methods in terms of convergence rate and solution quality in solving the ED problems with valve-point effects.
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