Cultural quantum-behaved particle swarm optimization for environmental/economic dispatch

• In the proposed algorithm, each particle is measured for multiple times at a single iteration.
• Belief space is used to obtain global best positions for the multiple measurements of each particle.
• A novel local search operator, which is guided by the knowledge in belief space, is proposed to maintain population diversity.
• The proposed algorithm is adopted to solve environmental/economic dispatch problems and tested on two EED systems with 6 and 40 generators, respectively.

In this paper, a novel CMOQPSO algorithm is proposed, in which cultural evolution mechanism is introduced into quantum-behaved particle swarm optimization (QPSO) to solve multiobjective environmental/economic dispatch (EED) problems. There are growing concerns about the ability of QPSO to handle multiobjective optimization problems. Two important issues in extending QPSO to multiobjective context are the construction of exemplar positions for each particle and the maintenance of population diversity. In the proposed CMOQPSO, one particle is measured for multiple times at each iteration in order to enhance its global searching ability. Belief space, which is based on cultural evolution mechanism and contains different types of knowledge extracted from the particle swarm, is adopted to generate global best positions for the multiple measurements of each particle. Moreover, to maintain population diversity and avoid premature, a novel local search operator, which is based on the knowledge in belief space, is proposed in this paper. CMOQPSO is compared with several state-of-art algorithms and tested on EED systems with 6 and 40 generators respectively. The comparative results demonstrate the effectiveness of the proposed algorithm.

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