An adaptive particle swarm optimizer with decoupled exploration and exploitation for large scale optimization

As a form of evolutionary computation, particle swarm optimization is less effective in large scale optimization since it is unable to effectively balance exploration and exploitation. To address this problem, first, a learning structure decoupling exploration and exploitation is proposed. This helps simultaneously and independently managing exploration and exploitation in different components. Second, following the proposed learning structure, two novel learning strategies are developed. On the one hand, a local sparseness degree measurement in fitness landscape is proposed to estimate the congestion and distribution of particles, based on which an exploration strategy is built by guiding particles to sparse areas. On the other hand, an adaptive exploitation strategy is developed which can effectively adjust the fitness differences between exemplars and updated particles during the optimization process by employing a multi-swarm strategy and an adaptive sub-swarm size adjustment. Finally, by embedding the two learning strategies into the proposed learning structure, an adaptive particle swarm optimizer with decoupled exploration and exploitation is proposed. Thanks to the novel balancing strategy of exploration and exploitation, the two functions in the proposed algorithm can be independently and simultaneously managed. Furthermore, theoretical analyses are put forward to prove the convergence and computational complexity of the proposed algorithm. Comprehensive experiments are conducted based on the large scale optimization benchmarks from CEC 2010 and CEC 2013 and six state-of-the-art large scale optimization evolutionary algorithms, the results demonstrate the effectiveness of the proposed learning strategies and the competitive performance of the proposed algorithm.