基于混合决策的改进鸟群算法

针对鸟群算法(bird swarm algorithms, BSA)在求解复杂函数问题时存在的精度低、易陷入局部最优等问题,在保留BSA简单性的同时,提出一种基于混合决策的改进鸟群算法(improved bird swarm algorithms based on mixed decision making, IBSA)。应用重心反向学习机制初始化鸟群,维持鸟群较好的空间解分布。为了有效平衡算法在寻优过程中全局探索能力和局部发觉能力,动态调整鸟群飞往另外区域的周期。引入自适应余弦函数权重策略和加权平均思想对生产者觅食公式进行改进,增加算法在陷入局部最优后的脱困能力。在9个测试函数的基础上通过仿真试验对比基于IBSA、BSA、粒子群算法(particle swarm optimization, PSO)性能。结果表明,改进算法在单峰函数和多峰函数的测试中,寻优精度和寻优速度得到了较大程度上的提升。

Improved bird swarm algorithms based on mixed decision making

Aiming at the problems of low precision and easy to fall into local optimum in solving complex function problems of traditional bird swarm algorithm (BSA), an improved bird swarm algorithm based on mixed decision-making was proposed while retaining the simplicity of BSA. The centroid opposition-based learning was used to initialize the bird population and maintain the better spatial solution distribution of the bird flock. In order to balance the global search ability and local detection ability of the algorithm in the optimization process, the period time of the birds flying to another area was dynamically adjusted. The weighting strategy of adaptive cosine function and weighted averaging idea were introduced to improve the producer's foraging formula, so as to increase the ability of the algorithm to get rid of difficulties after falling into local optimum. The performance of improved bird swarm algorithm based on mixed decision-making, bird swarm algorithm and particle swarm optimization were compared on the basis of nine test functions. The results showed that the accuracy and speed of the improved algorithm were greatly improved in the test of single-peak and multi-peak functions.