Hybrid swarm intelligent parallel algorithm research based on multi-core clusters - Li, Wenjing; Bi, Yingzhou; Zhu, Xiaofeng; Yuan, Chang-an; Zhang, Xiang-bo (Microprocessors and microsystems, 2016)
In order to solve poor fine searching capacity of artificial fish swarm algorithm and artificial bee colony swarm algorithm in late state to result in insufficient local optimization, hybrid swarm intelligent parallel algorithm research based on multi-core clusters is proposed; Then, reverse learning mechanism is introduced in early stage of algorithm, initialized swarms are evenly distributed, and swarms are randomly divided into two groups to make interactive learning strategy accelerates rate of convergence, and basic artificial fish swarm algorithm and artificial bee colony swarm algorithm are used to make global searching. In late stage of algorithm, niches artificial fish swarm algorithm and Random Perturbation Artificial Bee Colony are used to make local fine searching to the solution obtained in early stage; On this basis, MPI+OpenMP+STM parallel programming model based on multi-core clusters is established for parallel design and analysis. Finally, stimulation experiment indicates optimizing efficiency of this algorithm is higher than single artificial fish swarm algorithm and artificial bee colony swarm algorithm.