Graph Partition Approach Based on the Cauchy Mutation and Inertia Weight

Abstract

Due to the low quality of the existing online graph partition algorithm, the graph partition problem is solved through the Cat Swarm Optimization (CSO) algorithm to improve the partition quality. To avoid falling into the local optimum with CSO, an improved graph partition approach based on Cat Swarm Optimization with the Cauchy mutation and the Inertia weight (CICSO) was proposed. CICSO adopts the Cauchy mutation to update the optimal position, which can increase the accuracy of graph partition. Meanwhile, the self-adaptive inertia weight with the dynamic change is introduced in the tracking mode to increase the convergence speed and stability. Experimental results show that the improved cat algorithm CICSO has better performance than the standard cat algorithm in terms of partition quality and convergence time, compared with the LDG, FENNEL, and the standard CSO.