SUN J, WU T, WU Z W, et al. Research on multi-objective edge task scheduling based on an improved cat swarm optimization algorithm [J]. Journal of Henan Polytechnic University (Natural Science) , 2025, 44(4): 29-39.

doi: 10.16186/j.cnki.1673-9787.2024070045

Received: 2024/07/10

Revised: 2024/09/23

Published: 2025/06/19

Research on multi-objective edge task scheduling based on an improved cat swarm optimization algorithm

Sun Jian1,2, Wu Tao1, Wu Zhuiwei1, Yang Xiaohuan1, Ma Baoquan1

1.School of Computer Science and Engineering， North Minzu University， Yinchuan  750021， Ningxia， China； 2.The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission， Yinchuan  750021， Ningxia， China

Abstract: Objectives This study aims to reduce task transmission latency and improve resource utilization in resource-constrained edge computing environments. Methods A multi-objective task scheduling method based on an Improved Cat Swarm Optimization （ICSO） algorithm is proposed. The ICSO algorithm is applied to encode and solve the edge task scheduling model. the improved Cat Swarm Optimization algorithm was used for encoding and solving. A nonlinear selection strategy is introduced to update the proportion of cat behaviors and the memory pool， thereby balancing global and local search capabilities. To overcome the limitation of a narrow initial solution space， a reverse learning mechanism is incorporated to expand the optimization search space. In addition， the tracking behavior is enhanced based on average fitness， improving global search ability and avoiding local optima. A novel adoption behavior is also proposed to promote mutation and diffusion among cat individuals， further enhancing the optimization capability. Results Simulation experiments show that， compared with existing task scheduling algorithms such as PPCSO， OBL_TP_PSO， PCSO， DMOOTC， LCSO， and CSO， the proposed ICSO reduces task transmission latency by 4.3%， 7.8%， 8.3%， 9.3%， 10.8%， and 12.5%， respectively. It also reduces the maximum task completion time and cost， and achieves better convergence within a limited number of iterations， demonstrating the effectiveness and feasibility of the approach.  Conclusions The proposed optimization strategy proves effective for task scheduling in edge computing scenarios. The improved Cat Swarm Optimization algorithm significantly enhances task transmission efficiency and ensures more efficient utilization of edge resources. 

Key words: edge computing; task scheduling; cat swarm optimization algorithm; task transmission latency; multi-objective