Multi-Objective Bacterial Foraging Optimization Algorithm Based on Effective Area in Cognitive Emergency Communication Networks

作     者：Shibing Zhang Xue Ji Lili Guo Zhihua Bao Shibing Zhang;Xue Ji;Lili Guo;Zhihua Bao

作者机构：School of Information Science and TechnologyNantong UniversityNantong 226019China Xinglin CollegeNantong UniversityNantong 226008China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2021年第18卷第12期

页      面：252-269页

核心收录：

学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 081001[工学-通信与信息系统] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Sci-ence Foundation of China(Grant Nos.61871241 and 61771263) Science and Technology Program of Nantong(Grant No.JC2019117) 

主　　题：wireless communications emergency communications cognitive radio networks multi-objective optimization algorithm effective areas self-adaption 

摘      要：Cognitive emergency communication net-works can meet the requirements of large capac-ity,high density and low delay in emergency *** paper analyzes the properties of emergency users in cognitive emergency communi-cation networks,designs a multi-objective optimiza-tion and proposes a novel multi-objective bacterial foraging optimization algorithm based on effective area(MOBFO-EA)to maximize the transmission rate while maximizing the lifecycle of the *** the algorithm,the effective area is proposed to prevent the algorithm from falling into a local optimum,and the diversity and uniformity of the Pareto-optimal solu-tions distributed in the effective area are used to eval-uate the optimization ***,the dynamic preservation is used to enhance the competitiveness of excellent individuals and the uniformity and diversity of the Pareto-optimal solutions in the effective ***,the adaptive step size,adaptive moving direc-tion and inertial weight are used to shorten the search time of bacteria and accelerate the optimization *** simulation results show that the pro-posed MOBFO-EA algorithm improves the efficiency of the Pareto-optimal solutions by approximately 55%compared with the MOPSO algorithm and by approx-imately 60%compared with the MOBFO algorithm and has the fastest and smoothest convergence.