In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve...In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables.Due to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal solutions.The proposed algorithm adopts a dual-population framework and an improved environmental selection method.It utilizes a convergence archive to help the first population improve the quality of solutions.The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population.The combination of these two strategies helps to effectively balance and enhance conver-gence and diversity performance.In addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed.The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.展开更多
Hepatitis B virus(HBV) infection is a severe global health problem. In recent years, mutations as an essential element in the HBV evolution have been extensively studied. However, the study of the conserved sequence f...Hepatitis B virus(HBV) infection is a severe global health problem. In recent years, mutations as an essential element in the HBV evolution have been extensively studied. However, the study of the conserved sequence for the evolution of HBV is still in its infancy. In this paper, we applied MEME(multiple EM for motif elicitation) algorithm for motif discovery and proposed a new metric CI(conserved index) to make phylogenetic analysis of HBV sequences. Our results indicate that MEME can efficiently discover multiple motifs from HBV sequences and the new measurement CI for the conservative of sequences can effectively help us to build the phylogenetic tree.Thus, we can get evolutionary relationship of HBV sequence through the phylogenetic tree.展开更多
基金supported in part by National Natural Science Foundation of China(62106230,U23A20340,62376253,62176238)China Postdoctoral Science Foundation(2023M743185)Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications Open Fundation(BDIC-2023-A-007)。
文摘In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables.Due to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal solutions.The proposed algorithm adopts a dual-population framework and an improved environmental selection method.It utilizes a convergence archive to help the first population improve the quality of solutions.The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population.The combination of these two strategies helps to effectively balance and enhance conver-gence and diversity performance.In addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed.The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.
基金Science Research Foundation of Yunnan Educational Committeegrant number:2011J079+3 种基金Yunnan Fundamental Research Foundation of Applicationgrant number:2009ZC049MScience Research Foundation for the Overseas Chinese Scholars,State Education Ministrygrant number:2010-1561
文摘Hepatitis B virus(HBV) infection is a severe global health problem. In recent years, mutations as an essential element in the HBV evolution have been extensively studied. However, the study of the conserved sequence for the evolution of HBV is still in its infancy. In this paper, we applied MEME(multiple EM for motif elicitation) algorithm for motif discovery and proposed a new metric CI(conserved index) to make phylogenetic analysis of HBV sequences. Our results indicate that MEME can efficiently discover multiple motifs from HBV sequences and the new measurement CI for the conservative of sequences can effectively help us to build the phylogenetic tree.Thus, we can get evolutionary relationship of HBV sequence through the phylogenetic tree.
