Browsing by Author "Wang, Xueqing"

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    A Confidence and Conflict-based Consensus Reaching Process for Large-scale Group Decision Making Problems with Intuitionistic Fuzzy Representations
    (IEEE, 2024-03-07) Ding, Ru-Xi; Yang, Bing; Yang, Guo-Rui; Li, Meng-Nan; Wang, Xueqing; Chiclana, Francisco
    With the development of social democracy, the public begin to participate in large-scale group decision making (LSGDM) events that have a significant impact on their personal interests. However, the participation of the public with insufficient expertise will cause much hesitancy in the evaluations of decision makers (DMs), which can be captured by intuitionistic fuzzy sets. Meanwhile, due to the increment in the number of DMs, the cost of consensus reaching processes (CRPs), which are utilized to help DMs reach a consensus, is getting higher and higher. In order to improve the efficiency of the CRP, this paper presents a confidence and conflict-based consensus reaching process (CC-CRP) for LSGDM events with intuitionistic fuzzy representations. In the proposed model, according to the hesitancy of the DMs’ intuitionistic fuzzy evaluations, an objective method is firstly developed to calculate the confidence level of DMs that does not require any extra information. Then, a three-dimension clustering method is designed by considering the type of conflict, the degree of conflict, and the confidence level of DMs. After this, an efficiency rate of modification is defined to select DMs who will be persuaded first to adjust their evaluations with recommendation plans generated by a specific optimal method. Finally, according to the clustering process results, different CC-CRP management methods will apply to DMs with different attributes. An illustrative example and several experiments are reported to provide evidence that the proposed model is feasible and effective.
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    A dynamic preference-driven evolutionary algorithm for solving dynamic multi-objective problems
    (ACM, 2024-07-01) Wang, Xueqing; Zheng, Jinhua; Zou, Juan; Hou, Zhanglu; Liu, Yuan; Yang, Shengxiang
    Considering the decision-maker's preference information in static multi-objective optimization problems (MOPs) has been extensively studied. However, incorporating dynamic preference information into dynamic MOPs is a relatively less explored area. This paper introduces a preference information-driven DMOEA and proposes a preference-based prediction method. Specifically, a preference-based inverse model is designed to respond to the time-varying preference information, and the model is used to predict an initial population for tracking the changing ROI. Furthermore, a hybrid prediction strategy, that combines a linear prediction model and estimation of population manifolds in the ROI, is proposed to ensure convergence and distribution of population when the preference remain constant. The experimental results show that the proposed algorithm has significant advantages over existing representative DMOEAs through experimental tests on 19 common test problems.
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    A novel preference-driven dynamic multi-objective evolutionary algorithm for solving dynamic multi-objective problems
    (Elsevier, 2024-06-30) Wang, Xueqing; Zheng, Jinhua; Hou, Zhanglu; Liu, Yuan; Zou, Juan; Xia, Yizhang; Yang, Shengxiang
    Most studies in dynamic multi-objective optimization have predominantly focused on rapidly and accurately tracking changes in the Pareto optimal front (POF) and Pareto optimal set (POS) when the environment undergoes changes. However, there are real-world scenarios where it is necessary to simultaneously solve changing objective functions and satisfy the preference of Decision Makers (DMs). In particular, the DMs may be only interested in a partial region of the POF, known as the region of interest (ROI), rather than requiring the entire POF. To meet the challenge of simultaneously predicting a changing POF and/or POS and dynamic ROI, this paper proposes a new dynamic multi-objective evolutionary algorithm (DMOEAs) based on the preference. The proposed algorithm consists of three key components: an evolutionary direction adjustment strategy based on changing reference points to accommodate shifts in preferences, an angle-based search strategy for tracking the varying ROI, and a hybrid prediction strategy that combines linear prediction models and population manifold estimation within the ROI to ensure convergence and distribution in scenarios where preferences remain unchanged. Experimental studies conducted on 30 widely used benchmark problems in which it outperforms contrasting algorithms on 71% of test suits. Empirical results demonstrate the significant advantages of the proposed algorithm over existing state-of-the-art DMOEAs.
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    Interactive dynamic trust network for consensus reaching in social network analysis based Large-Scale decision making
    (Elsevier, 2024-06-23) Guo, Sijia; Ding, Ru-Xi; Li, Meng-Nan; Shi, Zijian; Wang, Xueqing; Chiclana, Francisco
    With the advances in social media and e-democracy technologies, large-scale decision making (LSDM) problems with trust relationships demands effective consensus-reaching processes. Existing literature has identified a need for improvement in the updating method of trust relationships and the recommendation-feedback mechanism, while the overall effect of trust relationships on consensus reaching remains unexplored and not quantitatively examined. This study proposes a novel interactive dynamic trust-lead consensus reaching process (IDT-CRP)-based model to address these challenges. The model integrates a dynamic Trust network updating (DTNU) process and a trust-lead and efficiency-driven recommendation-feedback (TERF) process. In the DTNU process, secondary trust is defined to represent the trust derived from decision makers’ modification behaviors in each iteration. In the TERF process, a novel trust-lead and efficiency-driven recommendation-feedback mechanism is proposed, in which two different decision scenarios are provided. Furthermore, the role of trust relationships in enhancing group consensus level is analyzed, revealing its significant impact while identifying an upper limit. Novelties of this paper include 1) the improved trust updating algorithm which derived secondary trust from modification behaviors instead of opinion similarities; 2) the improved recommendation-feedback mechanism with different trust utilization scenarios while retaining minority opinions; 3) the novel IDT-CRP model based on the interactive mechanism between trust relationship and opinion evolution. A series of experiments are implemented, validating that the proposed model is of good feasibility, effectiveness and robustness, and can be applied in future LSDM research.