Browsing by Author "Pedrycz, Witold"

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    A prediction and weak coevolution-based dynamic constrained multi-objective optimization
    (IEEE, 2024-06-24) Gong, Dunwei; Rong, Miao; Hu, Na; Wang, Yan; Pedrycz, Witold; Yang, Shengxiang
    Dynamic multi-objective evolutionary algorithms (DMOEAs) have gained great popularity in dealing with dynamic multi-objective optimization problems (DMOPs). However, existing studies have difficulties in tackling DMOPs subject to (dynamic) constraints. In this paper, we propose a prediction and weak coevolutionary multi-objective optimization algorithm (PWDCMO) to handle dynamic constrained multi-objective optimization problems (DCMOPs), where a prediction strategy is employed to forecast potential optimal regions under the new environment, with a weak coevolutionary constrained multi-objective optimization (CCMO) as the optimizer aiming at balancing exploration and convergence. The proposed method is compared with four popular dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) on six test instances from two various test suites with their convergence and the overall performance being discussed. Furthermore, the performance of the proposed prediction strategy is also investigated to observe its impact on the final results. Additionally, the PWDCMO is employed in the optimization of an integrated coal mine energy system (ICMES) to validate the proficiency in addressing real world problems. Experimental results demonstrate the superiority of PWDCMO.
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    Improving consistency in fuzzy preference relations with an allocation of information granularity
    (IOS Press, 2014-09) Cabrerizo, F. J.; Pedrycz, Witold; Chiclana, Francisco; Herrera-Viedma, Enrique
    An important issue to bear in mind in Group Decision Making situations is that of consistency. However, the expression of consistent preferences is often a very difficult task for the decision makers, specially in decision problems with a high number of alternatives and when decision makers use fuzzy preference relations to provide their opinions. It leads to situations where a decision maker may not be able to express all his/her preferences properly and without contradiction. To overcome this problem, we propose the concept of the information granularity being regarded as an important and useful asset supporting the goal to reach consistent fuzzy preference relations. To do so, we develop a concept of granular fuzzy preference relation where each pairwise comparison is formed as a certain information granule instead of a single numeric value. As being more abstract, the granular format of the preference model offers the required flexibility to increase the level of consistency.
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    A New Selection Process Based on Granular Computing for Group Decision Making Problems
    (Springer, 2015-05) Cabrerizo, F. J.; Urena, Raquel; Morente-Molinera, Juan Antonio; Pedrycz, Witold; Chiclana, Francisco; Herrera-Viedma, Enrique
    In Group Decision Making, there are situations in which the decision makers may not be able to provide his/her opinions properly and they could contain contradictions. To avoid it, in this contribution, we present a new selection process to deal with inconsistent information. As part of it, we use a method based on granular computing to increase the consistency of the opinions given by the decision makers. To do so, each opinion is articulated as a certain information granule instead of a single numeric value, offering the necessary flexibility to increase the consistency. Finally, the importance of the decision makers’ opinions in the aggregation step is modeled by means of their consistency.
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    Optimal resources allocation to support the consensus reaching in group decision making
    (Elsevier, 2024-04-30) Fan, Sha; Liang, Haiming; Li, Cong-Cong; Chiclana, Francisco; Pedrycz, Witold; Dong, Yucheng
    In group decision making (GDM), the minimum cost consensus model (MCCM) to assist a group to reach a consensus with the minimum cost has gained widespread attention. However, determining the unit costs for adjusting decision makers’ opinions in the MCCM is a challenging problem that limits its practical applications. Meanwhile, the MCCM is not modeled as a resources allocation problem in an explicit manner, and the opinions in the MCCM do not represent utilities/satisfactions, leading to the unclear implications of opinions’ adjustments. To overcome these limitations of the MCCM, this paper proposes the optimal resources allocation consensus model (ORACM) to assist the moderator to allocate resources without determining unit costs to support consensus reaching, through the introduction of the resources allocation problem and utility functions in its modeling. Furthermore, we present a theoretical analysis framework to reveal the properties of the ORACM and the connection between the ORACM and the MCCM, justifying the theoretical advantages of the ORACM. Moreover, the ORACM is applied to the transboundary river pollution control negotiations of Sichuan's Tuojiang River, and the effectiveness and feasibility of the ORACM are further validated with detailed simulation and comparison analyses.