Browsing by Author "Wei, Shuang"

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    A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary Multiobjective Optimization
    (2011-09-15) Wei, Shuang; Leung, Henry
    Most of the engineering problems are modeled as evolutionary multiobjective optimization problems, but they always ask for only one best solution, not a set of Pareto optimal solutions. The decision maker's subjective information plays an important role in choosing the best solution from several Pareto optimal solutions. Generally, the decision-making processing is implemented after Pareto optimality. In this paper, we attempted to incorporate the decider's subjective sense with Pareto optimality for chromosomes ranking. A new ranking method based on subjective probability theory was thus proposed in order to explore and comprehend the true nature of the chromosomes on the Pareto optimal front. The properties of the ranking rule were proven, and its transitivity was presented as well. Simulation results compared the performance of the proposed ranking approach with the Pareto-based ranking method for two multiobjective optimization cases, which demonstrated the effectiveness of the new ranking approach.
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    Compromise Rank Genetic Programming for Automated Nonlinear Design of Disaster Management
    (2015-05-28) Wei, Shuang; Leung, Henry
    This paper presents a novel multiobjective evolutionary algorithm, called compromise rank genetic programming(CRGP), to realize a nonlinear system design (NSD) for disaster management automatically. This NSD issue isformulated here as a multiobjective optimization problem (MOP) that needs to optimize model performance andmodel structure simultaneously. CRGP combines decision making with the optimization process to get the final globalsolution in a single run. This algorithm adopts a new rank approach incorporating the subjective information to guidethe search, which ranks individuals according to the compromise distance of their mapping vectors in the objectivespace. We prove here that the proposed approach can converge to the global optimum under certain constraints. Toillustrate the practicality of CRGP, finally it is applied to a postearthquake reconstruction management problem. Experimental results show that CRGP is effective in exploring the unknown nonlinear systems among huge datasets,which is beneficial to assist the postearthquake renewal with high accuracy and efficiency. The proposed method is foundto have a superior performance in obtaining a satisfied model structure compared to other related methods to addressthe disaster management problem.
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    A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary Multiobjective Optimization
    (Hindawi Publishing Corporation, 2011-07-21) Wei, Shuang; Leung, Henry
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    ItemOpen Access