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Differential evolution (DE) algorithm mainly uses the distance and direction information from the current population to guide search. However, it has no mechanism to extract and use global information about the search space. Cloud model is an effective tool in uncertain transforming between qualitative concepts and their quantitative expressions. It can be used to extract the global information about...
By combining CMBOA and PSO with quasi-oppositional learning, a cloud estimation of distribution particle swarm optimizer is firstly introduced. Then, it is extended to multi-objective optimization problems by using maximum ranking. In the algorithm's offspring generation scheme, new individuals are generated in the cloud estimation of distribution way or in the PSO way. And instead of Pareto dominance,...
Estimation of distribution algorithms(EDAs) are a class of evolutionary optimization algorithms. In this paper, EDAs scheme are extended to multi-objective optimization problems by using preference order and cloud model. In the algorithm, three digital characteristics from the current population are firstly estimated by backward cloud generator. Afterwards, forward cloud generator used to generate...
Cloud estimation of distribution particle swarm optimizer combining PSO and cloud model is introduced. In the algorithm's offspring generation scheme, new particles are generated in the cloud estimation of distribution way or in the PSO way. The innovation of the algorithm is production of cloud particles according to the cloud model theory. The cognitive population obtained during optimization is...
Cloud model is an effective tool in uncertain transforming between qualitative concepts and their quantitative expressions. In this paper, we introduce a new optimization method inspired from cloud model theory. The innovations of the algorithm are the estimation of good solution regions and new solution production according to the cloud model theory. First, the algorithm uses information obtained...
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