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Based on the properties of the cloud model on the process of transforming a qualitative concept to a set of quantitative numerical values, an adaptive computational intelligence optimization algorithm is proposed by analyzing the correspondence between search characteristics and cloud models. In the proposed algoritnm, the feature parameters of solution sets are created by a multidimensional backward...
According to the characteristics of constrained optimization problem, a new approach based on a new fitness function is presented to handle constrained optimization problems. The primary features of the algorithm proposed are as follows. Inspired by the smooth function technique, a new fitness function is designed which can automatically search potential solutions. In order to make the fitness function...
Linear bilevel programming problem, as a NP-hard problem, is the linear version of bilevel programming, in this paper we design an efficient algorithm for solving this kind of problems by combining genetic algorithm with enumeration procedure of extreme points. First, based on the duality principle, the follower problem is replaced by the prime-dual conditions, and the original problem is transformed...
The current GPM algorithm needs many iterations to get good process models with high fitness which makes the GPM algorithm usually time-consuming and sometimes the result can not be accepted. To mine higher quality model in shorter time, a heuristic solution by adding log-replay based crossover operator and direct/indirect dependency relation based mutation operator is put forward. Experiment results...
In this paper, a hybrid descent method, consisting of a genetic algorithm and the filled function method, is proposed. The genetic algorithm is used to locate descent points for previously converged local minima. The combined method has the decent property and the convergence is monotonic. To demonstrate the effectiveness of the proposed hybrid method, several multi-dimensional or non-convex optimization...
Differential Evolution(DE) is a kind of simple but powerful evolutionary optimization algorithm with many successful applications. However, it has some weaknesses, especially the slow convergence speed because of weak local search ability in its stochastic search. To overcome the drawback, we first employ the orthogonal design method with quantization technique to generate the initial population,...
It could be concluded that all multi-objective evolutionary algorithms draw their strength from two aspects: convergence and diversity. In order to achieve these goals, This paper proposes a hybrid methods that combines GA with simplex search method for multi-objective optimization using preference order ranking. Preference order ranking is used as fitness assignment methodology to accelerate the...
This paper considers the generalized assignment problem (GAP). It is well-known NP-hard combinatorial optimization problem that is interesting in itself and also appears as a sub problem in other problems of practical importance. Line-up competition Differential Evolution algorithm for the GAP is proposed. The algorithm uses integer-coding structure, and introduces the idea of line-up competition...
A new filled function is proposed for solving constrained global optimization problems without the coercive condition. The filled function is proposed for escaping the current local minimizer of a constrained global problem by combining the idea of filled function in unconstrained global optimization and the idea of penalty function in constrained optimization. Some numerical results on some typical...
To improve PSO, differential evolution (DEA) and ant colony strategy are involved into PSO algorithm, and new PSO(DAPSO) is presented. Handling the current optimal positions of particles with differential evolution, the detecting and exploitation ability of both PSO and DEA are utilized effectively, and some potential evolution directions are constructed for each particle in PSO, at the same time...
Consumer credit prediction is considered as an important issue in the credit industry. The credit department often makes decision which depends on intuitive experience with large risk. This study proposed a new model that hybridized the support vector machine (SVM) and particle swarm optimization (PSO) to evaluate the new consumer's credit score. The hybrid model simultaneously optimizes the SVM kernel...
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