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Information systems have been widely used to support workflow processes to record the execution of tasks in the process and are stored in so-called “event logs”. Techniques that relate to events extraction have gotten increasing attention such as process mining techniques. Developed process mining methods such as alpha algorithm, alpha++ algorithm, and genetic process mining (GPM) are capable of tackling...
In order to solve the problem of linearization, complexity and poor accuracy for parameter estimate of Muskingum Routing Model at present, this paper introduces three modern intelligent algorithms - Genetic Algorithm (GA), Simulated Annealing Algorithm (SA) and Particle Swarm Optimization Algorithm (PSO) for the parameter calibration of Muskingum model. Through specific simulation, the results of...
The last decade has witnessed a great interest in using evolutionary algorithms, such as genetic algorithms, evolutionary strategies and particle swarm optimization (PSO), for multivariate optimization. This paper presents a hybrid algorithm for searching a complex domain space, by combining the PSO and orthogonal design. In the standard PSO, each particle focuses only on the error propagated back...
The flow shop scheduling problem (FSSP) is a NP-HARD combinatorial problem with strong industrial background. Among the meta-heuristics, genetic algorithms attracted a lot of attention. However, lacking the major evolution direction, the effectiveness of regular genetic algorithm is restricted. In this paper, the particle swarm optimization algorithm (PSO) is introduced for better initial group. By...
In this paper, we present a new algorithm named Election campaign algorithm (ECA) for the multimodal function optimization. It acts by simulating the behavior that the election candidates pursue the highest support in election campaign. The proposed approaches are validated using test functions taken from the specialized literature, and our results are compared with those obtained by genetic algorithm...
The particle swarm optimization (PSO) algorithm is vulnerable to reach local optimal value. So, this paper presents an adaptive hybrid particles swarm optimization. During the solving process, both crossover operator in genetic algorithm and hyper-mutation are introduced. Referring to the selection mechanism of immune algorithm based on information entropy, the adaptive selections mechanism is proposed...
The efficiency of utilizing the satellite communications resource and system can be improved by optimizing the satellite broadcasting scheduling with genetic algorithm. However drawbacks such as complicated genetic operation, tardy convergent speed and the aptness to sink into local minimum within the genetic algorithm (GA) have encouraged a satellite broadcasting scheduling approach for resolving...
One hybrid intelligent algorithm is designed to solve the annular water supply network optimization. The model to minimize the objective function of the annual reduced cost with the constraints of hydraulic conditions. The intelligent optimization algorithm population based incremental learning - PBIL based on probability learning strategy is combined to particle swarm optimization algorithm-PSO....
The particle swarm optimization algorithm is easily trapped into local optimization. In order to improve its performance , The simulated annealing operation was introduced into PSO. The hybrid algorithm combines the fast search optimum ability of PSO with probability jump property of SA. It can maintain the individual diversity and restrain the degenerate phenomenon. The experiment results compared...
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