Liqun Gao

Transmission network optimal planning using the particle swarm optimization method

In this paper, power system transmission network planning is formulated as a multi-objective mathematical optimization problem. In this context, three objectives: investment cost, reliability and environmental impact are considered in the optimization. To overcome the drawbacks of conventional mathematical optimization method in arriving at local optimum and dimension disasters, etc., we introduced the particle swarm optimization (PSO) technique into transmission network optimal planning for the first time, from which the optimal scheme is generated. A case on transmission network planning problem is presented to show the methodologys feasibility and efficiency, compared with the existing optimal planning methods, the search time of the particle swarm optimization method is shorter and the result is close to the ideal solution, simultaneously.

A novel global harmony search algorithm for chemical equation balancing

Chemical equation balancing is an important issue in the field of chemistry, and it has drawn many researchers attention over the last few decades. Essentially, chemical equation balancing can be classified as integer programming problem with equality constraints. In this paper, we use a recently proposed algorithm - the novel global harmony search algorithm (NGHS) to solve this problem. The NGHS algorithm is an improved version of harmony search algorithm (HS), and it includes two important operations: position updating and genetic mutation with a low probability. The former can enhance the convergence of the NGHS, and the latter can effectively prevent the NGHS from trapping into the local optimum. Based on a large number of experiments, the NGHS has demonstrated high efficiency on solving chemical equation balancing. The results show that the NGHS can be an efficient alternative for solving chemical equation balancing.

Adaptive Particle Swarm Optimization Algorithm for Power System Reactive Power Optimization

Aiming at the precocious convergence problem of particle swarm optimization algorithm, adaptive particle swarm optimization(APSO) algorithm was presented. In this algorithm, the notion of species was introduced into population diversity measure. The species technique is based on the concept of dividing the population into several species according to their similarity. The inertia weight was nonlinearly adjusted by using population diversity information at each iteration step. Velocity mutation operator and position crossover operator were both introduced and the global performance was clearly improved. The algorithm had been applied to reactive power optimization. The simulation results of the standard IEEE-30-bus power system had indicated that APSO was able to undertake global search with a fast convergence rate and a feature of robust computation. It was proved to be validity, fast convergence and computation efficiency during the reactive power optimization.

Power System Reactive Power Optimization Based on Adaptive Particle Swarm Optimization Algorithm

Aiming at the precocious convergence problem of particle swarm optimization algorithm, adaptive particle swarm optimization (APSO) algorithm was presented. In this algorithm, inertia weight was nonlinearly adjusted by using population diversity information. Velocity mutation operator and position crossover operator were both introduced and the global performance was clearly improved. The algorithm had been applied to reactive power optimization. The simulation results of the standard IEEE-30-bus power system had indicated that APSO was able to undertake global search with a fast convergence rate and a feature of robust computation. It was proved to be efficient and practical during the reactive power optimization

Application of Analytic Hierarchy Process in Power Lines Maintenance

The lines maintenance plan has always been drawn only by experiences and trials due to this works difficult. There exist many disadvantages. In this paper analytic hierarchy process (AHP), an effective method that can solve a multiple criteria and multiple objective decision-making problems was introduced to the power lines maintenance problem in order to gain a scientific and objective maintenance scheduling. Firstly an AHP model was built up on the base of analyzing correlative factors. Then an example was given to indicate how AHP to apply

Wavelet-based Watershed for Image Segmentation Algorithm

A watershed image segmentation method based on wavelet transform is proposed. Firstly, source image was filtered by multi-scale morphological filtering and the filtered image was decomposed by wavelet, then the low-frequency approximate image was segmented into many small regions by watershed algorithm and these regions were merged according to some region mergence rules to get the original segmentation image. Finally, the original segmentation image was projected into a full-resolution image by inverse-wavelet transform to get the final segmentation image. Utilizing multi-scale morphological filtering and the multi-resolution characteristic of wavelet transform, this method solves effectively the problems of sensitizing to noises and over-segmentation phenomenon of watershed algorithm, and the computing speed is improved

A novel Genetic Algorithm with multiple sub-population parallel search mechanism

Genetic Algorithm (GA), based on metaphors from the natural evolutionary process, is a famous random heuristic approach for solving complex optimization problems. However, the traditional GA is always subjected to the low convergence velocity and deceptions of multiple local optima. To overcome such inconvenience, a novel GA is proposed which entitled self-adaptive genetic algorithms (SaGA) in this paper. During the execution of the search process, the whole populations are classified into subgroups by sufficiently analyzed the individuals state. Each individual in a different subset is assigned to the appropriate attribute (probabilities of crossover and mutation, pc, pm). Self-adaptive update the subgroups and adjust the control parameters, which are considered to be an optimal balance between exploration and exploitation. The empirical values and negative feedback technique are also used in parameters selection, which relieve the burden of specifying the parameters values. The new method is tested on a set of well-known benchmark test functions, and the simulation results suggest that it outperforms to other state-of-the-art techniques referred to in this paper in terms of the quality of the final solutions.

Immune Genetic Algorithm for Vehicle Routing Problem with Time Windows

Focused on the VRPTW (vehicle routing problem with time windows) and based on SGA (simple genetic algorithm), this paper employs a new IGA (immune genetic algorithm) to solve the VRPTW through using immune operator. This algorithm based on the global searching method of SGA, and using the diversity preservation strategy of antibodies in biology immunity mechanism, the method greatly improves the colony diversity of SGA. The experiment shows that the proposed IGA can improve the global research ability and the speed of convergence, so it could solve the VRPTW effectively

A fuzzy adaptive Genetic Algorithms for global optimization problems

Genetic Algorithms (GA) is a method based on natural selection in the theory of biological evolution, which has been widely applied to solve numerous optimization problems in diverse fields. However, the canonical GA is more likely to get stuck at a local optimum and thereby leads to premature convergence. To overcome such inconvenience, a fuzzy adaptive GA (FAGA) is proposed based on fuzzy clustering and adaptation policy of parameters control (probabilities of crossover and mutation, pc, pm respectively). Sufficiently analyzing the solution state and dynamically allocating different individuals with moderate properties, the core idea of the schema, are to maintain diversity in the population in order to cope with the deception multiple local optima. Self-adaptive adjust of pc, pm which is considered to be an optimal balance between exploration and exploitation. Fuzzy cluster in the approach depends on the rank of fitness, which has three categories during the whole search process; parameter control is based on the technique of negative feedback, which relieves the burden of specifying the values. The performance of the new approach is test on a set of standard benchmark functions and compares with traditional and adapted GA which has a better result.

An image fusion algorithm based on RBF neural networks

An image fusion algorithm based on RBF neural networks for multi-focus image is presented in this paper. Four features are defined and proved to indicate the clarity of an image block, and these features are extracted and fed into the neural networks, which then learns to determine which source image is clearer at that particular physical location. The comparison of the algorithm proposed in this paper and the DWT-based one is done. Experimental results show that the performance of the method proposed in this paper is superior to that of DWT-based one.