Weigang Jiang

Vehicle Routing Problem with Time Windows: A Hybrid Particle Swarm Optimization Approach

Vehicle routing problem (VRP) is a well-known combinatorial optimization and nonlinear programming problem seeking to service a number of customers with a fleet of vehicles. This paper proposes a hybrid particle swarm optimization (HPSO) algorithm for VRP. The proposed algorithm utilizes the crossover operation that originally appears in genetic algorithm (GA) to make its manipulation more readily and avoid being trapped in local optimum, and simultaneously for improving the convergence speed of the algorithm, level set theory is also added to it. We employ the HPSO algorithm to an example of VRP, and compare its result with those generated by PSO, GA, and parallel PSO algorithms. The experimental comparison results indicate that the performance of HPSO algorithm is superior to others, and it will become an effective approach for solving discrete combinatory problems.

A particle swarm optimization algorithm with crossover for vehicle routing problem with time windows

The vehicle routing problem (VRP) is a very important combinatorial optimization and nonlinear programming problem in the fields of transportation, distribution and logistics. In this paper, a particle swarm optimization (PSO) algorithm with crossover for VRP is proposed. The PSO algorithm combined with the crossover operation of genetic algorithm (GA) can avoid being trapped in local optimum due to using probability searching. We apply the proposed algorithm to an example of VRP, and compare its result with those generated by PSO, GA, and parallel PSO algorithms. The experimental comparison result demonstrates that the performance of PSO algorithm with crossover is competitive with others and will be an effective method for solving discrete combinatory problems.

A Particle Swarm Optimization Algorithm Based on Diffusion-Repulsion and Application to Portfolio Selection

Inspired by the diffusion movement phenomenon of the molecules, this paper presents a diffusion-repulsion particle swarm optimization (DRPSO). The proposed new algorithm (DRPSO) includes attraction and repulsion (or migration) phases. Once the diversity of population becomes too low, the individuals will be dispersed and carry out diffusion movement, while if the diversity of population becomes too high, the individuals have to be congregated, which is accomplished by diversity control method. Comparisons with standard SPSO and other algorithms on a portfolio problem indicate that DRPSO not only prevents premature convergence to a high degree, but also keeps a more rapid convergence rate than SPSO.

An image template matching method using particle swarm optimization

At present, most of the image template matching algorithms involve large computational complexity, and can hardly be used in practical projects. This paper proposes that particle swarm optimization algorithm (PSO) be used in image template matching problems (ITMP). Template matching in fact is a matter of seeking optimization. The cross-correlation function of template and sub image is set as the objective function, and then a fast template matching algorithm can reached based on particle swarm optimization algorithm. Experiment results prove both the computational accuracy and efficiency of this algorithm.

A Particle Swarm Optimization Algorithm with Crossover for Resource Constrained Project Scheduling Problem

Resource and project optimization scheduling has become the key of the success of researching project in the enterprises. In order to solve the mass resource constrained project scheduling problem, in this paper, an improved particle swarm algorithm (PSO) called particle swarm algorithm with crossover (CPSO) was presented. This improved algorithm is based on PSO and genetic algorithm (GA). Through comparing with SPSO and GA on RCPSP, it is indicated that CPSO not only avoids premature convergence to a high degree, but also keeps a faster convergence rate than SPSO and GA.

An Improved Grey-Markov Chain Method with an Application to Predict the Number of Chinese International Airlines

This paper proposes an improved Grey-Markov forecasting dynamic method based on unbiased grey system theory and fuzzy classification. The new forecasting method is named unbiased Grey-fuzzy-Markov Chain method, which can take advantage of the prediction power of conventional Grey-Markov forecasting method and at the same time eliminate grey bias and improve anti-jamming performance. As an example, we use the statistical data of the number of Chinese international airlines from 1987 to 2006 for a validation of the feasibility and practicability of the improved Grey-Markov forecasting model.

A particle swarm optimization algorithm based on molecule diffusion

Inspired by the diffusion movement phenomenon of the molecule, a molecule-diffusion particle swarm optimization (MDPSO) is presented. The proposed algorithm (MDPSO) has attraction and diffusion phases. Once the diversity of population become low, the individuals will be dispersed and turn into diffusion phases, while if the diversity of population get high, the individuals carry out the attraction phases. It is indicated that MDPSO not only prevents premature convergence to a high degree, but also keeps a more rapid convergence rate than SPSO by applying MDPSO to portfolio problem and comparing with SPSO and other algorithms.

Template Matching Based on an Improved PSO Algorithm

In many cases, the run time of template matching applications is dominated by the repeating of similar calculation and exploring of the optimum result. A new approach using accelerated species based particle swarm optimization for template matching problem (TMP) is proposed. Through changing the motion mode of particles in PSO, its shortage -- being easy to fall into local optimum, is overcome. The using of this improved PSO on instances proves the effectiveness of this approach.

An improved single variable first-order grey model

Grey system theory can effectively deal with incomplete and uncertain information. The grey model (GM) is the core of grey system theory, which collects available data to obtain internal regularity without using any assumptions. To further improve the precision of the prediction, this paper proposes an optimized GM(1,1) (OGM), which improves traditional GM(1,1) in two aspects: one is to improve the whitening equation by using the least square method; the other is to employ a technique of dynamic forecasting with recursive compensation by grey numbers of identical dimensions. The cases studies in population prediction and urban water demand prediction reveal that the improvement is definitely effective and the proposed OGM has not only greater precision but also higher stability than TGM.

Evaluation of Enterprise Innovation Ability Based on Rough Set Theory

The creative ability index is quite important for enterprises. This paper proposes an evaluation method for enterprise innovation ability based on rough set theory. Firstly, an index reduction which effects the enterprises based on the rough set theory is carried out, then the major indexes of enterprise development ability is extracted. Secondly, the standardized processing of all kinds of index values based on the membership function makes them have a unified dimension thus possessing comparability. Thirdly, a comprehensive evaluation of enterprise creative ability via the principal component analysis is performed. Finally, we obtain the creative ability level of that enterprise compared with other ones by using its concrete statistical data to prove its effectiveness.