Particle Swarm Optimization Combined With Heat Motion Mechanism And Its Application

Intelligence is the oldest, most complex and most wonderful topic of the living world. At all times and in all countries, numerous scholars have conducted a panoramic and profound study of it. Half a century ago, a dozen excellent scholars of math, psychology and information theory, to simulate natural intelligence, especially human intelligence with computer, proposed the new subject - "Artificial Intelligence". In the subsequent decades of the development process, this new subject has achieved a considerable progress and formed different academic schools; however, the bottleneck restricting the development of artificial intelligence has also become more and more prominent. As an extension and expansion of traditional artificial intelligence, Computational Intelligence and Artificial Intelligence technology intersect and complement each other, showing an outstanding performance in simulating non-linear reasoning, fuzzy concept, memory, and so on. As a new description method on Intelligence, Swarm Intelligence has gradually become the new hotspot of Computational Intelligence.Particle swarm optimization (PSO) is one of the two typical swarm intelligence optimization algorithms. Because of its simple in principle, both the profound background of the traditional evolutionary computation technology and its own unique optimization performance, it has attracted a wide attention from many scholars in the field of Computational Intelligence even since first proposed. Due to this, "IEEE Transactions on Evolutionary Computation", one of the top journals of computational intelligence, published the PSO's special issue in 2004. Eberhart and Shi in the preface put forward five directions and focuses for future PSO research: algorithm theory, population topology structure, parameter selection and optimization, hybrid algorithms with the other evolutionary computation techniques and applications. Based on this guideline, the mechanism of statistical physics and thermodynamics, which consists of the molecular force, ITO process, and diffusion phenomenon, is utilized to design and modify the PSO algorithm in this paper. And then the improved PSO algorithms are applied to non-linear model parameters estimation, and lastly the PSO algorithm platform is designed and implemented. The main content and innovation points of the dissertation are as follows:Firstly, to maintain the diversity of particles is crucial to improve the performance of PSO algorithm. Enlightened by molecular kinetic theory, the particle swarm optimization algorithm based on the molecular force (MPSO) is put forward. To make an analogy to thermodynamic molecular system, in the MPSO, molecular force between particles, swarm centroid and particle acceleration are introduced and thus particle's velocity updating formula is modified. The molecular force between itself and swarm centroid is presented as an attractive or repulsive force determined by the distance of them, and decides the particle to move towards the swarm centroid or to keep away from it for maintenance of diversity, hence the MPSO could effectively balance the global and local search. In addition, orthogonal test design method is applied to select and optimize the two additional parameters introduced in MPSO.Secondly, in order to improve the convergence rate of PSO, with the inspiration of the Brownian motion, ITO process, and ITO algorithm, a series of hybrid algorithms which mix ITO algorithm and PSO algorithm are proposed. In the first place, the PSO mixed with drift operator (ISPO1) is proposed. There is no speed attribute for particle of IPSO1, and the attractor concept is also introduced. It is proved that IPSO1 is much improved in convergence rate but is still short of sufficient stability comparing to the standard PSO. To solve this problem, the two strategies are taken on the basis of IPSO1. On the one hand, the fluctuation operator of ITO algorithm is introduced constantly and differential mutation operator is used to design the fluctuation operator. On the other hand, thermodynamic selection mechanism, which gives the particle relative energy, level entropy, free energy component, is introduced as well. And further experimental results show that the latter two algorithms retain the fast convergence characteristics of IPSO1, at the same time, possessing better robustness and stability.Thirdly, in view of that the thought of multi-populations can effectively improve the performance of PSO algorithm. Inspired by the phenomenon of the diffusion and migration, the double-swarms particle swarm optimization algorithm (DPSO) is proposed based on the heat diffusion mechanism. Particle diffusion energy, population temperature, and particle diffusion probability are defined in DPSO algorithm. During the evolution of DPSO, the particle of each swarm is chosen into the diffusion pool of each swarm. The diffusion pool of both swarms exchanged and shared information. It can be inferred from the experimental results that the DPSO algorithm has a better evolutionary capability than PSO in the later period.Fourthly, parameter estimation is the critical part of system identification and regression analysis and it relates to the application and promotion of nonlinear model. The parameter estimation problem of nonlinear model is transformed into an unconstrained multi-dimensional function optimization problem, and the four proposed PSO algorithms mentioned above are used to solve this problem, just taking the asymptotic regression model and the logistic model for example which are widespread in natural sciences and social sciences. There are real data, random sample data without noise, and sample data with Gaussian noise in the experiments. The latter two kinds of data are applied to analyze the impact of dimensions of parameter estimation, sampling interval and noise intensity on the algorithm performance, and experimental results show that PSO algorithm is an effective nonlinear model parameter estimation method.Finally, algorithm platform plays a decisive role in ensuring the continuity of algorithms research, saving the research results and conducting comparative analysis, etc. Based on the analysis of both the scope of application and advantages, disadvantages of all design patterns, the strategy pattern is used to design the PSO algorithm platform. A set of strategies classes is applied to package different PSO algorithms. Considering the implementation efficiency of programs and convenience in the graphical display, the mixed programming technology with VC and MATLAB is utilized to implement the platform.