Research On Theories And Algorithms Of Hybrid Immune Optimization And Its Applications

As optimization problems exist widely in scientific research and engineeringpractice, research on optimization problems is of great theoretical significance andpractical value. With characters of self-organization, good diversity and strongrobustness, immune optimization algorithm simulating intelligent informationprocessing mechanism of biological immune system is suit for solving optimizationproblems. Since optimization problems are becoming more and more complex, it ishard to meet the performance requirements of complex optimization problems withfeatures of strong nonlinearity, uncertainty and time variation only by a singleoptimization method. Hybrid immune optimization algorithm not only can offer newidea and effective way for this kind of problem but also is a direction of thedevelopment of optimization theory and algorithms.Inspired by the mechamism of immune system, research on Theories andAlgorithms of Hybrid Immune Optimization and its applications is carried through bycombining with other optimization algorithms in this dissertation. Aiming atcombinatorial optimization problem and numerical optimization problem, a systematicstudy of this dissertation is launched on mechanism model, algorithm design, theoryanalysis, performance testing and algorithm comparison. The performance of hybridimmune optimization algorithm is confirmed through the simulation experiments.Hybrid immune optimization algorithm is used to sliding mode optimization control ofcomplex discrete-time chaotic systems, favorable control performance is achieved.The main work can be summarized as follows:(1) To solve combinatorial optimization problem, utilizing each superiority ofimmune clonal selection algorithm and ant colony algorithm, a serial hybrid algorithm,which combines immune algorithm and ant algorithm with local search algorithmbased on antibody small window (ACLA), is proposed. A mechanism of chaoticdisturbance is introduced into ant colony algorithm to avert precocity and stagnationto a certain extent. In order to improve convergent velocity of clonal selectionalgorithm, the operators of clone expansion and immune gene operation are introducedinto clonal selection algorithm.Through the application of local search algorithm,ACLA can improve searching efficiency. Simulation tests for traveling salesman problem illustrate that ACLA has a remarkable quality of convergent precision and theconvergent velocity.(2) Aiming at combinatorial optimization problem, combining the respectiveadvantages of co-evolutionary algorithm and immune clonal selection algorithm, atwo-floor model based on multiple-population immune evolution as well asHierarchical Co-evolutionary Immune Algorithm(HCIA) based oncompetition-cooperation is put forward. Multiple subpopulations are operated bybottom floor immune operators such as local optimization immunodominance, clonalexpansion based on competition and top floor genetic operators. Through thoseoperators, excellent antibody affinity maturation and diversity of antibodysubpopulation distribution was enhanced, the balance between in the depth andbreadth of the search-optimizing was acquired. Experimental results for travelingsalesman problem, a typical combinatorial optimization problem, indicate that HCIAhas a remarkable quality of the global convergence reliability and convergencevelocity.(3) Focus on global function optimization problem, integrating diversitymechanism of immune algorithm with the thought of co-evolutionary and particleswarm neighborhood information sharing, a novel Multi-subpopulation AdaptivePolymorphic Crossbreeding Particle Swarm Optimization immune co-evolutionaryalgorithm(MAPCPSOI) based on two-layer model is raised. Through the bottom layeradaptive polymorphic crossbreeding particle swarm optimization operation of severalsubpopulations, the MAPCPSOI algorithm, firstly, can ameliorate diversity ofsubpopulation distribution and effectively suppress premature and stagnation behaviorof the convergence process. Secondly, the MAPCPSOI algorithm, by the top layerimmune clonal selection operation of several subpopulations, can significantlyimprove the global optimization performance and further enhance convergenceprecision. Compared with other improved particle swarm optimization algorithms,simulation results of function optimization show that the MAPCPSOI algorithm,especially suitable for solving optimization problems of hyper-high dimensionfunction and other complex function, has more rapid convergence speed and highersolution precision.(4) To address multi-modal function optimization problem, a novel hybridalgorithm(IPSO-P) which combines Improved Particle Swarm Optimization algorithmwith Powell search method and a novel hybrid immune cloud particle swarmoptimization algorithm(PPSO) which integrates Cloud Mutation Particle Swarm Optimization algorithm(CMPSO) with Wavelet Mutation Clonal SelectionAlgorithm(WMCSA) are proposed. The IPSO-P algorithm organically integratesparticle swarm optimization algorithm which has powerful global search capabilitywith Powell search method which has strong local search ability.The IPSO-Palgorithm ensures quick convergent speed and find all extreme points as much aspossible, and solution’s precision is improved. In the PPSO algorithm, cloud mutationoperator based on cloud model is employed to enhance the diversity of population,WMCSA is used to further improve the accuracy of the sub-optimal solutions whichCMPSO has found. The simulation experiments demonstrate the effectiveness of thetwo hybrid algorithms.(5) The hybrid immune cloud particle swarm optimization algorithm (PPSO) isused to sliding mode optimization control of discrete-time chaotic systems, a neuralnetwork sliding mode equivalent control method based on PPSO algorithm isproposed.When taking the output of BP neural network as coefficient of switch part ofsliding mode equivalent control, the method effectively overcome the chatteringphenomenon of conventional sliding mode equivalent control. The PPSO algorithm isapplied to globally optimize the parameters of neural network sliding mode controllerand then to control discrete-time chaotic systems more effective. Simulation resultsshow that the method requires no knowledge about the precise mathematical model ofdiscrete-time chaotic systems with fast response speed, high control precision andstrong anti-interference ability.