Bio-inspired Computing Based Key Techniques And Applications Research

Bio-inspired computing, enlightened by natural intelligence of biological world, is a novel science for research and development of intelligent computing models and algorithms. Bio-inspired computing, including genetic algorithm, particle swarm optimization, artificial immune algorithm, ant clonal algorithm, neural network algorithm and etc., is considered as efficient optimizing algorithms widely applied in areas as artificial intelligence, machine learning, data mining, robots, network intrusion detection and etc. It provides novel solutions for complex problems. Compared with matured sciences, bio-inspired computing is still young which needs further discuss and research. In order to improve the efficiency, this paper is focused on three classic Bio-inspired computing algorithms: genetic algorithm, particle swarm optimization and artificial immune computing. CCMGA, penalty mechanism based crossover PSO and MPTMA are brought up, and the put forward algorithms are applied to multiple robots path planning and intrusion detection system to testify their efficiency. This paper provides novel ideas and methods for solving premature, local convergence and algorithm complexity problems in Bio-inspired computing.The main work of this thesis can be concluded as:1. Aiming at premature problem of GA optimizing multiple objective problems, drawbacks of traditional coding and fitness function definition in GA are pointed out. Messy GA with variable length of chromosome and hybrid coding is brought up, based on which a global fitness function is defined to implement cooperative co-evolution Messy GA (CCMGA). Besides operations of selection, crossover and mutation in traditional GA, simplify, smooth and repair operators are adopted to assist optimizing objective functions. Aiming at population diversity lost in GA, chaotic mechanism is applied to improve local search ability of CCMGA. Finally CCMGA is applied for multiple robots path planning. Matlab simulation results testify that multiple robots are able to optimize paths in various complicated maps. CCMGA is proved be capable of overcoming premature problem in GA, on basis of which convergence speed and optimized results are improved. 2. Particle swarm optimization (PSO) has obvious shortcoming: local convergence. In this paper, penalty mechanism based self-adaptive crossover PSO is put forward to overcome local convergence problem Based on population diversity model in evolutionary process for particles, crossover operation applied into PSO proved by Cauchy inequality is used to maintain population diversity to overcome problem of premature and local convergence and achieve global optimum solution. Evolutionary state transition process is depicted by Markov model consisted with finite-states, and self-adaptive crossover PSO is implemented. Penalty mechanism based Self-adaptive crossover PSO is designed for solving constrained optimization problems. Based on improved H strategy and simplified P strategy, experiments on benchmark functions demonstrate that parameters affect performance when optimizing unimodal and multimodal problems. A general calculating formula is put forward to control parameters for optimizing unimodal and multi-modal function optimizations respectively, which overcomes the in prior parameter setting difficulty.3. Aiming at optimal matching threshold of antibody generation, a matching threshold prediction model is brought up in this paper. Analyzing antigen and antibody matching principle, optimized threshold is calculated by prediction model. In order to decrease complexity of antibody maturation algorithm, improve detection rate and smaller antibody set, multiple population GA based antibody maturation algorithm (MPTMA) is put forward. Antibody and antigen matching principle is analyzed in morphological space, it's proved that antibody set and redundancy are efficiently decreased by MPTMA, and antibody diversity is maintained, detection rate is improved. MPTMA is proved by theory and simulations that detection rate is improved and time complexity id decreased.4. MPTMA with threshold prediction is applied to intrusion detection system (IDS), so a hybrid intrusion detection system (HIDS) is put forward. Minimal information disperse algorithm is adopted to disperse information and features of original data is extracted by PC A to implement IDS. This thesis puts forward hybrid intrusion detection system (HIDS) which compromise NSA based fast detectors and clonal selection algorithm based intelligent detectors, and the new features concluded by latter detectors are used to update database for better NSA based detection. The cooperation of the two kind detectors promises real-time and high detection rate. By simulation on our lib network, the better performance of brought up HIDS compared with NSA based IDS or clonal selection algorithm based IDS is testified in aspect of real-time, detection rate and false detection rate.The main contributions of this thesis can be concluded as:1. Aiming at premature problem of GA solving multi-objective optimizing problems, a cooperative co-evolution Messy GA is put forward. Global fitness function is defined, assistant operations are used, and chaotic mechanism is adopted to improve local search ability.2. Aiming at local convergence problem of particle swarm optimization, penalty mechanism based crossover PSO is brought up. Self-adaptive crossover models are designed on basis of population convergence principle. Optimized parameters are calculated by designed formula to solve unimodal and multimodal optimizations.3. For antibody maturation algorithm in artificial immune, a matching threshold prediction model is put forward, and tests prove that it overcomes optimal threshold difficulty. And multiple population GA based antibody maturation algorithm is brought up, whose detection rate is improved and time complexity is decreased.4. MPTMA is applied as detector generation algorithm for IDS. Minimal information disperse algorithm and PCA feature extraction algorithm are adopted to realize IDS. A novel HIDS combining NSA based detectors and clonal selection algorithm based detectors is designed. Its performance in optimizing results, convergence results and stability are improved.