Research On Swarm Intelligence Algorithms And Its Application In Function Optimization

Swarm algorithms come from the simulation of nature biology community's intelligent behavior, at present, representative swarm intelligence algorithms include: genetic algorithm, artificial immunity, particle swarm algorithm and ant colony algorithm. They are all, the random optimization algorithms which based on the swarm searches, their characteristic to the optimized objective function which is have no request such as continuous and differential, also the algorithms results are not depend on the selection of the starting value. Therefore, research of swarm intelligence algorithms, has the important theory significance and the practical value. This article has mainly studied the present representative swarm intelligence optimization algorithms in the application of function optimization aspect.An adaptive genetic algorithm (AGA) for function optimization which bases on the principle of genetic algorithm is proposed. The AGA improves the algorithm from two aspects: One is that the ratios of cross and mutation can regulate automatically, the other is that cross and mutation have directionality. By the simulation research of the AGA, analyses the parameters' effect to the algorithm.Finally, the simulation results of the AGA are compared with standard genetic algorithm. The results show that the AGA has more superior performance.An adaptive clonal selection algorithm (ACSA) for function optimization which bases on the principle of clonal selection algorithm is proposed. The ACSA improves the algorithm from two aspects: first, a coefficient is multiplied by a former hypermutation, which is reduced with the evolution algebra; the other is the renewal number of each generation will be updated with the average value degree of antibody swarm. Through the simulation research of the ACSA, and analyse parameters' effect to the algorithm, and compared the simulation results of the ACSA with standard genetic algorithm. The conclusion shows that the ACSA has eximious simplicity and effectiveness.Based on the theory of immune clonal selection algorithm, an adaptive niche clonal selection algorithm (ANCSA) is proposed for multi-modal function optimization. The decisive bit field of the niche can automatically regulate with the variation of the optimized objects' dimension and feasible field, and then it may form different niche, every niche has the ability of immune memory. The simulation results of three typical multi-modal functions are compared with correlative algorithms. The results show that the ANCSA has stronger adaptability and convergence.Combined the basic principle of particle swarm algorithm, a niche particle swarm algorithm (NPSA) is proposed for multi-modal function optimization. According to the convergence analysis and the simulation results of four typical multi-modal functions are compared with correlative algorithms. The conclusion shows that the NPSA has eximious simplicity and effectiveness.A niche ant colony algorithm (NACA) for multi-modal function optimization which bases on principle of ant colony algorithm is devised. The algorithm adopts the real number code, by simulation research of the NACA, and the simulation results of the NACA are compared with correlative algorithms. The results show that the NACA has strong adaptability, good convergence advantages, the parameter can be easily chosen, and extremely suits to the solution which has many optimal solutions or needs to search for the partial optimal solution for multi-modal function optimization simultaneously.