Improved Immune Clonal Selection Algorithm and its Application in Power Network Planning

Power network planning is a NP hard problem difficult to be solved. It can be contributed to similar TSP problem. Aiming at the slow convergence speed of the traditional immune clonal selection algorithm (ICA), adaptive immune clonal selection algorithm without memory（AICA）and adaptive immune clonal selection algorithm with memory(AICAM) are proposed respectively based on the combination of adaptive algorithm of clonal probability, immune probability , and group disaster algorithm. The two proposed algorithms have been applied to Power network planning problem. The adaptive algorithm has strong global search ability and weak local search ability at early evolution. Global search ability is weakened and local search ability is enhanced with the process of evolution in order to find global optimal point. The application of group disaster algorithm can enhance the diversity of the population and avoid the premature problems to some extent. Simulation results indicate that compared with the traditional immune clonal selection algorithm(ICA), the proposed algorithms can enhance the diversity of the population, avoid the premature problems, and can accelerate convergence speed in some extent.