Abstract
Inspired by related mechanisms of ant colony and idiotypic network hypothesis, a model of ant colony and immune network is proposed to solve the problem of path planning in a complex environment. The mechanism of stimulation and suppression between antigen and antibody is used to find the path, which solves the complex environment modeling of ant colony algorithm, and improves the planning efficiency. The ant colony algorithm is used to search in the antibody network, which improves the optimal path planning effect. To overcome the local minimum, the strategies of retracing and instruction definition punishing are proposed and confirmed their usefulness. Compared with the corresponding ant colony algorithm (ACA) and immune network algorithm (INA), the simulation results indicate that the ant colony and immune network algorithm (AC-INA) is characterized by high convergence speed and short planning path, which solves the path planning well in a complex environment.
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