To solve the task allocation of multi-robot systems, a novel explosive evolution - based immune genetic algorithm (EIGA) is presented. On the basis of the immune genetic algorithm (IGA), the population number of EIGA is increased quickly through explosive evolutionary mode, and then the better individuals are selected through the comparison of allelic genes, which can improve the population quality with the premise of ensuring the population diversity, and enhance the search speed and search precision of EIGA. Compared with the IGA and genetic algorithm (GA), the simulation results indicate that the proposed EIGA is characterized by quick convergence speed, high optimization precision and good stability, and the tasks are allocated rationally and scientifi-cally which realizes the task cooperation of multi-robot systems well.
By making use of the characteristics of ergodicity, randomicity and regularity of chaotic variables and information entropy, a novel chaotic small-world algorithm is presented to improve the optimization performance of the simple small-world algorithm. Compared with the corresponding simple small-world algorithm and the modified genetic algorithm approach, the optimization results of selected complex functions indicate that the proposed chaotic small-world algorithm is characterized by a strong search capability and a quick convergence speed. A study of parameter performance of the chaotic small-world algorithm aids in further improvement of its optimization capability. Additionally, the chaotic small-world algorithm is applied to mobile robot path planning, and the global path is optimized by the chaotic small-world algorithm based on a MAKLINK graph. Finally, experimental results verify the validity of the chaotic smallworld algorithm for robot path planning.
To solve the motion control of mobile robot, a PID control optimized by improved immune clonal algorithm is presented. On the basis of immune clonal algorithm, a new virus evolutionary clonal algorithm (VECA) is provided firstly. The VECA focuses on the virus infection of population after immune mutation, which improves the population diversity and strengths the local search ability of immune clonal algorithm. Then the proposed VECA is used to optimize the algorithm parameters of PID control strategy for the robot motion. The simulation results show that VECA can realize the optimization of PID parameters and improve the control precision of path track.
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