Ant colony based on cat swarm optimization and application in picking robot path planning

Abstract

In view of solving multi-objective path planning in the static environment, there are some faults for ant colony optimization(ACO), such as the long computation and easy to fall into local optimum. To solve these problems, the ACO based on cat swarm optimization (CSO) algorithm searching model (CSOACO) is presented. In this algorithm, the introduction of CSO algorithm search pattern realizes the local search in the current solution for ant colony individuals, which not only enrich the diversity of solution, but improve the accuracy of the algorithm. Finally, the new algorithm is simulated in MATLAB for picking robot multi-objective path planning problem. Through the simulation analysis, not only set the parameters, but compare CSOACO with other algorithms. Simulation results show that the algorithm can accelerate the convergence speed, search to the global optimal solution and realize the multi-objective path planning of picking robot.