In multi-objective particle swarm optimization (MOPSO), the selection of global guides for all partials is vital to improve the convergence and diversity of solutions. In this paper, the related work of global guides searching in MOPSO is introduced, and a new Pareto–based selecting strategy is proposed. Basing on the analysis of the structure and mapping relation of the particle swarm and the nondominated solutions archive, considering the density information, the global guides selecting frequency and other factors, a new gbest selecting strategy for each particle in the swam is presented. Experimental results of contrasting experiments of two typical MOPSO functions demonstrate that the proposed strategy is satisfying.

The power and brake performance of wheeled construction machinery are separately detected on different test equipment in present chassis detection line. A new method is put forward in the veiw of high cost, large area occupied and low treatment efficiency of the traditional line. A DC dynamometer is provided as the power absorbing device and the reverse drag device to combine the two separate platforms in the proposed method, and the mathematical model of DC dynamometer is built based on the principle of chassis performance test. The parameter of dynamometer is determined by calculating, and power and brake detection integration of the wheeled construction machinery is achieved on the integrated test platform.