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In order to solve the high transportation cost problem faced by the third party logistics (3PL) suppliers, from the fourth party logistics ( 4PL) supplier' s point of view, this paper proposed a multi 3PL collaborative routing customization problem. As the problem needed to consider the route and the 3PL simultaneously, this paper designed a hybrid particle swarm optimization based K-shortest path algorithm ( K-PSO). In the experimental analysis, the results show the effectiveness of the K-PSO by comparing with the GA and enumeration algorithm with different nodes' and 3PL suppliers' number. Finally, through the changes of the 3PL suppliers' transit cost, it shows the advantages of the 4PL collaborative transportation. [ABSTRACT FROM AUTHOR]

Aiming at the kinematics of single-segment and multi-segment continuum robots, this paper proposed a complete forward and inverse kinematics analysis method combining segmented constant curvature and particle swarm algorithm. Taking the double-segment wire-driven continuum robot as the research object, it designed the robot prototype with translation segment. Then it established the mutual mapping between the driving space and the joint space using the piecewise constant curvature method, and obtained the positive mapping relationship from the joint space to the working space according to the homogeneous transformation. Finally, it used the linearly decreasing weight particle swarm algorithm to realize the inverse mapping from the workspace to the joint space . The kinematics of the two-segment continuum robot was simulated and the time-consuming test of the inverse kinematics solution was carried out, and carried out the experimental verification on the developed prototype. The simulation results show the rationality of the proposed kinematics research method and the rapidity of the inverse kinematics solution. The experimental results show that the average position error is less than 6. 22% of the length of the two-segment continuum robot body, which verifies the effectiveness of the proposed kinematics. [ABSTRACT FROM AUTHOR]

For the capacitated vehicle routing problem(CVRP),this paper proposed an imperialist competitive algorithm which integrated a split mechanism to solve the problem.Firstly,combined with the characteristics of CVRP,this algorithm used the encoding and decoding strategies based on greedy criteria to switch from the algorithm space to the solution space.Secondly,this algorithm presented an imperialist splitting mechanism to improve the global search ability of the algorithm,and simultaneously combined with the 2-Opt algorithm to enhance the local search ability.Finally,the results of simulation experiments with 25 benchmark examples indicate that:the proposed algorithm can effectively solve CVRP,and the optimization errors of all examples are less than 1.0%.Furthermore,the proposed algorithm is more efficiently than the existing imperialist competitive algorithms,particle swarm optimization algorithm,genetic algorithm and cuckoo search algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the problem of simultaneous tracking of other vehicles in the process of intelligent vehicles driving on the road, a multi-object tracking method based on binary particle swarm optimization is proposed. In this paper, the state equation and measurement equation of tracking target are established based on radar data, the measurement data of multiple scanning periods of radar are correlated to form a variety of assumptions with possible orbit, and the orbit is scored by likelihood ratio to form a mixed integer linear programming problem, which is solved by discrete binary particle swarm optimization algorithm. The problem of correlation between the measured value and the target at each time is solved, and the track discrimination of the tracking target is realized. Finally, through the simulation in MATLAB, the data results verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposed an improved binary particle swarm optimization (IBPSO) algorithm to solve the controllers' placement problem. IBPSO algorithm determined the current value of particles based on both the global optimal and the individual optimal, thus weakened the influence of particle current value on the next iteration, and accelerated the convergence, which leaded to a better final result. The simulation results show that compared with the BPSO algorithm, the controllers' placement scheme obtained by this algorithm can significantly reduce the number of controllers while realizing the load balancing of the controller. [ABSTRACT FROM AUTHOR]