A multi-swarm-evolutionary structure based on the parasitic relationship in the biosphere is proposed in this paper and, according to the conception, the Para-PSO-ABC algorithm (ParaPA), combined with merits of the modified particle swarm optimization (MPSO) and artificial bee colony algorithm (ABC), is conducted with the multimodal routing strategy to enhance the safety and the cost issue for the mobile robot path planning problem. The evolution is divided into three stages, where the first is the independent evolutionary stage, with the same evolution strategies for each swarm. The second is the fusion stage, in which individuals are evolved hierarchically in the parasitism structure. Finally, in the interaction stage, a multi-swarm-elite strategy is used to filter the information through a predefined cross function among swarms. Meanwhile, the segment obstacle-avoiding strategy is proposed to accelerate the searching speed with two fitness functions. The best path is selected according to the performance on the safety and consumption issues. The introduced algorithm is examined with different obstacle allocations and simulated in the real routing environment compared with some typical algorithms. The results verify the productiveness of the parasitism-relation-based structure and the stage-based evolution strategy in path planning.

This paper studies the data-driven optimal synchronization problem for complex networks (CNs) with unknown dynamics. By using pre-compensation technology, a compensator and a controller are proposed. Then, an augmented error system is constructed, which can circumvent the requirement of system dynamics. It is revealed that the the optimal synchronization control of CNs works as the optimal regulation of the augmented system with a performance function. A novel policy iteration (PI) algorithm is given to ensure that the iterative performance function can converge to the optimal value which is the solution of the coupled Hamilton-Jacobi-Bellman equation (HJB), which means that the optimal regulation of the augmented system can be solved and the synchronization can be achieved. Based on this, a novel data-driven control scheme is proposed, which is composed of parts: compensator, controller and critic network. The iterative performance is generated by critic network. The compensator is used to construct the control parameter by using performance and the controller is used to construct control input by using control parameter. Both compensator and critic network are implemented by neural networks (NNs) and only depend on the process sampling data. Finally, we use robot network as an example to verify the effectiveness of proposed control scheme.