Efficient electron acceleration by a linearly chirped ultrashort laser pulse in vacuum is investigated using the particle swarm optimization method. By applying this method for optimizing the initial parameters of the laser pulse, a pronounced increase in final energy gain of the electron is obtained compared to that expected from the successive optimization method. Our results also suggest that the value of the optimal chirp parameter is independent of laser polarization and the energy gain could be insensitive to the sign of this parameter when the initial phase is optimally adjusted. In addition, utilizing the chirped laser pulse with optimized conditions for acceleration of an electron bunch reveals that the energy spectrum is shifted to considerably higher energies and the spatial distribution is significantly improved in a polarization-dependent manner.

This paper considers the path planning problem for deployment and collection of a marsupial vehicle system which consists of a ground mobile robot and two aerial flying robots. The ground mobile robot, usually unmanned ground vehicle (UGV), as a carrier, is able to deploy and harvest the aerial flying robots, and each aerial flying robot, usually unmanned aerial vehicles (UAVs), takes off from and lands on the carrier. At the same time, owing to the limited duration in the air in one flight, UAVs should return to the ground mobile robot timely for its energy-saving and recharge. This work is motivated by cooperative search and reconnaissance missions in the field of heterogeneous robot system. Especially, some targets with given positions are assumed to be visited by any of the UAVs. For the cooperative path planning problem, this paper establishes a mathematical model to solve the path of two UAVs and UGV. Many real constraints including the maximum speed of two UAVs and UGV, the minimum charging time of two UAVs, the maximum hovering time of UAVs, and the dynamic constraints among UAVs and UGV are considered. The objective function is constructed by minimizing the time for completing the whole mission. Finally, the path planning problem of the robot system is transformed into a multi-constrained optimization problem, and then the particle swarm optimization algorithm is used to obtain the path planning results. Simulations and comparisons verify the feasibility and effectiveness of the proposed method.

In this paper, an optimized hyper beamforming method is presented based on a hyper beam exponent parameter for receiving linear antenna arrays using a new meta-heuristic search method based on the Firefly algorithm (FFA). A hyper beam is derived from the sum and difference beam patterns of the array, each raised to the power of a hyper beam exponent parameter. As compared to the conventional hyper beamforming of the linear antenna array, FFA applied to the hyper beam of the same array can achieve much more reduction in sidelobe level (SLL) and improved first null beam width (FNBW), keeping the same value of the hyper beam exponent. As compared to the uniformly excited linear antenna array with inter-element spacing of λ/2, conventional non-optimized hyper beamforming and optimal hyper beamforming of the same obtained by real-coded genetic algorithm, particle swarm optimization and Differential evolution, FFA applied to the hyper beam of the same array can achieve much greater reduction in SLL and same or less FNBW, keeping the same value of the hyper beam exponent parameter. The whole experiment has been performed for 10-, 14-, and 20-element linear antenna arrays.

In this study, we present the implementation of invasive weed optimization (IWO) in the maximization of main-lobe to side-lobe level for the non-uniform planar antenna array. The antenna arrays investigated in this study are generated using the chaos game algorithm (CGA) and shaped into selected fractal geometries chosen on the basis of their interesting performance. This CGA is picked out in order to overcome the limitations found in the fractal arrays. All the attained results are compared with the results produced by a well-known optimization algorithm that is the particle swarm optimization (PSO). In all the optimized arrays, IWO shows superior optimization results compared with PSO.

This paper presents a new algorithm to find the singularity-free cylindrical workspace of parallel manipulators. Because of the limited workspace of parallel manipulators cluttered with different types of singularities, a simple and robust technique to determine continuous singularity-free zones in the workspace of parallel manipulators is required. Here, the largest singularity-free cylinder within the workspace for any prescribed orientation ranging around a reference orientation angle of moving platform is determined. To this end, Particle Swarm Optimization is utilized to find the closest point on the singularity surface to the axis of the cylinder. By implementing the algorithm on 3-RPR planar parallel manipulator, the results show that this algorithm improves the efficiency and leads to significantly larger singularity-free workspace than those reported earlier.

In this paper, the definition of generalized symmetric Gough–Stewart parallel manipulators is presented. The concept of dynamic isotropy is proposed and the singular values of the bandwidth matrix are introduced to evaluate dynamic isotropy and solved analytically. Considering the payload's mass-geometry characteristics, the formulations for completely dynamic isotropy are derived in close form. It is proven that a generalized symmetric Gough–Stewart parallel manipulator is easer to achieve dynamic isotropy and applicable in engineering applications. An optimization procedure based on particle swarm optimization is proposed to obtain better dexterity and large singularity-free workspace, which guarantees the optimal solution and gives mechanically feasible realization.

The main goal of this paper is the design of 4PUS+1PS parallel manipulator, using an optimization problem that takes into accounts the characteristics of the workspace and dexterity. The optimization problem is formulated considering constraints on actuated and passive joint limits. A comparison between quantum particle swarm Optimization (QPSO) and PSO is developed. Two numerical examples are presented, which reveal the advantages of QPSO to PSO. Moreover, it is shown that by introducing the dexterity index as a quality measure throughout the workspace, the parallel manipulator is improved at the cost of a minor reduction in its workspace.