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This paper introduces hybrid iterative algorithms that combine Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms for Field Programmable Gate Array (FPGA) placement by considering adaptive inertia weight and local minima avoidance. The algorithms target to optimize the wire-length of the nets, run time and critical path delay in the placement of logic blocks. Using the adaptive inertia weight parameter and local minima avoidance, the hybrid PSO-SA algorithm is modified to Time-varying PSO-SA (TPSO-SA) and Modified PSO-SA (MPSO-SA) algorithm, respectively. These different hybrid PSO-SA algorithms are checked for efficiency by comparing with the Versatile Place and Route (VPR) algorithm of the Verilog to Routing (VTR) tool using Microelectronics Centre of North Carolina (MCNC) benchmark circuits. The hybrid PSO-SA algorithms give 5–37% better results for wire-length cost and 20–58% reduction in runtime compared to the VPR placement algorithm on different benchmark circuits. Critical path delay is also taken into consideration. [ABSTRACT FROM AUTHOR]

Path planning is the essential aspect of autonomous flight system for unmanned aerial vehicles (UAVs). An improved particle swarm optimization (PSO) algorithm, named GBPSO, is proposed to enhance the performance of three-dimensional path planning for fixed-wing UAVs in this paper. In order to improve the convergence speed and the search ability of the particles, the competition strategy is introduced into the standard PSO to optimize the global best solution during the process of particle evolution. More specifically, according to a set of segment evaluation functions, the optimal path found by single waypoint selection way is adopted as one of the candidate global best paths. Meanwhile, based on the particle as an integrated individual, an optimal trajectory from the start point to the flight target is generated as another global best candidate path. Subsequently, the global best path is determined by considering the pre-specified elevation function values of two candidate paths. Finally, to verify the performance of the proposed method, GBPSO is compared with some existing path-planning methods in two simulation scenarios with different obstacles. The results demonstrate that GBPSO is more effective, robust and feasible for UAV path planning. [ABSTRACT FROM AUTHOR]