Query: "pso" discrete vehicle

title: Hybrid Discrete Evolutionary PSO for AC dynamic Transmission Expansion Planning
abstract: Multiyear Transmission Expansion Planning (TEP) aims to determine how and when a transmission network capacity should be expanded taking into account an extended horizon. This is an optimization problem very difficult to solve and that has unique characteristics that increase its complexity such as its non convex search space and its integer and nonlinear nature. This paper describes a hybrid tool to solve the TEP problem, including a first phase to select a list of equipment candidates conducted by a Constructive Heuristic Algorithm (CHA) and a second phase that uses Discrete Evolutionary Particle Swarm Optimization (DEPSO) for the final planning. Both phases use the AC power flow model as a way to improve the realism of the developed tool. The paper includes a case study based on the IEEE 24 Bus Reliability Test System and the results show that tools based on swarm intelligence applied to reduced search spaces are able to find good quality solutions with low computational effort.

title: MODA PSO: Towards Fast Hard Block Legalization for Analytical FPGA Placement
abstract: Placement is a crucial step in the FPGA design tool flow, as it determines the overall performance of the circuits. Unfortunately, it is a time consuming task. Analytical placers have been shown to be the most time efficient while retaining good quality. One way of implementing analytical placement is to use an iterative technique that consists of optimization and look ahead legalization, followed by an optional refinement step. In this work, with the aim towards fast hard block legalization for further accelerating analytical placement, a novel optimizer is proposed based on the modified discrete adaptive particle swarm optimization. The proposed optimizer is embedded into the publicly available analytical placer Liquid. When compared to its version using simulated annealing for hard block legalization, this approach results in a 30% reduction in hard block legalization time and a consequent 5% runtime reduction for the analytical placement, at the cost of only a 1% increase in post routed wirelength and critical path delay. The results indicate that the nature inspired particle swarm optimization is promising for tackling such a problem with new learning strategies and adaptation.

title: Comparison of Exact and Approximate methods for the Vehicle Routing Problem with Time Windows
abstract: This paper presents a comparison of two approaches for solving the vehicle routing problem with time windows (VRPTW) Scheduling of vehicles for pickup and delivery is a common problem in logistics and may be expressed as VRPTW, for which both exact and approximate techniques are available. It is therefore interesting to compare such techniques to evaluate their performance and Figure what is the best option based on the instance features and size. In this work, we compared Mixed Integer Linear Programming (MILP) with Set Based Particle Swarm optimization (S PSO) Both algorithms are tested on the full 56 instances of the Solomon dataset. The results show that the two algorithms perform similarly for lower number of customers while there are significant differences for the cases with higher number of customers. For higher number of customers MILP consistently performs as good as or better than S PSO for the clustered data, both with short and long scheduling horizons, while the S PSO outperforms MILP in most cases with random and mixed random clustered data with long scheduling horizons. Furthermore when the algorithms perform the same with regards to the main objective (number of vehicles) MILP generally achieves a better result in the second objective (distance traveled)

title: Optimization of Tremblay's battery model parameters for plug in hybrid electric vehicle applications
abstract: Accurate modeling of batteries for plug in hybrid vehicle applications is of fundamental importance to optimize the operation strategy, extend battery life and improve vehicle performance. Tremblay's battery model has been specifically designed and validated for electric vehicle applications. Tremblay's parameter identification method is based on evaluating the three remarkable points manually picked from a manufacturer's discharge curve. This method is error prone and the resultant discharge curve may deviate significantly from the experimental curve as reported in previous studies. This paper proposes to use a novel quantum behaved particle swarm optimization (QPSO) parameter estimation technique to estimate the model parameters. The performance of QPSO is compared to that of genetic algorithm (GA) and particle swarm optimization (PSO) approaches. The QPSO technique needs less tuning effort than other techniques since it only uses one tuning parameter. Reducing the number of iterations should be a welcome development in most applications areas. Results show that the QPSO parameter estimation technique converges to acceptable solutions with fewer iterations than that obtained by the GA and the PSO approaches.

title: A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems
abstract: Distributed Constraint Optimization Problems (DCOPs) are a widely studied constraint handling framework. The objective of a DCOP algorithm is to optimize a global objective function that can be described as the aggregation of a number of distributed constraint cost functions. In a DCOP, each of these functions is defined by a set of discrete variables. However, in many applications, such as target tracking or sleep scheduling in sensor networks, continuous valued variables are more suited than the discrete ones. Considering this, Functional DCOPs (F DCOPs) have been proposed that is able to explicitly model a problem containing continuous variables. Nevertheless, the state of the art F DCOPs approaches experience onerous memory or computation overhead. To address this issue, we propose a new F DCOP algorithm, namely Particle Swarm Based F DCOP (PFD) which is inspired by a meta heuristic, Particle Swarm Optimization (PSO) Although it has been successfully applied to many continuous optimization problems, the potential of PSO has not been utilized in F DCOPs. To be exact, PFD devises a distributed method of solution construction while significantly reducing the computation and memory requirements. Moreover, we theoretically prove that PFD is an anytime algorithm. Finally, our empirical results indicate that PFD outperforms the state of the art approaches in terms of solution quality and computation overhead.

title: Compensation of Charging Station Overload via On Road Mobile Energy Storage Scheduling
abstract: Supported by the technical development of electric battery and charging facilities, plug in electric vehicle (PEV) has the potential to be mobile energy storage (MES) for energy delivery from resourceful charging stations (RCSs) to limited capacity charging stations (LCSs) In this paper, we study the problem of using on road PEVs as MESs for energy compensation service to compensate charging station (CS) overload. A price incentive scheme is proposed for power system operator (PSO) to stimulate on road MESs fulfilling energy compensation tasks. The price service interaction between the PSO and MESs is characterized as a one leader, multiple follower Stackelberg game. The PSO acts as a leader to schedule on road MESs by posting service price and on road MESs respond to the price by choosing their service amount. The existence and uniqueness of the Stackelberg equilibrium are validated, and an algorithm is developed to find the equilibrium. Simulation results show the effectiveness of the proposed scheme in utility optimization and overload mitigation.

title: Energy Optimal Trajectory Planning for Electrically Driven Railway Vehicles with Particle Swarm optimization
abstract: This paper deals with the optimization of velocity trajectories for electrically driven railway vehicles w.r.t. the total energy consumption between two successive stops. First, a quasi static model of the complete power train of a standard railway vehicle is described. Based on four principle operating regimes acceleration, cruising, coasting, and braking energy optimal trajectories are computed by determining the optimal regime sequence. For the optimization of the switching points between these operating states, particle swarm optimization (PSO) is used. The benefits of this approach are pointed out by simulation results using validated train models.

title: A Particle Swarm Optimization for Selective Pickup and Delivery Problem
abstract: This paper studies a variant of vehicle routing problem called Selective Pickup and Delivery Problem with Time Windows and Paired Demands (SPDPTWPD) A visiting sequence of each assigned vehicle needs to be determined by respecting the imposed constraints. Like for other combinatorial problems, the optimal solution cannot be obtained in a reasonable time when the size increases. An approached method is thus chosen as an alternative to tackle this issue. The proposed method integrates particle swarm optimization (PSO) with local searches by considering the diversification of PSO and intensification of local search. To validate the method, experiments are made on the benchmarks from the literature. The experiments are divided into two parts. In the first part, a self comparison is made to demonstrate the evolutionary capacity of PSO and the efficiency of proposed local searches. In the second part, the proposed method is compared with a genetic algorithm from the literature. The results show that the method is competitive and efficient.

title: A chemotactic pollution homing UAV guidance system
abstract: Due to their deployment flexibility, Unmanned Aerial Vehicles have been found suitable for many application areas, one of them being air pollution monitoring. In fact, deploying a fleet of Unmanned Aerial Vehicles (UAVs) and using them to take environmental samples is an approach that has the potential to become one of the key enabling technologies to enforce pollution control in industrial or rural areas. In this paper, we propose to use an algorithm called Pollution driven UAV Control (PdUC) that is based on a chemotaxis metaheuristic and a Particle Swarm Optimization (PSO) scheme that only uses local information. Our approach will be used by a monitoring Unmanned Aerial Vehicle to swiftly cover an area and map the distribution of its aerial pollution. We show that, when using PdUC, an implicit priority is applied in the construction of pollution maps, by focusing on areas where the pollutants' concentration is higher. In this way, accurate maps can be constructed in a faster manner when compared to other strategies. We compare PdUC against various standard mobility models through simulation, showing that our protocol achieves better performances, by finding the most polluted areas with more accuracy, within the time bounds defined by the UAV flight time.

title: Set based particle swarm optimization for mapping and scheduling tasks on heterogeneous embedded systems
abstract: Modern heterogeneous multiprocessor embedded platforms is important for the high volume markets that have strict performance. However, it presents many challenges that need to be addressed in order to be efficiently utilized for multitask applications. Since mapping and scheduling problems for multi processors belong to the classic of NP Complete problems, common methods used to solve this kind of problem usually fail. In this paper, we present an algorithm based on the meta heuristic optimization technique, set based discrete particle swarm optimization (S PSO) which efficiently solves scheduling and mapping problems on the target platform. This algorithm can simultaneously addressed the mapping and scheduling problems on a complex and heterogeneous MPSoC and it has better performance than other algorithms in dealing with large scale problems. This algorithm also reduces the execution time of the application by exploring various solutions for mapping and scheduling of tasks and communications. We compare our approach with other heuristics, Ant Colony Optimization (ACO) on the performance to reach the optimum value and on the potential to explore the target platform. The results show that our approach performs better than other heuristics.