An improved exact ε constraint and cut-and-solve combined method for biobjective robust lane reservation

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Engineering

AN IMPROVED EXACT Ε-CONSTRAINT AND CUT-AND-SOLVE
COMBINED METHOD FOR BIOBJECTIVE
ROBUST LANE RESERVATION
ABSTRACT
This study investigates a new biobjective lanereservation problem, which is to
exclusively reserve lanes from an existing transportation network for special transport tasks with
given deadlines. The objectives are to minimize the total negative impact on normal traffic due to
the reduction of available lanes for general-purpose vehicles and to maximize the robustness of
the lane-reservation solution against the uncertainty in link travel times. We first define the
robustness for the lanereservation problem and formulate a biobjective mixed-integer linear
program. Then, we develop an improved exact ε-constraint and a cut-and-solve combined
method to generate its Pareto front. Computational results for an instance based on a real
network topology and 220 randomly generated instances with up to 150 nodes, 600 arcs, and 50
tasks demonstrate that the proposed method is able to find the Pareto front and that the proposed
cut-and-solve method is more efficient than the direct use of optimization software CPLEX.

Presented in INFOCOM 2016 http://www3.cs.stonybrook.edu/~chni/publication/optran/ -- We consider the problem of capacitated kinetic clustering in which n n mobile terminals and k k base stations with respective operating capacities are given. The task is to assign the mobile terminals to the base stations such that the total squared distance from each terminal to its assigned base station is minimized and the capacity constraints are satisfied. This paper focuses on the development of distributed and computationally efficient algorithms that adapt to the motion of both terminals and base stations. Suggested by the optimal transportation theory, we exploit the structural property of the optimal solution, which can be represented by a power diagram on the base stations such that the total usage of nodes within each power cell equals the capacity of the corresponding base station. We show by using the kinetic data structure framework the first analytical upper bound on the number of changes in the optimal solution, i.e., its stability. On the algorithm side, using the power diagram formulation we show that the solution can be represented in size proportional to the number of base stations and can be solved by an iterative, local algorithm. In particular, this algorithm can naturally exploit the continuity of motion and has orders of magnitude faster than existing solutions using min-cost matching and linear programming, and thus is able to handle large scale data under mobility.

Ieee transactions 2018 topics on wireless communications for final year stude...

tsysglobalsolutions

Ieee transactions 2018 on wireless communications Title and Abstract

tsysglobalsolutions

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Route optimization via improved ant colony algorithm with graph network

International Journal of Reconfigurable and Embedded Systems

Route optimization problem using vehicle routing problem (VRP) and time window constraint is explained as finding paths for a finite count of vehicles to provide service to a huge number of customers and hence, optimizing the path in a given duration of the time window. The vehicles in the loop have restricted intake of capacity. This path initiates from the depot, delivers the goods, and stops at the depot. Each customer is to serve exactly once. If the arrival of the vehicle is before the time window “opens” or when the time window “closes,” there will be waiting for cost and late cost. The challenge involved over here is to scheduling visits to customers who are only available during specific time windows. Ant colony optimization (ACO) algorithm is a meta-heuristic algorithm stimulated by the growing behaviour of real ants. In this paper, we combine the ACO algorithm with graph network henceforth increasing the number of vehicles in a particular depot for increasing the efficiency for timely delivery of the goods in a particular time width. This problem is solved by, an efficient technique known as the ACO+graph algorithm.

Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.

A collaborated genetic with lion optimization algorithms for improving the qu...

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Vehicular ad-hoc network (VANET) is dynamic and it works on various noteworthy applications in intelligent transportation systems (ITS). In general, routing overhead is more in the VANETs due to their properties. Hence, need to handle this issue to improve the performance of the VANETs. Also due to its dynamic nature collision occurs. Up till now, we have had immense complexity in developing the multi-constrained network with high quality of forwarding (QoF). To solve the difficulties especially to control the congestion this paper introduces an enhanced genetic algorithm-based lion optimization for QoF-based routing protocol (EGA-LOQRP) in the VANET network. Lion optimization routing protocol (LORP) is an optimization-based routing protocol that can able to control the network with a huge number of vehicles. An enhanced genetic algorithm (EGA) is employed here to find the best possible path for data transmission which leads to meeting the QoF. This will result in low packet loss, delay, and energy consumption of the network. The exhaustive simulation tests demonstrate that the EGA-LOQRP routing protocol improves performance effectively in the face of congestion and QoS assaults compared to the previous routing protocols like Ad hoc on-demand distance vector (AODV), ant colony optimization-AODV (ACO-AODV) and traffic aware segment-AODV (TAS-AODV).

Soft Computing Technique Based Enhancement of Transmission System Lodability ...

IJERA Editor

Due to the growth of electricity demands and transactions in power markets, existing power networks need to be enhanced in order to increase their loadability. The problem of determining the best locations for network reinforcement can be formulated as a mixed discrete-continuous nonlinear optimization problem (MDCP). The complexity of the problem makes extensive simulations necessary and the computational requirement is high. This paper compares the effectiveness of Evolutionary Programming (EP) and an ordinal optimization (OO) technique is proposed in this paper to solve the MDCP involving two types of flexible ac transmission systems (FACTS) devices, namely static var compensator (SVC) and thyristor controlled series compensator (TCSC), for system loadability enhancement. In this approach, crude models are proposed to cope with the complexity of the problem and speed up the simulations with high alignment confidence. The test and Validation of the proposed algorithm are conducted on IEEE 14–bus system and 22-bus Indian system.Simulation results shows that the proposed models permit the use of OO-based approach for finding good enough solutions with less computational efforts.

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AN OPTIMAL ROUTE DISCOVERY USING BIOGEOGRAPHY BASED OPTIMIZATION FOR VEHICULA...

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Vehicular ad hoc network (VANET) is a subdivision of the mobile ad hoc networks which uses the moving vehicles as mobile nodes to form the mobile network. In conventional vehicular communications, the restricted radio frequency bandwidth affects the network performances. Therefore, Visible Light Communication (VLC) is integrated with the growing vehicular ad hoc network to obtain high data rate and less energy consumption during the communication. In this paper, vehicular communication is integrated with visible light communication to avoid the issues caused by the restricted radio frequency bandwidth. Moreover, the Routing using Biogeography Based Optimization (RBBO) is proposed to develop an optimal route between the source vehicles to the destination. This research performs two different communications such as vehicle to vehicle and vehicle to the infrastructure. The performance of the RBBOVLC-VANET method is analyzed by means of throughput, packet delivery ratio, delay and routing overhead as well as these performances are compared with the existing method namely ant colony optimization based routing protocol. The throughput of the routing using the biogeography based optimization method is 589.763 kbps for 500 nodes which is high when compared to the existing method.

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IEEE Vehicular technology 2016 Title and Abstract

tsysglobalsolutions

Simultaneous network reconfiguration and capacitor allocations using a novel ...

IJECEIAES

Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Combining two of the numerous strategies for solving these problems and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies, proposing a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA’s results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The network voltage profile was significantly improved as the least voltage at bus 18 was increased to 0.9530 p.u. Furthermore, the overall real power loss was significantly mitigated by 48.87%, which, when compared to the results of other methods, was superior.

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The development of industrial informatization stimulates the implementation of cyber-physical system (CPS) in distribution network. As a close integration of the power network infrastructure with cyber system, the research of design methodology and tools for CPS has gained wide spread interest considering the heterogeneous characteristic. To address the problem of planning communication system in distribution network CPS, at first, this paper proposed an optimization model utilizing topology potential equilibrium. The mutual influence of nodes and the spatial distribution of topological structure is mathematically described. Then, facing the complex optimization problem in binary space with multiple constraints, a novel binary bare bones fireworks algorithm (BBBFA) with a surrogate-assisted model is proposed. In the proposed algorithm, the surrogate model, a back propagation neural network, replaces the complex constraints by incremental approximation of nonlinear constraint functions for reducing the difficulty in finding the optimal solution. The communication system planning of IEEE 39-bus power system, which comprises four terminal units, was optimized. Considering the different heterogeneous degrees, four programs were involved in planning for practical considerations. The simulation results of the proposed algorithm were compared with other representative methods, which demonstrated the effective performance of the proposed method to solve communication system planning for optimizing problems of distribution network.

Matlab abstract 2016

Nexgen Technology

Nexgen Technology Address: Nexgen Technology No :66,4th cross,Venkata nagar, Near SBI ATM, Puducherry. Email Id: praveen@nexgenproject.com. www.nexgenproject.com Mobile: 9751442511,9791938249 Telephone: 0413-2211159. NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km

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An improved exact ε constraint and cut-and-solve combined method for biobjective robust lane reservation

1. AN IMPROVED EXACT Ε-CONSTRAINT AND CUT-AND-SOLVE COMBINED METHOD FOR BIOBJECTIVE ROBUST LANE RESERVATION ABSTRACT This study investigates a new biobjective lanereservation problem, which is to exclusively reserve lanes from an existing transportation network for special transport tasks with given deadlines. The objectives are to minimize the total negative impact on normal traffic due to the reduction of available lanes for general-purpose vehicles and to maximize the robustness of the lane-reservation solution against the uncertainty in link travel times. We first define the robustness for the lanereservation problem and formulate a biobjective mixed-integer linear program. Then, we develop an improved exact ε-constraint and a cut-and-solve combined method to generate its Pareto front. Computational results for an instance based on a real network topology and 220 randomly generated instances with up to 150 nodes, 600 arcs, and 50 tasks demonstrate that the proposed method is able to find the Pareto front and that the proposed cut-and-solve method is more efficient than the direct use of optimization software CPLEX.

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