A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

Hu, Wenbin; Liang, Huanle; Peng, Chao; Du, Bo; Hu, Qi

doi:10.3390/e15041247

Cited by 45 publications

(18 citation statements)

References 35 publications

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“…The particle swarm optimization is easy to implement and calculated easily within a short time. It has been applied to many network issues such as resource allocation problem [45], routing algorithms [46], and the concept of information theory and entropy were used in the development of particle swarm optimization. In this paper, the discrete particle swarm optimization (DPSO) [43] is applied to obtain the solution to the proposed optimization problem.…”

Section: An Optimized Handover Schemementioning

confidence: 99%

Modeling and Performance Evaluation of a Context Information-Based Optimized Handover Scheme in 5G Networks

Seo

Chung

2017

Entropy

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Abstract:Recently, green networks are considered as one of the hottest topics in Information and Communication Technology (ICT), especially in mobile communication networks. In a green network, energy saving of network nodes such as base stations (BSs), switches, and servers should be achieved efficiently. In this paper, we consider a heterogeneous network architecture in 5G networks with separated data and control planes, where basically a macro cell manages control signals and a small cell manages data traffic. Then, we propose an optimized handover scheme based on context information such as reference signal received power, speed of user equipment (UE), traffic load, call admission control level, and data type. In this paper, the main objective of the proposed optimal handover is either to reduce the number of handovers or the total energy consumption of BSs. To this end, we develop optimization problems with either the minimization of the number of total handovers or the minimization of energy consumption of BSs as the objective function of the optimization problem. The solution of the optimization problem is obtained by particle swarm optimization, since the developed optimization problem is an NP hard problem. Performance analysis results via simulation based on various probability distributions of the characteristics of UE and BS show that the proposed optimized handover based on context information performs better than the previous call admission control based handover scheme, from the perspective of the number of handovers and total energy consumption. We also show that the proposed handover scheme can efficiently reduce either the number of handovers or the total energy consumption by applying either handover minimization or energy minimization depending on the objective of the application.

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Section: An Optimized Handover Schemementioning

confidence: 99%

Modeling and Performance Evaluation of a Context Information-Based Optimized Handover Scheme in 5G Networks

Seo

Chung

2017

Entropy

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“…Meta-heuristic algorithms, which often couple exploring the search space with its intensive exploitation, allow for existing infeasible solutions and deteriorating their quality temporarily. Such approaches include simulated annealing (Chiang and Russell 1996), tabu searches (Ho and Haugland 2004), swarm optimization (Hu et al 2013), ant colony systems (Gambardella et al 1999;Gomez et al 2014), hybrid techniques (Liu et al 2014), and many more (Bräysy and Gendreau 2005;Coltorti and Rizzoli 2007;Banos et al 2013).…”

Section: Vehicle Routing Problem With Time Windowsmentioning

confidence: 99%

“…Therefore, various heuristic algorithms that do not guarantee obtaining the optimal solution but execute very fast have been introduced to solve the VRPTW in a short time, and became a main stream of development in this field. They encompass simulated annealing (Zhong and Pan 2007), tabu searches (Ho and Haugland 2004), ant colony systems (Gambardella et al 1999;Gomez et al 2014), swarm optimization algorithms (Hu et al 2013), evolutionary approaches (Repoussis et al 2009), genetic and memetic algorithms (GAs and MAs) (Ghoseiri and Ghannadpour 2010;Nagata et al 2010;Nalepa and Czech 2013;Vidal et al 2013;, and more (Bräysy and Gendreau 2005).…”

Section: Introductionmentioning

confidence: 99%

Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

Nalepa

Błocho²

2015

Soft Comput

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This paper presents an adaptive memetic algorithm to solve the vehicle routing problem with time windows (VRPTW). It is a well-known NP-hard discrete optimization problem with two objectives-to minimize the number of vehicles serving a set of geographically dispersed customers, and to minimize the total distance traveled in the routing plan. Although memetic algorithms have been proven to be extremely efficient in solving the VRPTW, their main drawback is an unclear tuning of their numerous parameters. Here, we introduce the adaptive memetic algorithm (AMA-VRPTW) for minimizing the total travel distance. In AMA-VRPTW, a population of solutions evolves with time. The parameters of the algorithm, including the selection scheme, population size and the number of child solutions generated for each pair of parents, are adjusted dynamically during the search. We propose a new adaptive selection scheme to balance the exploration and exploitation of the solution space. Extensive experimental study performed on the well-known Solomon's and Gehring and Homberger's benchmark sets confirms the efficacy and convergence capabilities of the proposed AMA-VRPTW. We show that it is very competitive compared with other state-of-the-art techniques. Finally, the influence of the proposed adaptive schemes on the AMA-VRPTW behavior and performance is investigated in a thorough sensitivity analysis. This analysis is complemented with

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“…Also, it is less dependent of initial points than other optimization algorithms and there exist techniques which ensure convergence. Therefore, it has been applied to network issues such as the resource allocation problem [64] and routing algorithms [65]. Table 2 shows the notations used in the development of the optimization problem.…”

Section: Performance Analysismentioning

confidence: 99%

An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization

Kang

Chung

2017

Energies

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Abstract:Reducing energy consumption of mobile communication networks has gained significant attentions since it takes a major part of the total energy consumption of information and communication technology (ICT). In this paper, we consider 5G networks with heterogeneous macro cells and small cells, where data and control planes are separated. We consider two types of data traffic, i.e., low rate data traffic and high rate data traffic. In basic separation architecture, a macro cell base station (MBS) manages control signals, while a small cell base station (SBS) manages both low rate data traffic and high rate data traffic. In the considered modified separation architecture, an MBS manages control signals and low rate data traffic, while an SBS manages high rate data traffic. Then, an efficient energy saving scheme for base stations (BSs) is proposed, where the state of a BS is determined depending on the number of user equipments (UEs) that request high rate data traffic and the number of UEs that exist under the overlapping areas commonly covered by the considered BS and the neighbor BSs. We formulate an optimization problem for the proposed energy saving scheme and obtain the solution using particle swarm optimization (PSO). Numerical results show that the proposed energy saving scheme in the modified separated network architecture has better energy efficiency compared to the conventional energy saving schemes in both basic and modified separated network architectures. Also, the proposed energy saving scheme has lower aggregate delay.

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A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

Cited by 45 publications

References 35 publications

Modeling and Performance Evaluation of a Context Information-Based Optimized Handover Scheme in 5G Networks

Modeling and Performance Evaluation of a Context Information-Based Optimized Handover Scheme in 5G Networks

Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization

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