Comparison of particle swarm optimization and dynamic programming for large scale hydro unit load dispatch

This paper addresses the Capacitated Vehicle Routing Problem (CVRP) with a homogenous fleet of vehicles serving a large customer base. The authors propose a multi-phase heuristic that clusters the nodes based on proximity, orients them along a route, and allots vehicles. For the final phase of determining the routes for each vehicle, they have developed a Particle Swarm Optimization (PSO) approach. Benchmark datasets as well as hypothetical datasets have been used for computational trials. The proposed heuristic is found to perform exceedingly well even for large problem instances, both in terms of quality of solutions and in terms of computational effort.

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QALPA- An Efficient QoS Aware Hybrid Localization Model Using Particle Swarm Optimization and Ant Colony Optimization for Cognitive Wireless Sensor Networks

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2021.0228.26 ◽

2021 ◽

Vol 14 (1) ◽

pp. 270-280

Author(s):

Abhijit Halkai ◽

◽

Sujatha Terdal ◽

Keyword(s):

Particle Swarm Optimization ◽

Sensor Networks ◽

Ant Colony Optimization ◽

Large Scale ◽

Cluster Head ◽

Particle Swarm ◽

Base Station ◽

Ant Colony ◽

Swarm Optimization ◽

Localization Model

A sensor network operates wirelessly and transmits detected information to the base station. The sensor is a small sized device, it is battery-powered with some electrical components, and the protocols should operate efficiently in such least resource availability. Here, we propose a novel improved framework in large scale applications where the huge numbers of sensors are distributed over an area. The designed protocol will address the issues that arise during its communication and give a consistent seamless communication system. The process of reasoning and learning in cognitive sensors guarantees data delivery in the network. Localization in Scarce and dense sensor networks is achieved by efficient cluster head election and route selection which are indeed based on cognition, improved Particle Swarm Optimization, and improved Ant Colony Optimization algorithms. Factors such as mobility, use of sensor buffer, power management, and defects in channels have been identified and solutions are presented in this research to build an accurate path based on the network context. The achieved results in extensive simulation prove that the proposed scheme outperforms ESNA, NETCRP, and GAECH algorithms in terms of Delay, Network lifetime, Energy consumption.

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Hybrid Particle Swarm Optimization for Multi-Sensor Data Fusion

Sensors ◽

10.3390/s18092792 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2792 ◽

Cited By ~ 5

Author(s):

Hyunseok Kim ◽

Dongjun Suh

Keyword(s):

Particle Swarm Optimization ◽

Data Fusion ◽

Large Scale ◽

Synergistic Effects ◽

Particle Swarm ◽

Sensor Data ◽

Fusion Model ◽

Swarm Optimization ◽

Hybrid Particle ◽

Hybrid Particle Swarm Optimization

A hybrid particle swarm optimization (PSO), able to overcome the large-scale nonlinearity or heavily correlation in the data fusion model of multiple sensing information, is proposed in this paper. In recent smart convergence technology, multiple similar and/or dissimilar sensors are widely used to support precisely sensing information from different perspectives, and these are integrated with data fusion algorithms to get synergistic effects. However, the construction of the data fusion model is not trivial because of difficulties to meet under the restricted conditions of a multi-sensor system such as its limited options for deploying sensors and nonlinear characteristics, or correlation errors of multiple sensors. This paper presents a hybrid PSO to facilitate the construction of robust data fusion model based on neural network while ensuring the balance between exploration and exploitation. The performance of the proposed model was evaluated by benchmarks composed of representative datasets. The well-optimized data fusion model is expected to provide an enhancement in the synergistic accuracy.

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Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud

Organizational Efficiency through Intelligent Information Technologies ◽

10.4018/978-1-4666-2047-6.ch006 ◽

2012 ◽

pp. 88-107 ◽

Cited By ~ 1

Author(s):

R. Jeyarani ◽

N. Nagaveni ◽

R. Vasanth Ram

Keyword(s):

Particle Swarm Optimization ◽

Large Scale ◽

Virtual Machines ◽

Particle Swarm ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Resource Dynamics ◽

Virtual Machine Provisioning ◽

Dynamic Resource ◽

Self Adaptive

Cloud Computing provides dynamic leasing of server capabilities as a scalable, virtualized service to end users. The discussed work focuses on Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data-center. The context of the environment is a large scale, heterogeneous and dynamic resource pool. Nonlinear variation in the availability of processing elements, memory size, storage capacity, and bandwidth causes resource dynamics apart from the sporadic nature of workload. The major challenge is to map a set of VM instances onto a set of servers from a dynamic resource pool so the total incremental power drawn upon the mapping is minimal and does not compromise the performance objectives. This paper proposes a novel Self Adaptive Particle Swarm Optimization (SAPSO) algorithm to solve the intractable nature of the above challenge. The proposed approach promptly detects and efficiently tracks the changing optimum that represents target servers for VM placement. The experimental results of SAPSO was compared with Multi-Strategy Ensemble Particle Swarm Optimization (MEPSO) and the results show that SAPSO outperforms the latter for power aware adaptive VM provisioning in a large scale, heterogeneous and dynamic cloud environment.

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