Glowworm Swarm Optimization: Algorithm Development

Kaipa, Krishnanand N.; Ghose, Debasish

doi:10.1007/978-3-319-51595-3_2

Cited by 14 publications

(17 citation statements)

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“…When the beacon node rebroadcasts its own data packet, it may add a new error to the previous positioning error, increasing the final error. When the WSN is deployed in a wide range, it will result in a greater positioning error [19].…”

Section: Mean Hop Distance and Floodingmentioning

confidence: 99%

DV-Hop Node Location Algorithm Based on GSO in Wireless Sensor Networks

Song

Zhao

2019

Journal of Sensors

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Node location is one of the most important problems to be solved in practical application of WSN. As a typical location algorithm without ranging, DV-Hop is widely used in node localization of wireless sensor networks. However, in the third phase of DV-Hop, a least square method is used to solve the nonlinear equations. Using this method to locate the unknown nodes will produce large coordinate errors, poor stability of positioning accuracy, low location coverage, and high energy consumption. An improved localization algorithm based on hybrid chaotic strategy (MGDV-Hop) is proposed in this paper. Firstly, a glowworm swarm optimization of hybrid chaotic strategy based on chaotic mutation and chaotic inertial weight updating (MC-GSO) is proposed. The MC-GSO algorithm is used to control the moving distance of each firefly by chaos mutation and chaotic inertial weight when the firefly falls into a local optimum. The experimental results show that MC-GSO has better convergence and higher accuracy and avoids the premature convergence. Then, MC-GSO is used to replace the least square method in estimating node coordinates to solve the problem that the localization accuracy of the DV-Hop algorithm is not high. By establishing the error fitness function, the linear solution of coordinates is transformed into a two-dimensional combinatorial optimization problem. The simulation results and analysis confirm that the improved algorithm (MGDV-Hop) reduces the average location error, increases the location coverage, and decreases and balances the energy consumption as compared to DV-Hop and the location algorithm based on classical GSO (GSDV-Hop).

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Section: Mean Hop Distance and Floodingmentioning

confidence: 99%

DV-Hop Node Location Algorithm Based on GSO in Wireless Sensor Networks

Song

Zhao

2019

Journal of Sensors

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“…This is an evolutionary optimization that is used to find the global maxima or minima of a given multi-modal function. It is based on the glowworm in which the less luminous glow worms move towards the more luminous glow worms that are nearby [6]. The algorithm of GSO consists of several steps defining how each glowworm behaves and how they are used to determine the peaks of a multi-modal function.…”

Section: A Glowworm Swarm Optimisation (Gso)mentioning

confidence: 99%

“…The algorithm of GSO consists of several steps defining how each glowworm behaves and how they are used to determine the peaks of a multi-modal function. There are three basic mechanisms at work [6]. Fitness broadcast is carried in the luminescent pigment of the glowworm, called luciferin.…”

Section: A Glowworm Swarm Optimisation (Gso)mentioning

confidence: 99%

“…Fitness broadcast is carried in the luminescent pigment of the glowworm, called luciferin. It is proportional to the value of the function at their current position and this works under the assumption that the luciferin sensed by other glowworms does not change with distance [6]. A glowworm tends to move towards its neighbour if the neighbour glows brighter than itself.…”

Section: A Glowworm Swarm Optimisation (Gso)mentioning

confidence: 99%

“…A glowworm tends to move towards its neighbour if the neighbour glows brighter than itself. In case of multiple such neighbours, a probabilistic mechanism is used [6]. A glowworm will take another glowworm to be in its neighbourhood only if it is within its neighbourhood radius.…”

Section: A Glowworm Swarm Optimisation (Gso)mentioning

confidence: 99%

See 2 more Smart Citations

Tuning of PID Controller using Glowworm Swarm Optimisation on a Satellite Attitude Control Reaction Wheel

Sharma¹,

Nair

2019

IJRTE

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This paper attempts to use GSO algorithm to tune a PID controller that can be used to control a satellite using reaction wheels. These have a higher order transfer function and the controller will be more difficult to tune due to this. To do this, a satellite is chosen which controls its attitude using reactions wheels. An axis is chosen and the reaction wheel along this axis is taken into consideration. A PID controller is then attached to this system. PID (Proportional-Integral-Derivative) are one of the most popular controllers used due to their broad applications and easy to design nature. The PID controller is tuned using Glowworm Swarm Optimization (GSO) algorithm and then the system is checked against a step input. The optimization of the controller is done by minimizing the time weighed absolute error cost function.

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Multimodal Biometric System Using Ear and Palm Vein Recognition Based on GwPeSOA: Multi-SVNN for Security Applications

Vijay¹,

Indumathi²

2020

New Trends in Computational Vision and Bio-Inspired Computing

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Glowworm Swarm Optimization: Algorithm Development

Cited by 14 publications

References 0 publications

DV-Hop Node Location Algorithm Based on GSO in Wireless Sensor Networks

DV-Hop Node Location Algorithm Based on GSO in Wireless Sensor Networks

Tuning of PID Controller using Glowworm Swarm Optimisation on a Satellite Attitude Control Reaction Wheel

Multimodal Biometric System Using Ear and Palm Vein Recognition Based on GwPeSOA: Multi-SVNN for Security Applications

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