Glowworm Swarm Optimization - Modifications and Applications

Kaipa, Krishnanand N.; Ghose, Debasish

doi:10.1201/9780429422607-14

Cited by 9 publications

(11 citation statements)

References 1 publication

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“…The GSO operators, also known as glowworms, carry an amount of luminescence called luciferin [ 21 , 22 , 23 ]. Each glowworm uses a proportional artificial luciferin to transfer the relevance of its present location by assessing its neighbours’ reliance on the objective function.…”

Section: Glowworm Swarm Optimisationmentioning

confidence: 99%

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Warpage Optimisation on the Moulded Part with Straight Drilled and Conformal Cooling Channels Using Response Surface Methodology (RSM), Glowworm Swarm Optimisation (GSO) and Genetic Algorithm (GA) Optimisation Approaches

et al. 2021

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It is quite challenging to control both quality and productivity of products produced using injection molding process. Although many previous researchers have used different types of optimisation approaches to obtain the best configuration of parameters setting to control the quality of the molded part, optimisation approaches in maximising the performance of cooling channels to enhance the process productivity by decreasing the mould cycle time remain lacking. In this study, optimisation approaches namely Response Surface Methodology (RSM), Genetic Algorithm (GA) and Glowworm Swarm Optimisation (GSO) were employed on front panel housing moulded using Acrylonitrile Butadiene Styrene (ABS). Each optimisation method was analysed for both straight drilled and Milled Groove Square Shape (MGSS) conformal cooling channel moulds. Results from experimental works showed that, the performance of MGSS conformal cooling channels could be enhanced by employing the optimisation approach. Therefore, this research provides useful scientific knowledge and an alternative solution for the plastic injection moulding industry to improve the quality of moulded parts in terms of deformation using the proposed optimisation approaches in the used of conformal cooling channels mould.

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Section: Glowworm Swarm Optimisationmentioning

confidence: 99%

“…In simple words, the glowworm will be attracted to the brighter neighbour. As a final point, the development of glowworms swarms formed depends on the nearby data and a specific neighbouring interaction to generate multiple multimodal function [ 21 , 23 ].…”

Section: Glowworm Swarm Optimisationmentioning

confidence: 99%

Warpage Optimisation on the Moulded Part with Straight Drilled and Conformal Cooling Channels Using Response Surface Methodology (RSM), Glowworm Swarm Optimisation (GSO) and Genetic Algorithm (GA) Optimisation Approaches

et al. 2021

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“…However, the glowworm can only follow a certain number of worms nearby and even a limited distance. Naturally, glowworms use your bioluminescence light to signal, and taxis toward, in direction for other worms [19]. This algorithm can be see in Algorithm 3.…”

Section: Glowworm Swarm Optimizationmentioning

confidence: 99%

Comparison between Meta-Heuristic Algorithms for Path Planning

Rocha

Vivaldini

2020

Anais Estendidos Do XII Simpósio Brasileiro De Robótica E XVII Simpósio Latino Americano De Robótica (SBR/LARS Estendido 2020)

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Unmanned Aerial Vehicle (UAV) has been increasingly employed in several missions with a pre-defined path. Over the years, UAV has become necessary in complex environments, where it demands high computational cost and execution time for traditional algorithms. To solve this problem meta-heuristic algorithms are used. Meta-heuristics are generic algorithms to solve problems without having to describe each step until the result and search for the best possible answer in an acceptable computational time. The simulations are made in Python, with it, a statistical analyses was realized based on execution time and path length between algorithms Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Glowworm Swarm Optimization (GSO). Despite the GWO returns the paths in a shorter time, the PSO showed better performance with similar execution time and shorter path length. However, the reliability of the algorithms will depend on the size of the environment. PSO is less reliable in large environments, while the GWO maintains the same reliability.

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“…The Glowworm Swarm Optimization (GSO) is a swarm-inspired metaphor approach proposed by Krishnanand and Ghose [29] and reflects the grouped movement of glowworm particles controlled by a luminescent quantity named luciferin. The latter controls the interactive behaviors between glowworms in tracking food and organizing their swarm.…”

Section: Glowworm Swarm Optimization (Gso)mentioning

confidence: 99%

A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach

2020

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The probabilistic Delay Tolerant Network (DTN) routing has been adjusted for vehicular network (VANET) routing through numerous works exploiting the historic routing profile of nodes to forward bundles through better Store-Carry-and-Forward (SCF) relay nodes. In this paper, we propose a new hybrid swarm-inspired probabilistic Vehicular DTN (VDTN) router to optimize the next-SCF vehicle selection using the combination of two bio-metaheuristic techniques called the Firefly Algorithm (FA) and the Glowworm Swarm Optimization (GSO). The FA-based strategy exploits the stochastic intelligence of fireflies in moving toward better individuals, while the GSO-based strategy mimics the movement of glowworm towards better area for displacing and food foraging. Both FA and GSO are executed simultaneously on each node to track better SCF vehicles towards each bundle’s destination. A geography-based recovery method is performed in case no better SCF vehicles are found using the hybrid FA–GSO approach. The proposed FA–GSO VDTN scheme is compared to ProPHET and GeoSpray routers. The simulation results indicated optimized bundles flooding levels and higher profitability of combined delivery delay and delivery probability.

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Glowworm Swarm Optimization - Modifications and Applications

Cited by 9 publications

References 1 publication

Warpage Optimisation on the Moulded Part with Straight Drilled and Conformal Cooling Channels Using Response Surface Methodology (RSM), Glowworm Swarm Optimisation (GSO) and Genetic Algorithm (GA) Optimisation Approaches

Warpage Optimisation on the Moulded Part with Straight Drilled and Conformal Cooling Channels Using Response Surface Methodology (RSM), Glowworm Swarm Optimisation (GSO) and Genetic Algorithm (GA) Optimisation Approaches

Comparison between Meta-Heuristic Algorithms for Path Planning

A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach

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