Abstract. Abstract—Glowworm swarm optimization (GSO) algorithm is a new intelligent optimization algorithm. Based on the problems of GSO, such as easy to fall into local optimum, slow convergence speed and low optimization precision, an improved GSO with group division is presented.

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related. The list of acronyms and abbreviations related to GSO - Glowworm Swarm Optimization

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Glowworm Swarm Optimization (GSO) algorithm is a derivative-free, meta-heuristic algorithm and mimicking the glow behavior of glowworms which can efficiently capture all the maximum multimodal function. Nevertheless, there are several weaknesses to locate the global optimum solution for instance low calculation accuracy, simply falling into the local optimum, convergence rate of success and ...

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Glowworm swarm optimization (GSO) is a well-established swarm-intelligencebased optimization technique mainly used for identifying peaks of all local optima of multimodal functions rather than just the global optima as done by most other similar algorithms. The main application of GSO is in cases where the optima are created by sources of certain signals, which interact to create these peaks ...

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Using Glowworm Swarm Optimization Algorithm for Clustering Analysis Zhengxin Huang, Yongquan Zhou Journal of Convergence Information Technology, Volume 6, Number 2. February 2011 A full parameters analysis is found in Krishnanand and Ghose (2008b), show that the choice of these parameters has some influence on the performance of the algorithm.

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DOI: 10.14257/ijast.2016.96.07 Corpus ID: 63252091. Glowworm Swarm Optimization (GSO) for Cloud Jobs Scheduling @article{Esa2016GlowwormSO, title={Glowworm Swarm Optimization (GSO) for Cloud Jobs Scheduling}, author={Dolagi Izzat Esa and Adil Yousif}, journal={International journal of advanced science and technology}, year={2016}, volume={96}, pages={71-82} }

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2. Algorithms 2.1. Glowworm Swarm Optimization In GSO a swarm is composed of N agents called glowworms. A state of a glowworm i at time t can be described by the following set of variables: a position in the search space

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This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm.

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Glowworm swarm optimization (GSO) is a swarm intelligence based algorithm, introduced by K.N. Krishnanand and D. Ghose in 2005, for simultaneous computation of… LinkedIn emplea cookies para mejorar la funcionalidad y el rendimiento de nuestro sitio …

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This study proposes glowworm swarm optimization (GSO) algorithm to estimate an improved value of machining performance measurement. GSO is a recent nature-inspired optimization algorithm that ...

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Apr 11, 2018· Glowworm swarm optimization (GSO) is a swarm intelligence based algorithm, introduced by K.N. Krishnanand and D. Ghose in 2005, for simultaneous computation of… Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising.

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This paper presents a review on glowworm swarm optimization (GSO) algorithm based methods. GSO is a current nature-inspired optimization algorithm that simulates the behavior of the lighting worms.

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and swarm intelligence concepts in combination with different objective functions. Glowworm swarm optimization (GSO) has also been in-vestigated. In [33], a basic GSO algorithm has been applied to the multi-level thresholding problem using the criterion of minimum cross entropy. The GSO …

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Glowworm Swarm Optimization: Theory, Algorithms, and Applications provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. Most MATLAB code can be found in Appendix A.

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212 K.N. Krishnanand and D. Ghose / Glowworm swarm based optimization algorithm for multimodal functions range of each agent covers the entire workspace, all the agents move to the global optimum point and the local optima are ignored. Since it is assumed that a priori information about the objective function (e.g., number

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,KrishnanandGhose(2005)[1](Glowworm Swarm Optimization,GSO),。,GSO,,GSO, ...

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May 27, 2016· gso. A Clojure implementation of the Glowworm Swarm Optimization algorithm as described in the original paper from: Krishnanand, K.N. and Ghose, D., "Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions", published in Swarm Intelligence, 3, 2, June 2009, 87-124.

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May 18, 2015· Glow worm Swarm Optimization (GSO) is a new SI-based technique aimed to optimize multi-modal functions, proposed by Krishnanad and Ghose in 2005 [89, 90]. GSO employs physical entities (agents) called glowworms.

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1192 V. Yepes et al. / Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm Latin American Journal of Solids and Structures 11 (2014) 1190-1205 2 THE OPTIMUM DESIGN PROBLEM The structural design problem established for this study aims to minimize the objective function F of equation (1), subject to the constraints represented by equation (2).

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Glowworm swarm optimization (GSO) The glowworm swarm optimization (GSO) algorithm is an optimization technique developed for simultaneous capture of multiple optimums of multi-modal functions. [6] The algorithm utilizes agents called glowworms which use a luminescent quantity called Luciferin to (indirectly) communicate the function-profile ...

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The glowworm swarm optimization (GSO) algorithm is a novel meta-heuristic algorithm based on swarm intelligence, inspired by the light emission behavior of glowworms, which is used to attract a …

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Abstract—This paper reviews glowworm swarm optimization algorithm (GSO), which is a meta-heuristic swarm intelligence algorithm. The GSO algorithm is applied for solving optimization problems. Shortcoming of the GSO algorithm has been identified with the introduction and discussion of the improvement taken place in recent years.

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By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the ...

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Here, the single objective optimization problems are solved using the Glowworm Swarm Optimization (GSO) algorithm, whereas the MO-OPF problems are solved using the multi-objective GSO (MO-GSO) algorithm. The proposed optimization problem is solved on a modified IEEE 30 and 300 bus test systems with wind farms located at different buses in the ...

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This paper investigates a new Glowworm Swarm Optimization (GSO) clustering algorithm for hierarchical splitting and merging of automatic multi-spectral satellite image classification (land cover mapping problem). Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping.

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The glowworm swarm optimization is a swarm intelligence optimization algorithm developed based on the behaviour of glowworms (also known as fireflies or lightning bugs). The GSO algorithm was developed and introduced by K.N. Krishnanand and Debasish Ghose in 2005 at the Guidance, Control, and Decision Systems Laboratory in the Department of ...

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Glowworm swarm optimization (GSO) is a swarm intelligence based algorithm, introduced by K.N. Krishnanand and D. Ghose in 2005, for simultaneous computation of multiple optima of multimodal functions.The algorithm shares a few features with some better known algorithms, such as ant colony optimization and particle swarm optimization, but with ...

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Glowworm swarm optimization (GSO) is proposed by Krishnanand and Ghose as one of the newest nature inspired heuristics, with it's simple model, less adjustable parameters, and fast convergence rate, which can be usually viewed in pattern recognition, routing, combinatorial optimization, and so …

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