Tree Growth Algorithm (TGA): A novel approach for solving optimization problems

Cheraghalipour, Armin; Hajiaghaei–Keshteli, Mostafa; Paydar, Mohammad Mahdi

doi:10.1016/j.engappai.2018.04.021

Cited by 187 publications

(90 citation statements)

References 81 publications

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“…TGA is a recent nature-inspired population-based metaheuristic [33], which is inspired by the growing behavior of tree in the jungle. In TGA, a set of candidate solutions are randomly generated to construct the initial trees in the jungle.…”

Section: Tree Growth Algorithmmentioning

confidence: 99%

“…As discussed in Section 2, the parameter is an important factor which should to be adjusted. In literature [33], the value of is tuned before the simulation and it does not change during the processing. We think this is seemed to unreasonable.…”

Section: Linear Increasing Mechanism For Parameter Tuningmentioning

confidence: 99%

“…Tree growth algorithm, TGA, is a recent nature-inspired population-based metaheuristic which mimics the growing behavior of tree in the jungle. The performance of TGA was evaluated on several well-known benchmarks and engineering problems [33]. The results in the author's reported show that the performance of TGA was very excellent and impressive.…”

Section: Introductionmentioning

confidence: 99%

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Feature Selection Based on a Novel Improved Tree Growth Algorithm

Zhong¹,

Chen²,

Peng³

2020

IJCIS

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Feature selection plays a significant role in the field of data mining and machine learning to reduce the data dimension, speed up the model building process and improve algorithm performance. Tree growth algorithm (TGA) is a recent proposed populationbased metaheuristic, which shows great power of search ability in solving optimization of continuous problems. However, TGA cannot be directly applied to feature selection problems. Also, we find that its efficiency still leave room for improvement. To tackle this problem, in this study, a novel improved TGA (iTGA) is proposed, which can resolve the feature selection problem efficiently. The main contribution includes, (1) a binary TGA is proposed to tackle the feature selection problems, (2) a linearly increasing parameter tuning mechanism is proposed to tune the parameter in TGA, (3) the evolutionary population dynamics (EPD) strategy is applied to improve the exploration and exploitation capabilities of TGA, (4) the efficiency of iTGA is evaluated on fifteen UCI benchmark datasets, the comprehensive results indicate that iTGA can resolve feature selection problems efficiently. Furthermore, the results of comparative experiments also verify the superiority of iTGA compared with other stateof-the-art methods.

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Section: Tree Growth Algorithmmentioning

confidence: 99%

Section: Linear Increasing Mechanism For Parameter Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature Selection Based on a Novel Improved Tree Growth Algorithm

Zhong¹,

Chen²,

Peng³

2020

IJCIS

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“…The MBO has also been implemented for cloudlet scheduling problems in cloud computing environments [4]. Another relatively new swarm approach that is worth mentioning is the tree growth algorithm (TGA) [69]. With many implementations, the TGA is positioned as a robust optimization method [70,71].…”

Section: Swarm Intelligence Overview and Cloud Computing Applicationsmentioning

confidence: 99%

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

et al. 2019

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The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation-exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

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“…Besides all mentioned above, there are also other state-of-the-art swarm intelligence algorithms that showed outstanding performance for tackling various kinds of practical problems, for example ant colony optimization (ACO) [83], brain storm optimization (BSO) [84][85][86], krill herd (KH) [87] algorithm, tree growth algorithm (TGA) [5,88,89], and many others [90][91][92][93].…”

Section: Review Of Swarm Intelligence Metaheuristics and Its Applicatmentioning

confidence: 99%

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Strumberger

Tuba

Bačanin

et al. 2019

JSAN

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Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.

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Tree Growth Algorithm (TGA): A novel approach for solving optimization problems

Cited by 187 publications

References 81 publications

Feature Selection Based on a Novel Improved Tree Growth Algorithm

Feature Selection Based on a Novel Improved Tree Growth Algorithm

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

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