Mobile Robot Path Planning by Improved Brain Storm Optimization Algorithm

Tuba, Eva; Strumberger, Ivana; Živković, Dejan; Bačanin, Nebojša; Tuba, Milan

doi:10.1109/cec.2018.8477928

Cited by 45 publications

(15 citation statements)

References 36 publications

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“…In addition to the mentioned swarm intelligence algorithms, many others have also proven to be efficient optimization approaches, for example elephant herding optimization (EHO) [5,7,31,32,77], the fireworks algorithm (FWA), [78][79][80], and brain storm optimization (BSO) [81].…”

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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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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“…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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“…Besides all above mentioned, other swarm intelligence approaches that are worth of mentioning encompass: seeker optimization algorithm (SOA) [33], cuckoo search (CS) [34], [35], [36], bran storm optimization (BSO) [37], [38], bat algortithm (BA) [39], [40], and state-of-the-art approach for cobinatorial optimization, ant colony optimization (ACO) [41].…”

Section: Review Of Swarm Intelligence Metaheuristicsmentioning

confidence: 99%

Hybridized Particle Swarm Optimization for Constrained Problems

Džakula¹,

Štumberger²,

Tuba³

et al. 2019

Proceedings of the International Scientific Conference - Sinteza 2019

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This paper presents hybridized implementation of the well-known particle swarm optimization algorithm that belongs to the family of swarm intelligence metaheuristics. The proposed approach was adapted for tackling constrained optimization problems. With the basic goals to enhance the converge of the algorithm and to improve the exploitation-exploration tradeoff, the mechanism that replaces exhausted solutions from the population with randomly generated solutions from the search domain was adopted from the artificial bee colony approach. Proposed metaheuristic was tested on standard constrained engineering benchmark, and comparative analysis with other state-of-the-art algorithms was conducted. Empirical results obtained from practical simulations proved that the hybridized particle swarm optimization for constrained problems is able to successfully tackle this type of NP hard challenges.

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Mobile Robot Path Planning by Improved Brain Storm Optimization Algorithm

Cited by 45 publications

References 36 publications

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

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

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Hybridized Particle Swarm Optimization for Constrained Problems

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