Donkey and smuggler optimization algorithm: A collaborative working approach to path finding

Shamsaldin, Ahmed S.; Rashid, Tarik A.; Agha, Rawan A. Al-Rashid; Al-Salihi, Nawzad K.; Mohammadi, Mokhtar

doi:10.1016/j.jcde.2019.04.004

Cited by 78 publications

(48 citation statements)

References 38 publications

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“…QoS criteria for the service composition problem will be considered in our future work. Also, some new meta-heuristic algorithms, such as WOA-BAT Algorithm [36], Donkey and Smuggler Optimisation Algorithm [37], Fitness Dependent Optimiser [38], and Modified Grey Wolf Optimiser [39] can be applied to evaluate the energy-aware service composition approach in future work.…”

Section: Resultsmentioning

confidence: 99%

An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment

Ibrahim

Rashid

Akinsolu

2020

Journal of Parallel and Distributed Computing

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Godar J. Ibrahim, Tarik A. Rashid, Mobayode O. Akinsolu. An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment, Journal of Parallel and Distributed Computing, AbstractBy increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the nonfunctional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy-saving problem in mobile nodes. To mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms. An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment, Journal of Parallel and Distributed Computing, 2 Godar J. Ibrahim, Tarik A. Rashid, Mobayode O. Akinsolu. An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment, Journal of Parallel and Distributed Computing,

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Section: Resultsmentioning

confidence: 99%

An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment

Ibrahim

Rashid

Akinsolu

2020

Journal of Parallel and Distributed Computing

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“…Finally, adaptation and hybridization of the IFDO with other algorithms will be the main focus of future work. Also, the performance of IFDO can be further evaluated against other popular algorithms, such as WOA-BAT [65], Donkey and Smuggler Optimisation [66], and Modified Grey Wolf Optimiser [67], Modifications of Dragonfly Algorithm [68], Modifications of Backtracking Algorithm [69]. [31].…”

Section: Discussionmentioning

confidence: 99%

Improved Fitness-Dependent Optimizer Algorithm

2020

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The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019. An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems. To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position. Moreover, in determining the weights, the FDO uses the weight factor ( ), which is zero in most cases and one in only a few cases. Conversely, the IFDO performs randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved. In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated. Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results. Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results. The results show that IFDO is practical in some cases, and its results are improved in most cases. Finally, to prove the practicability of the IFDO, it is used in real-world applications.

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Section: Discussionmentioning

confidence: 99%

Improved Fitness-Dependent Optimizer Algorithm

Muhammed¹,

Saeed²,

Rashid³

2020

Preprint

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<div> <table> <tr> <td> <p>The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019. An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems. To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position. Moreover, in determining the weights, the FDO uses the weight factor ( ), which is zero in most cases and one in only a few cases. Conversely, the IFDO performs randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved. In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated. Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results. Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results. The results show that IFDO is practical in some cases, and its results are improved in most cases. Finally, to prove the practicability of the IFDO, it is used in real-world applications.</p> </td> </tr> </table> </div> <br>

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Donkey and smuggler optimization algorithm: A collaborative working approach to path finding

Cited by 78 publications

References 38 publications

An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment

An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment

Improved Fitness-Dependent Optimizer Algorithm

Improved Fitness-Dependent Optimizer Algorithm

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