Modified Cuckoo Optimization Algorithm (MCOA) to solve Precedence Constrained Sequencing Problem (PCSP)

Maadi, Mansoureh; Javidnia, Mohammad; Ramezani, Rohollah

doi:10.1007/s10489-017-1022-0

Cited by 7 publications

(2 citation statements)

References 38 publications

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“…The cuckoo search algorithm has been successfully applied in several optimization problems such as job scheduling [46], production planning problem [47], and precedence constrained sequencing [48].…”

Section: Cuckoo Optimization Algorithmmentioning

confidence: 99%

“…In this section, to determine the proper value of the algorithm parameters, we conduct a sensitivity analysis considering all the parameters of Cuckoo-PC. The reasonable range of parameters is derived from related papers, including [46][47][48]. Then, multiple values within the specified ranges are checked to find the proper value of each parameter.…”

Section: Sensitivity Analysismentioning

confidence: 99%

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Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs

Tahmasebi

Safi

Zolfi

et al. 2020

Sensors

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Due to reliability and performance considerations, employing multiple software-defined networking (SDN) controllers is known as a promising technique in Wireless Sensor Networks (WSNs). Nevertheless, employing multiple controllers increases the inter-controller synchronization overhead. Therefore, optimal placement of SDN controllers to optimize the performance of a WSN, subject to the maximum number of controllers, determined based on the synchronization overhead, is a challenging research problem. In this paper, we first formulate this research problem as an optimization problem, then to address the optimization problem, we propose the Cuckoo Placement of Controllers (Cuckoo-PC) algorithm. Cuckoo-PC works based on the Cuckoo optimization algorithm which is a meta-heuristic algorithm inspired by nature. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. To evaluate the performance of Cuckoo-PC, we compare it against a couple of state-of-the-art methods, namely Simulated Annealing (SA) and Quantum Annealing (QA). The experiments demonstrate that Cuckoo-PC outperforms both SA and QA in terms of the network performance by lowering the average distance between sensors and controllers up to 13% and 9%, respectively. Comparing our method against Integer Linear Programming (ILP) reveals that Cuckoo-PC achieves approximately similar results (less than 1% deviation) in a noticeably shorter time.

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Section: Cuckoo Optimization Algorithmmentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%