Discrete Cuckoo Search for Resource Constrained Project Scheduling Problem

Bibiks, Kirils; Hu, Yim Fun; Li, Jianping; Smith, Aleister

doi:10.1109/cse.2015.39

Cited by 13 publications

(16 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve the issue of optimal production planning Unit Commitment (UC), a CS algorithm is applied by (Gharegozi and Jahani, 2013). Discrete Cuckoo Search (DCS) is functioned to solve resource constrained project scheduling problem (Bibiks et al, 2015) and travelling salesman problem (Ouaarab et al, 2014). CS based method is evolved for scheduling optimization of a flexible manufacturing system.…”

Section: 20 Backgroundmentioning

confidence: 99%

Optimal Resource Scheduling for IaaS Cloud Computing using Cuckoo Search Algorithm

Madni

Latiff

Abdulhamid

2017

View full text Add to dashboard Cite

Effective resource scheduling is essential for the overall performance of cloud computing system. Resource scheduling problem in IaaS cloud computing is investigated in this paper. It is established to be an NP-hard problem. A recently developed Cuckoo Search (CS) meta-heuristic algorithm is proposed in this paper, to minimize the response time, makespan and throughput for the resource scheduling in IaaS cloud computing. Simulation results show that CS algorithm outperforms that of Ant Colony Optimization (ACO) algorithm based on the considered parameters.

show abstract

Section: 20 Backgroundmentioning

confidence: 99%

Optimal Resource Scheduling for IaaS Cloud Computing using Cuckoo Search Algorithm

Madni

Latiff

Abdulhamid

2017

View full text Add to dashboard Cite

show abstract

“…Furthermore, many hybrid forms have been implemented, such as a scatter search algorithm with a path re-linking methodology in [26], a hybrid Ant Colony Optimization and Scatter Search (ACOSS) in [27], a Tabu Search (TS) with scatter search implemented by [28], as well as a neuro-genetic approach, which is a hybrid of GA and a neural network, proposed by [29], and more recently, ANGEL, a hybrid that combines ANt colony, GEnetic algorithm and Local search can be found in [30]. Finally, the newest meta-heuristic implementations can be found, including Differential Evolution (DE) [31], Artificial Immune System (AIS) [32] and, most recently, Estimation of Distribution Algorithm (EDA) [33], the shuffled frog-leaping algorithm (SFLA) [34], a hybrid EDA with a new local search based on a random walk and the delete-then-insert operator proposed by [35] along with the use of a discrete version of the ABC algorithm in [36] and an implementation of a discrete cuckoo search in [37].…”

Section: Objectives Solved and Solution Proceduresmentioning

confidence: 99%

A Practical and Robust Execution Time-Frame Procedure for the Multi-Mode Resource-Constrained Project Scheduling Problem with Minimal and Maximal Time Lags

2016

View full text Add to dashboard Cite

Abstract:Modeling and optimizing organizational processes, such as the one represented by the Resource-Constrained Project Scheduling Problem (RCPSP), improve outcomes. Based on assumptions and simplification, this model tackles the allocation of resources so that organizations can continue to generate profits and reinvest in future growth. Nonetheless, despite all of the research dedicated to solving the RCPSP and its multi-mode variations, there is no standardized procedure that can guide project management practitioners in their scheduling tasks. This is mainly because many of the proposed approaches are either based on unrealistic/oversimplified scenarios or they propose solution procedures not easily applicable or even feasible in real-life situations. In this study, we solve a more true-to-life and complex model, Multimode RCPSP with minimal and maximal time lags (MRCPSP/max). The complexity of the model solved is presented, and the practicality of the proposed approach is justified depending on only information that is available for every project regardless of its industrial context. The results confirm that it is possible to determine a robust makespan and to calculate an execution time-frame with gaps lower than 11% between their lower and upper bounds. In addition, in many instances, the solved lower bound obtained was equal to the best-known optimum.

show abstract

“…The associate editor coordinating the review of this manuscript and approving it for publication was Emanuele Lattanzi. traveling salesman problem [12], the scheduling problem [13], the graph coloring problem [14], the node localization problem [15]. However, PFA has not any improved versions to consider dealing with a class of the memory saving variables in the optimization problems.…”

Section: Introductionmentioning

confidence: 99%

An Improved Flower Pollination Algorithm for Optimizing Layouts of Nodes in Wireless Sensor Network

Nguyen¹,

Pan²,

Dao³

2019

IEEE Access

130

View full text Add to dashboard Cite

The arrangement of nodes impacts the quality of connectivity and energy consumption in wireless sensor network (WSN) for prolonging the lifetime. This paper presents an improved flower pollination algorithm based on a hybrid of the parallel and compact techniques for global optimizations and a layout of nodes in WSN. The parallel enhances diversity pollinations for exploring in space search and sharing computation load. The compact can save storing variables for computation in the optimization process. In the experimental section, the selected test functions and the network topology issue WSN are used to test the performance of the proposed approach. Compared results with the other methods in the literature show that the proposed algorithm achieves the practical way of reducing the number of its stored memory variables and running times. INDEX TERMS Improved flower pollination algorithm, layout optimization problems, probabilistic model, wireless sensor network.

show abstract

Discrete Cuckoo Search for Resource Constrained Project Scheduling Problem

Cited by 13 publications

References 34 publications

Optimal Resource Scheduling for IaaS Cloud Computing using Cuckoo Search Algorithm

Optimal Resource Scheduling for IaaS Cloud Computing using Cuckoo Search Algorithm

A Practical and Robust Execution Time-Frame Procedure for the Multi-Mode Resource-Constrained Project Scheduling Problem with Minimal and Maximal Time Lags

An Improved Flower Pollination Algorithm for Optimizing Layouts of Nodes in Wireless Sensor Network

Contact Info

Product

Resources

About