Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Graphical abstractDisplay Omitted HighlightsProposed a hybrid artificial bee colony (HABC) algorithm for a nurse rostering problem (NRP).Compared the HABC results with other eleven comparative methods using INRC2010 dataset.Shod that HABC algorithm performs ll.This research shod that a ll-designed hybrid technique is a competitive alternative for solving NRP. The nurse rostering problem (NRP) is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses, each has specific skills and work contract, to a predefined rostering period according to a set constraints. The metaheuristics are the most successful methods for tackling this problem.  proposes a metaheuristic technique called a hybrid artificial bee colony (HABC) for NRP. In HABC, the process of the employed bee operator is replaced with the hill climbing optimizer (HCO) to empor its exploitation capability and the usage of HCO is controlled by hill climbing rate (HCR) parameter. The performance of the proposed HABC is evaluated using the standard dataset published in the first international nurse rostering competition 2010 (INRC2010). This dataset consists of 69 instances which reflect this problem in many real-world cases that are varied in size and complexity. The experimental results of studying the effect of HCO using different value of HCR show that the HCO has a great impact on the performance of HABC. In addition, a comparative evaluation of HABC is carried out against other eleven methods that worked on INRC2010 dataset. The comparative results show that the proposed algorithm achieved two new best results for two problem instances, 35 best published results out of 69 instances as achieved by other comparative methods, and comparable results in the remaining instances of INRC2010 dataset.
['Mohammed A. Awadallah', 'Asaju La’aro Bolaji', 'Mohammed Azmi Al-Betar']
I am writing a research paper on a idea A hybrid artificial bee colony for a nurse rostering problem
[{"from": "human", "value": "I am writing a research paper on a idea A hybrid artificial bee colony for a nurse rostering problem"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Graphical abstractDisplay Omitted HighlightsProposed a hybrid artificial bee colony (HABC) algorithm for a nurse rostering problem (NRP).Compared the HABC results with other eleven comparative methods using INRC2010 dataset.Shod that HABC algorithm performs ll.This research shod that a ll-designed hybrid technique is a competitive alternative for solving NRP. The nurse rostering problem (NRP) is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses, each has specific skills and work contract, to a predefined rostering period according to a set constraints. The metaheuristics are the most successful methods for tackling this problem.  proposes a metaheuristic technique called a hybrid artificial bee colony (HABC) for NRP. In HABC, the process of the employed bee operator is replaced with the hill climbing optimizer (HCO) to empor its exploitation capability and the usage of HCO is controlled by hill climbing rate (HCR) parameter. The performance of the proposed HABC is evaluated using the standard dataset published in the first international nurse rostering competition 2010 (INRC2010). This dataset consists of 69 instances which reflect this problem in many real-world cases that are varied in size and complexity. The experimental results of studying the effect of HCO using different value of HCR show that the HCO has a great impact on the performance of HABC. In addition, a comparative evaluation of HABC is carried out against other eleven methods that worked on INRC2010 dataset. The comparative results show that the proposed algorithm achieved two new best results for two problem instances, 35 best published results out of 69 instances as achieved by other comparative methods, and comparable results in the remaining instances of INRC2010 dataset."}]