Improving Search Efficiency Adopting Hill-Climbing to Ant Colony Optimization for Constraint Satisfaction Problems

Hayakawa, D.; Mizuno, Kazunori; Sasaki, Hisayuki; Nishihara, Seiichi

doi:10.1109/kse.2011.39

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…The ACO technique was deemed appropriate in this study, to be utilized in solving a highly constrained NRP as recommended by Gujarth and Rauner (2007), which was further improved by an effective local search technique, i.e., the Hill Climbing as suggested by several reviewed studies (Lim et al, 2006;Hayakawa et al, 2011;Awadallah et al, 2017;Oonsrikaw & Thammano, 2018). Our proposed model, in particular, was inspired by the proven ACO as an effective technique for highly constrained problems (Gujarth & Rauner, 2007).…”

Section: Methodsmentioning

confidence: 99%

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A Hybrid Ant Colony Optimization Algorithm for Solving a Highly Constrained Nurse Rostering Problem

Ramli¹,

Rahman²,

Rohim³

2019

JICT

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Distribution of work shifts and off days for nurses in a duty roster is a crucial task. Much effort is spent trying to produce workable and quality rosters for nurses in hospital wards. However, there are cases, such as mandatory working days per week and balanced distribution of shift types that cannot be achieved in manually generated rosters, which are still being practised today. Hence, this study focused on solving these issues arising in Nurse Rostering Problems (NRPs) strategizing on a hybrid of Ant Colony Optimization (ACO) algorithm with a hill climbing technique. The hybridization with hill climbing was aimed at fine-tuning the initial solution or roster generated by the ACO algorithm to achieve better rosters. The hybrid model was developed with the goal of satisfying hard constraints, while minimizing violations of soft constraints in such a way that fulfilled hospital rules and nurses' preferences. The real data used for the highly constrained NRP was obtained from a large Malaysian hospital. There were specifically, three main phases involved in developing the hybrid model: generating an initial roster; updating the roster through 306 the ACO algorithm, and implementing the hill climbing to further search for a refined solution. The results showed that with a larger value of pheromone, the chances of obtaining a good solution was found with only small penalty values. This study has proven that the hybrid ACO is able to solve NRPs with good potential solutions that satisfied all four important criteria: coverage, quality, flexibility, and cost. Subsequently, the hybrid model is also beneficial to the hospital's management as nurses can be scheduled with a balanced distribution of shifts, which in turn fulfilled their preferences.

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Section: Methodsmentioning

confidence: 99%

“…This approach had shown relatively good performance. Meanwhile, Hayakawa et al (2011) employed an ACO-Hill Climbing hybrid for a Constraint Satisfaction Problem (CSP). In their studies, the search inefficiency in the ACO was improved by the Hill Climbing procedure.…”

Section: Related Reviews On Nurse Rosteringmentioning

confidence: 99%

A Hybrid Ant Colony Optimization Algorithm for Solving a Highly Constrained Nurse Rostering Problem

Ramli¹,

Rahman²,

Rohim³

2019

JICT

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“…MNC has demonstrated to be able to solve the queens problem in minutes [17]. MNC is widely used to construct hybrid algorithms with other optimizations [14][15] [18] [19]. In this way, the basic idea of our proposed approach is to use MNC to improve the solution reached by CHN.…”

Section: Mnc Local Searchmentioning

confidence: 99%

A comparative study of CHN-MNC, GA and PSO for solving constraints satisfaction problems

Bouhouch¹,

Loqman²,

Bennis³

et al. 2019

Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 20

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Our approach CHN-MNC, based Continuous Hopfield neural network and Min-Conflict heuristic), have proved that is more efficient than using CHN alone to solve Constraints Satisfaction Problem (CSP). In This paper we study the performance of CHN-MNC by comparing it robustness with two evolutionary algorithms. We choose a Genetic Algorithm and Swarm optimisation to performers this study. Some numerical experiments are done over a variety of problems to verify the efficiency and fast convergence of our approach. abstract needs to summarize the content of the paper.

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“…MNC has demonstrated to be able to solve the queens problem in minutes [24]. MNC is widely used to construct hybrid algorithms with other optimizations [25][26][27][28]. In this way, the basic idea of our proposed approach is to use MNC to improve the solution reached by CHN.…”

Section: Chn and Min Conflict Heuristic To Solve Cspsmentioning

confidence: 99%

Neural Network and Local Search to Solve Binary CSP

Bouhouch

Bennis

Loqman

et al. 2018

IJEECS

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<p>Continuous Hopfield neural Network (CHN) is one of the effective approaches to solve Constrain Satisfaction Problems (CSPs). However, the main problem with CHN is that it can reach stabilisation with outputs in real values, which means an inconsistent solution or an incomplete assignment of CSP variables. In this paper, we propose a new hybrid approach combining CHN and min-conflict heuristic to mitigate these problems. The obtained results show an improvement in terms of solution quality, either our approach achieves feasible soluion with a high rate of convergence, furthermore, this approach can also enhance theperformance more than conventional CHN in some cases, particularly, when the network crashes.</p>

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Improving Search Efficiency Adopting Hill-Climbing to Ant Colony Optimization for Constraint Satisfaction Problems

Cited by 6 publications

References 9 publications

A Hybrid Ant Colony Optimization Algorithm for Solving a Highly Constrained Nurse Rostering Problem

A Hybrid Ant Colony Optimization Algorithm for Solving a Highly Constrained Nurse Rostering Problem

A comparative study of CHN-MNC, GA and PSO for solving constraints satisfaction problems

Neural Network and Local Search to Solve Binary CSP

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