@article {Aoun2021315,
	title = {A Cooperative Multi-swarm Particle Swarm Optimizer Based Hidden Markov Model},
	journal = {Studies in Computational Intelligence},
	volume = {906},
	year = {2021},
	note = {cited By 1},
	pages = {315-334},
	abstract = {Particle swarm optimization (PSO)~is a population-based stochastic metaheuristic algorithm; it has been successful in dealing with a multitude of optimization problems. Many PSO variants have been created to boost its optimization capabilities, in particular, to cope with more complex problems. In this paper, we provide a new approach of multi-population~particle swarm optimization with a cooperation strategy. The proposed algorithm splits the PSO population into four sub swarms and attributes a main role to each one. A machine learning technique is designed as an individual level to allow each particle to determine its suitable swarm membership at each iteration. In a collective level, cooperative rules are designed between swarms to ensure more diversity and realize the better solution using a Master/Slave cooperation scheme. Several simulations are performed on a set of benchmark functions to examine the performances of this approach compared to a multitude of state of the art of PSO variants. Experiments reveal a good computational efficiency of the presented method with distinguishable performances. {\textcopyright} 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-030-58930-1_21},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097954015\&doi=10.1007\%2f978-3-030-58930-1_21\&partnerID=40\&md5=64af5832e5e4b2f749a5c24339a3911a},
	author = {Aoun, O. and El Afia, A. and Talbi, E.-G.}
}
@article {Aoun2018331,
	title = {Time-dependence in multi-agent MDP applied to gate assignment problem},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {9},
	number = {2},
	year = {2018},
	pages = {331-340},
	doi = {10.14569/IJACSA.2018.090247},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049525520\&doi=10.14569\%2fIJACSA.2018.090247\&partnerID=40\&md5=18412e008a36b0bc8220a03385704b3a},
	author = {Aoun, O. and Afia, A.E.}
}
@conference {ElAfia2017,
	title = {Data-driven based aircraft maintenance routing by markov decision process model},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F129474},
	year = {2017},
	note = {cited By 0},
	abstract = {Aircraft maintenance routing is of basic significance to the safe and efficient operations of an airline. However, the timely efficiency of the airline flight schedule is susceptible to various factors during the daily operations. Air traffic often undergoes some random disruptions that expose maintenance routing to random flight delays, which have to be considered to ensure safe and operational flight schedule. The idea of data-driven methods was the focal point of much studies during a previous couple of years. Constrained Markov Decision process model was selected in this paper to remedy this problem and design the maintenance needs of an aircraft taking past data information into account. Maintenance actions are so modeled with stochastic state transitions. This can offer the opportunity to solve the maintenance routing problem deliberating and handling flight disturbances. Through computational tests on real data of a Moroccan airline company, we investigate the efficiency of this solution approach on history data sets. {\textcopyright} 2017 Association for Computing Machinery.},
	doi = {10.1145/3090354.3090430},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028455379\&doi=10.1145\%2f3090354.3090430\&partnerID=40\&md5=b414d040a3e32989b0121144dbd1f7f3},
	author = {El Afia, A. and Aoun, O.}
}
@article {Afia20179997,
	title = {Hidden markov model control of inertia weight adaptation for Particle swarm optimization},
	journal = {IFAC-PapersOnLine},
	volume = {50},
	number = {1},
	year = {2017},
	note = {cited By 0},
	pages = {9997-10002},
	abstract = {Particle swarm optimization (PSO) is a stochastic algorithm based population that integrates social interactions of animals in nature. One of the main challenges within PSO is to balance between global and local search throughout the course of a run. To achieve this trade-off, various adaptive PSOs have been proposed in order to control the values of its parameters. The present paper makes an attempt to determine a generalized adaptive framework for the setting of the inertia weight parameter which is named HMM-wPSO. That is, a control mechanism of the inertia weight is proposed based on the estimation of states using hidden Markov model (HMM). We performed evaluations on ten benchmark functions to test the HMM control of inertia weight parameter for the PSO. Experimental results show that our proposed scheme outperforms other compared PSO variants in major cases in terms of solution accuracy and convergence speed. {\textcopyright} 2017},
	doi = {10.1016/j.ifacol.2017.08.2030},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031775272\&doi=10.1016\%2fj.ifacol.2017.08.2030\&partnerID=40\&md5=3e5c7fac9adf59cd9559143a77cf11ab},
	author = {Afia, A.E. and Sarhani, M. and Aoun, O.}
}
@article {Aoun2016347,
	title = {Investigation of hidden markov model for the tuning of metaheuristics in airline scheduling problems},
	journal = {IFAC-PapersOnLine},
	volume = {49},
	number = {3},
	year = {2016},
	note = {cited By 0},
	pages = {347-352},
	abstract = {The tuning approach consists in finding the most suitable configuration of an algorithm for solving a given problem. Machine learning methods are usually used to automate this process. They may enable to construct robust autonomous artifacts whose behavior becomes increasingly expert. This paper focuses on the restriction of this general problem to the field of air planning and more specifically the crew scheduling problem. Metaheuristics are widely used to solve this problem. Our approach consists of using hidden markov model to find the best configuration of the algorithm based on the estimation of the most likely state. The experiment consists of finding the best parameter values of the particle swarm optimization algorithm for the crew scheduling problem. Our approach has shown that it can be a promising solution for automatic optimization of airline scheduling problems. {\textcopyright} 2016},
	doi = {10.1016/j.ifacol.2016.07.058},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991108851\&doi=10.1016\%2fj.ifacol.2016.07.058\&partnerID=40\&md5=1a1629bc31766ee8816bba42989cb03d},
	author = {Aoun, O. and Sarhani, M. and El Afia, A.}
}
@conference {Aoun2015196,
	title = {Application of multi-agent Markov decision processes to gate assignment problem},
	booktitle = {Colloquium in Information Science and Technology, CIST},
	volume = {2015-January},
	number = {January},
	year = {2015},
	note = {cited By 0},
	pages = {196-201},
	abstract = {Gate assignment Problem (GAP) is an important subject of airport management to ensure smooth traffic operations. However, flights schedule may undergo some stochastic events such as delays that usually occur and have to be considered in the planning. Our approach considers the representation of gates as collaborative agents trying to complete a set of flights assignment tasks as given by a centralized controller. That will allow giving a new model for the GAP based on Multi Agent Markov Decision Processes (MMDP). The aim of this work is to give to controllers at the airport a robust priory solution instead of taking the risk of online schedule modifications to handle uncertainty. The solution of this problem will be a set of optimal decisions to be taken in every case of traffic disturbance. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/CIST.2014.7016618},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938059541\&doi=10.1109\%2fCIST.2014.7016618\&partnerID=40\&md5=3c8489dfc2f96904a052d1ca21232020},
	author = {Aoun, O. and El Afia, A.}
}
@conference {Aoun2014762,
	title = {A robust crew pairing based on multi-agent Markov decision processes},
	booktitle = {2014 2nd World Conference on Complex Systems, WCCS 2014},
	year = {2014},
	note = {cited By 0},
	pages = {762-768},
	abstract = {Airline scheduling is a real challenge in the context of the airline industry; this includes a lot of planning and operational decision problems and deals with a large number of interdependent resources. A prominent problem in airline scheduling is crew scheduling, specially pairings or Tour-of-Duty planning problem. The objective is to ensure optimal allocation of crews to flights by specifying the set of pairings that minimize the planned cost. The widely used algorithms assume no disruptions. However, airline operations often undergo stochastic disturbances that have to be taken into account in order to minimize the real operating cost. Recently, great interest has been given to robust crew scheduling with consideration of the stochastic nature of disturbances like technical breakdowns or bad weather conditions. In this paper, we develop a stochastic model of crew pairing problem based on Multi-agent Markov Decision Processes (MMDP); thus, the problem will be treated as finding the optimal policy to adopt in stochastic cases of disturbances. Also, a computational study is conducted to ensure validity of our proposed model. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/ICoCS.2014.7060940},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929193249\&doi=10.1109\%2fICoCS.2014.7060940\&partnerID=40\&md5=24e45c65c00ba3e4dc4825bd5909386b},
	author = {Aoun, O. and El Afia, A.}
}
@conference {Aoun201442,
	title = {Using Markov decision processes to solve stochastic gate assignment problem},
	booktitle = {Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014},
	year = {2014},
	note = {cited By 0},
	pages = {42-47},
	abstract = {Airport traffic often undergoes some random disruptions, which have to be considered to ensure operational flight-gate assignments. Inadequate assignment of gates may result in flight delays that happen in airport operations and must be taken into account. Here, we are using the original algorithm, based on Markov decision process (MDP) to solve the gate assignment problem (GAP) under uncertainty; we include stochastic parameters that depend on probabilities to express fluctuations in flight operations. The use of MDP for modeling will provide for airport controllers a robust solution for the GAP that takes in consideration possible flight delays. This paper gives the corresponding model, which includes strict constraints of the GAP and other soft constraints like choice preferences of gates. We give experimental results on a sample of real data to demonstrate the feasibility and efficiency of our approach. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/GOL.2014.6887445},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908638693\&doi=10.1109\%2fGOL.2014.6887445\&partnerID=40\&md5=5ca43e35806c14816feb2afcffb9c9c8},
	author = {Aoun, O. and El Afia, A.}
}
