@article {ElAfia2019337, title = {Runtime prediction of optimizers using improved support vector machine}, journal = {Lecture Notes in Networks and Systems}, volume = {49}, year = {2019}, note = {cited By 0}, pages = {337-350}, doi = {10.1007/978-3-319-97719-5_21}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063278208\&doi=10.1007\%2f978-3-319-97719-5_21\&partnerID=40\&md5=3326fe51db036a4ca8af1da02286aba7}, author = {El Afia, A. and Sarhani, M.} } @article {Sarhani2018447, title = {Facing the feature selection problem with a binary PSO-GSA approach}, journal = {Operations Research/ Computer Science Interfaces Series}, volume = {62}, year = {2018}, pages = {447-462}, doi = {10.1007/978-3-319-58253-5_26}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032615163\&doi=10.1007\%2f978-3-319-58253-5_26\&partnerID=40\&md5=45163b8637f55b8af04f902f4f33afc2}, author = {Sarhani, M. and El Afia, A. and Faizi, R.} } @article {Sarhani2018447, title = {Facing the feature selection problem with a binary PSO-GSA approach}, journal = {Operations Research/ Computer Science Interfaces Series}, volume = {62}, year = {2018}, note = {cited By 0}, pages = {447-462}, abstract = {Feature selection has become the focus of much research in many areas where we can face the problem of big data or complex relationship among features. Metaheuristics have gained much attention in solving many practical problems, including feature selection. Our contribution in this paper is to propose a binary hybrid metaheuristic to minimize a fitness function representing a trade-off between the classification error of selecting the feature subset and the corresponding number of features. This algorithm combines particle swarm optimization (PSO) and gravitational search algorithm (GSA). Also, a mutation operator is integrated to enhance population diversity. Experimental results on ten benchmark dataset show that our proposed hybrid method for feature selection can achieve high performance when comparing with other metaheuristic algorithms and well-known feature selection approaches. {\textcopyright} Springer International Publishing AG 2018.}, doi = {10.1007/978-3-319-58253-5_26}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032615163\&doi=10.1007\%2f978-3-319-58253-5_26\&partnerID=40\&md5=45163b8637f55b8af04f902f4f33afc2}, author = {Sarhani, M. and El Afia, A. and Faizi, R.} } @conference {Sarhani2018, title = {Generalization enhancement of support vector regression in electric load forecasting with model selection}, booktitle = {ACM International Conference Proceeding Series}, year = {2018}, doi = {10.1145/3230905.3230947}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053484922\&doi=10.1145\%2f3230905.3230947\&partnerID=40\&md5=a29a4cececf76f665c91e09b16ba8692}, author = {Sarhani, M. and Afia, A.E.} } @conference {ElAfia20181, title = {Performance prediction using support vector machine for the configuration of optimization algorithms}, booktitle = {Proceedings of 2017 International Conference of Cloud Computing Technologies and Applications, CloudTech 2017}, volume = {2018-January}, year = {2018}, pages = {1-7}, doi = {10.1109/CloudTech.2017.8284699}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046624408\&doi=10.1109\%2fCloudTech.2017.8284699\&partnerID=40\&md5=13c42fe4df8b128f5a87988605c2485f}, author = {El Afia, A. and Sarhani, M.} } @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.} } @conference {ElAfia2017, title = {Particle swarm optimization for model selection of aircraft maintenance predictive models}, booktitle = {ACM International Conference Proceeding Series}, volume = {Part F129474}, year = {2017}, note = {cited By 0}, abstract = {Nowadays, predictive models -especially the ones based on machine learning- are widely used to solve many big data problems. One of the main challenges within predictive models is to choose the best model for each problem. In particular, model selection and feature selection are two important issues in machine learning models as they help to achieve the best results. This paper focuses on the restriction of these two problems to σ-SVR (support vector regression) and more specifically the optimization of both problems using the particle swarm optimization algorithm. Our approach is investigated in the estimation of remaining useful life (RUL) of aircrafts which a ects their maintenance planning and which is an interesting issue in predictive maintenances. That is, the experiment consists of predicting RUL of aircraft engines using an σ-SVR optimized by PSO. Experimental results show the efficiency of the proposed approach. {\textcopyright} 2017 Association for Computing Machinery.}, doi = {10.1145/3090354.3090402}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028474954\&doi=10.1145\%2f3090354.3090402\&partnerID=40\&md5=2d4328ae0f65ce92a35e5516b96bd0b7}, author = {El Afia, A. and Sarhani, M.} } @conference {Sarhani2016288, title = {Feature selection and parameter optimization of support vector regression for electric load forecasting}, booktitle = {Proceedings of 2016 International Conference on Electrical and Information Technologies, ICEIT 2016}, year = {2016}, note = {cited By 0}, pages = {288-293}, abstract = {Forecasting of future electricity demand has become a promising issue for the electric power industry. Since many factors affect electric load data, machine learning methods are useful for electric load forecasting (ELF). On the one hand, it is important to determine the irrelevant factors as a preprocessing step for ELF. On the other hand, the performance of machine learning models depends heavily on the choice of its parameters. These problems are known respectively as feature selection and model selection problems. In this paper, we use the support vector regression (SVR) model for ELF. Our contribution consists of investigating the use the particle swarm optimization for both feature selection and model selection problems. Experimental results on two widely used electric load dataset show that our proposed hybrid method for feature selection and parameter optimization of SVR can achieve better results when compared with the classical SVR model while using feature selection and without using it. {\textcopyright} 2016 IEEE.}, doi = {10.1109/EITech.2016.7519608}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992187838\&doi=10.1109\%2fEITech.2016.7519608\&partnerID=40\&md5=c467eb46055ce1640930ac5b1ece403b}, author = {Sarhani, M. and El Afia, A.} } @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 {Sarhani2016, title = {Particle swarm optimization with a mutation operator for solving the preventive aircraft maintenance routing problem}, booktitle = {Proceedings of the 3rd IEEE International Conference on Logistics Operations Management, GOL 2016}, year = {2016}, note = {cited By 0}, abstract = {Aircraft Maintenance Routing (AMR) is one of the major optimization problems in the airline industry. In this study, we present a mathematical formulation for the daily AMR problem which aims to minimize the risk of both scheduled and non-scheduled maintenance costs. Exact methods may fail to deal with such problems. Our contribution is then to examine the use of an improved particle swarm optimization (PSO) algorithm by a uniform mutation operator for solving this probabilistic problem. Computational results show that our hybrid approach gives competitive results comparing to the native binary PSO. {\textcopyright} 2016 IEEE.}, doi = {10.1109/GOL.2016.7731683}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85001976721\&doi=10.1109\%2fGOL.2016.7731683\&partnerID=40\&md5=9bef08dc37eff9c6ed7b1c2c6e2e41b6}, author = {Sarhani, M. and Ezzinbi, O. and Afia, A.E. and Benadada, Y.} } @conference {Sarhani20151, title = {Electric load forecasting using hybrid machine learning approach incorporating feature selection}, booktitle = {CEUR Workshop Proceedings}, volume = {1580}, year = {2015}, note = {cited By 0}, pages = {1-7}, abstract = {Forecasting of future electricity demand is very important for the electric power industry. As influenced by various factors, it has been shown in several publications that machine learning methods are useful for electric load forecasting (ELF). On the one hand, we introduce in this paper the approach of support vector regression (SVR) for ELF. In particular, we use particle swarm optimization (PSO) algorithm to optimize SVR parameters. On the other hand, it is important to determine the irrelevant factors as a preprocessing step for ELF. Our contribution consists of investigating the importance of applying the feature selection approach for removing the irrelevant factors of electric load. The experimental results elucidate the feasibility of applying feature selection without decreasing the performance of the SVR-PSO model for ELF.}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977574358\&partnerID=40\&md5=405be8fb16c29c43f82f16549d26252b}, author = {Sarhani, M. and El Afia, A.} } @conference {Sarhani201479, title = {An extension of X13-ARIMA-SEATS to forecast islamic holidays effect on logistic activities}, booktitle = {Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014}, year = {2014}, note = {cited By 0}, pages = {79-84}, abstract = {To better manage and optimize logistic activities, factors that affect it must be determined: The calendar effect is one of these factors which must be analyzed. Analyzing such kind of data by using classical time series forecasting methods, such as exponential smoothing method and ARIMA model, will fail to capture such variation. This paper is released to present a review of the models which are used to forecast the calendar effect, especially moving holidays effect. We adopt the recent approach of X13-ARIMA-SEATS and extend it for being able to forecast the effect of Islamic holidays. Our extension is applied to Moroccan case studies, and aims to give recommendations concerning this effect on logistic activities. {\textcopyright} 2014 IEEE.}, doi = {10.1109/GOL.2014.6887423}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908655728\&doi=10.1109\%2fGOL.2014.6887423\&partnerID=40\&md5=de88391f3f7ec0517e753c7aeb4aeae4}, author = {Sarhani, M. and El Afia, A.} } @conference {Sarhani201479, title = {Intelligent system based support vector regression for supply chain demand forecasting}, booktitle = {2014 2nd World Conference on Complex Systems, WCCS 2014}, year = {2014}, note = {cited By 0}, pages = {79-83}, abstract = {Supply chain management (SCM) is an emerging field that has commanded attention from different communities. On the one hand, the optimization of supply chain which is an important issue, requires a reliable prediction of future demand. On the other hand, It has been shown that intelligent systems and machine learning techniques are useful for forecasting in several applied domains. In this paper, we introduce the machine learning technique of time series forecasting Support Vector Regression (SVR) which is nowadays frequently used. Furthermore, we use the Particle Swarm Optimization (PSO) algorithm to optimize the SVR parameters. We investigate the accuracy of this approach for supply chain demand forecasting by applying it to a case study. {\textcopyright} 2014 IEEE.}, doi = {10.1109/ICoCS.2014.7060941}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929162457\&doi=10.1109\%2fICoCS.2014.7060941\&partnerID=40\&md5=8f615fc2cd27fff20b0220a543294e57}, author = {Sarhani, M. and El Afia, A.} } @conference {Ezzinbi201448, title = {A metaheuristic approach for solving the airline maintenance routing with aircraft on ground problem}, booktitle = {Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014}, year = {2014}, note = {cited By 1}, pages = {48-52}, abstract = {In the airline industry, the Aircraft Maintenance Routing (AMR) problem has been one of the great successes of operations research. The AMR problem is to determine a particular route for each aircraft to undergo different levels of maintenance checks. The objective is to minimize the total maintenance costs. In this study, our aim is to present a mathematical formulation for the AMR problem which takes into account the case of Aircraft On Ground (AOG). We develop solution approaches based on Particle Swarm Optimization algorithm and Genetic algorithm for solving the problem. The results show the effectiveness of this solution in reducing computational time. {\textcopyright} 2014 IEEE.}, doi = {10.1109/GOL.2014.6887446}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908628134\&doi=10.1109\%2fGOL.2014.6887446\&partnerID=40\&md5=0d3ce9e2a4abfc7cd5949d8a813591cc}, author = {Ezzinbi, O. and Sarhani, M. and El Afia, A. and Benadada, Y.} } @conference {Ezzinbi201452, title = {Particle swarm optimization algorithm for solving airline crew scheduling problem}, booktitle = {Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014}, year = {2014}, note = {cited By 1}, pages = {52-56}, abstract = {In air transport, the cost related to crew members presents one of the most important cost supported by airline companies. The objective of the crew scheduling problem is to determine a minimum-cost set of pairings so that every flight leg is assigned a qualified crew and every pairing satisfies the set of applicable work rules. In this paper, we propose a solution for the crew scheduling problem with Particle Swarm Optimization (PSO) algorithm, this solution approach is compared with the Genetic Algorithm (GA) for both crew pairing and crew assignment problems which are the two part of crew scheduling problem. {\textcopyright} 2014 IEEE.}, doi = {10.1109/GOL.2014.6887447}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908628135\&doi=10.1109\%2fGOL.2014.6887447\&partnerID=40\&md5=61f25771c33cea8f4845104ba352a303}, author = {Ezzinbi, O. and Sarhani, M. and El Afia, A. and Benadada, Y.} }