@article {Bouzbita202248,
	title = {A Fuzzy Meta Model for Adjusting Ant Colony System Parameters},
	journal = {Lecture Notes in Networks and Systems},
	volume = {489 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {48-58},
	abstract = {Metaheuristic algorithms have become an important choice for solving complex optimization problems which are difficult to solve by conventional methods. But, like many other metaheuristic algorithms, ant colony system (ACS) has the problem of parameters setting. In the last few years, different approaches have been proposed to deal whit this problem. Recently the use of fuzzy logic in dynamic parameters adaptation of metaheuristic algorithms is gaining a considerable interest from the researchers. In this paper, a meta model for modifying the parameters of ACS during runtime based on fuzzy logic concept is presented. The main idea is to study the effect of modifying all the parameters of the ACS on the same time on its performance. To compare the efficiency of the proposed approaches, they were applied to a set of traveling salesman problem instances. Also, a comparison with the standard ACS and some literature results are discussed. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-07969-6_4},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135092796\&doi=10.1007\%2f978-3-031-07969-6_4\&partnerID=40\&md5=319150216144782de4bcb5697185e658},
	author = {Bouzbita, S. and Afia, A.E.}
}
@article {Bouzbita2021253,
	title = {A New Hidden Markov Model Approach for Pheromone Level Exponent Adaptation in Ant Colony System},
	journal = {Studies in Computational Intelligence},
	volume = {906},
	year = {2021},
	note = {cited By 1},
	pages = {253-267},
	abstract = {We propose in this paper a Hidden Markov Model~(HMM) approach to avoid premature convergence of ants in the Ant Colony System~(ACS) algorithm. Indeed, the proposed approach was modelled as a classifier method to control the convergence through the dynamic adaptation of the α parameter that weighs the relative influence of the pheromone. The implementation was tested on several Travelling Salesman Problem~(TSP) instances with different number of cities. The proposed approach was compared with the standard ACS and the existing fuzzy logic~in the literature. The experimental results illustrate that the proposed method shows better performance. {\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_17},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097964733\&doi=10.1007\%2f978-3-030-58930-1_17\&partnerID=40\&md5=220c02fd7287bc58dc79db86db73282b},
	author = {Bouzbita, S. and Afia, A.E. and Faizi, R.}
}
@article {Bouzbita20205436,
	title = {The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller},
	journal = {International Journal of Electrical and Computer Engineering},
	volume = {10},
	number = {5},
	year = {2020},
	note = {cited By 4},
	pages = {5436-5444},
	abstract = {In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the responsible parameters for the decay of the pheromone trails ξ and using fuzzy logic controller (FLC) applied in the travelling salesman problems (TSP). The purpose of the proposed method is to understand the effect of both parameters ξ and on the performance of the ACS at the level of solution quality and convergence speed towards the best solutions through studying the behaviour of the ACS algorithm during this adaptation. The adaptive ACS is compared with the standard one. Computational results show that the adaptive ACS with dynamic adaptation of local pheromone parameter ξ is more effective compared to the standard ACS. {\textcopyright} 2020 Institute of Advanced Engineering and Science. All rights reserved.},
	doi = {10.11591/IJECE.V10I5.PP5436-5444},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087312967\&doi=10.11591\%2fIJECE.V10I5.PP5436-5444\&partnerID=40\&md5=fb268b2912214f5d902a8ec0f867a2cd},
	author = {Bouzbita, S. and El Afia, A. and Faizi, R.}
}
@article {Bouzbita2019153,
	title = {Hidden Markov Model classifier for the adaptive ACS-TSP pheromone parameters},
	journal = {Studies in Computational Intelligence},
	volume = {774},
	year = {2019},
	note = {cited By 0},
	pages = {153-169},
	doi = {10.1007/978-3-319-95104-1_10},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053070003\&doi=10.1007\%2f978-3-319-95104-1_10\&partnerID=40\&md5=a3b7e585281ec5fd0d0268e2187f8014},
	author = {Bouzbita, S. and El Afia, A. and Faizi, R.}
}
@conference {Bouzbita2018,
	title = {Parameter adaptation for ant colony system algorithm using hidden markov model for tsp problems},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	doi = {10.1145/3230905.3230962},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053485206\&doi=10.1145\%2f3230905.3230962\&partnerID=40\&md5=69e09fa2debc5dc07106895d9ce7948b},
	author = {Bouzbita, S. and El Afia, A. and Faizi, R.}
}
@conference {Bouzbita2017344,
	title = {Dynamic adaptation of the ACS-TSP local pheromone decay parameter based on the Hidden Markov Model},
	booktitle = {Proceedings of 2016 International Conference on Cloud Computing Technologies and Applications, CloudTech 2016},
	year = {2017},
	note = {cited By 1},
	pages = {344-349},
	abstract = {The objective of the present paper is to propose an improved Ant Colony System (ACS) algorithm based on a Hidden Markov Model (HMM) so as dynamically adapt the local pheromone decay parameter ξ. The proposed algorithm uses Iteration and Diversity as indicators of the hidden states in the search space in ACS. To test the efficiency of our algorithm, we experimented it on several benchmark Travelling Salesman Problem (TSP) instances. The results have proven the effectiveness of our algorithm in both the convergence speed and the solution quality. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/CloudTech.2016.7847719},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013861130\&doi=10.1109\%2fCloudTech.2016.7847719\&partnerID=40\&md5=26cafb2d23ee70bf2f3552d7fec22e8a},
	author = {Bouzbita, S. and El Afia, A. and Faizi, R. and Zbakh, M.}
}
@article {ElAfia20172161,
	title = {The effect of updating the local pheromone on ACS performance using fuzzy logic},
	journal = {International Journal of Electrical and Computer Engineering},
	volume = {7},
	number = {4},
	year = {2017},
	note = {cited By 1},
	pages = {2161-2168},
	abstract = {Fuzzy Logic Controller (FLC) has become one of the most frequently utilised algorithms to adapt the metaheuristics parameters as an artificial intelligence technique. In this paper, the Ξ parameter of Ant Colony System (ACS) algorithm is adapted by the use of FLC, and its behaviour is studied during this adaptation. The proposed approach is compared with the standard ACS algorithm. Computational results are done based on a library of sample instances for the Traveling Salesman Problem (TSPLIB). Copyright {\textcopyright} 2017 Institute of Advanced Engineering and Science. All rights reserved.},
	doi = {10.11591/ijece.v7i4.pp2161-2168},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030845141\&doi=10.11591\%2fijece.v7i4.pp2161-2168\&partnerID=40\&md5=b3dc47e58e78c530f0c597ab93eda8bf},
	author = {El Afia, A. and Bouzbita, S. and Faizi, R.}
}
@conference {Bouzbita2017633,
	title = {A novel based Hidden Markov Model approach for controlling the ACS-TSP evaporation parameter},
	booktitle = {International Conference on Multimedia Computing and Systems -Proceedings},
	year = {2017},
	note = {cited By 0},
	pages = {633-638},
	abstract = {The aim of this paper is to propose a new method capable of dynamically controlling the evaporation parameter in an Ant Colony System (ACS) using a Hidden Markov Model. The purpose is to improve the performance of ACS by controlling the exploration and exploitation in the search space. To this end, two HMM approaches are proposed. The first is a training method that best suits the observed data of the Hidden Markov Model. The second is a method that dynamically controls the adapted parameter by applying several processes. To test our algorithm we used a set of Travelling Salesman Problem (TSP) instances. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/ICMCS.2016.7905544},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019108752\&doi=10.1109\%2fICMCS.2016.7905544\&partnerID=40\&md5=2acc848761420a801ab6567757203ac4},
	author = {Bouzbita, S. and El Afia, A. and Faizi, R.}
}
