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26 results on '"Zitouni, Farouq"'

1. The Walking Palm Tree algorithm: A new Metaheuristic Algorithm for Solving Optimization Problems

Author

Zitouni, Farouq, Harous, Saad, Mirjalili, Seyedali, Bouaicha, Abdelhai Mohamed, Houari, Hocine Abdellatif, Mohamed, Ali Wagdy, Limane, Abdelhadi, Lakbichi, Rihab, Ferhat, Aridj, Luo, Xun, Editor-in-Chief, Almohammedi, Akram A., Series Editor, Chen, Chi-Hua, Series Editor, Guan, Steven, Series Editor, Pamucar, Dragan, Series Editor, Kerrache, Chaker Abdelaziz, editor, Tahari, Abdou El Karim, editor, Kassimi, Dounya, editor, and Chakraborty, Chinmay, editor

Published
2024
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3. List of contributors

Author

Abdalla, Mohmad Hussein, primary, Abdelhamid, Abdelaziz A., additional, Abualigah, Laith, additional, Ahmadi, Mahmood, additional, Ahmed, Aram M., additional, Ajibare, Adedotun T., additional, Akhter, Malik Naveed, additional, Akinyemi, Lateef A., additional, Ala, Ali, additional, Al-Betar, Mohammed Azmi, additional, Ali, Nabeel Salih, additional, Alyasseri, Zaid Abdi Alkareem, additional, Amini, Erfan, additional, Arasteh, Bahman, additional, Ashwini, K., additional, Avcu, Mehmet Enes, additional, Awadallah, Mohammed A., additional, Azizi, Mahdi, additional, Azzi, Ziad, additional, Balasubramanian, E., additional, Basiri, Mahla, additional, Benayad, Abdelbaki, additional, Bhattacharya, Diptendu, additional, Boustil, Amel, additional, Bozorgi, Seyed Mostafa, additional, Braik, Malik, additional, Chakraborty, Sanjoy, additional, Chhabra, Amit, additional, Dadrasajirlou, Yashar, additional, Dalbah, Lamees Mohammad, additional, da Silva, Leandro S.P., additional, Eid, Marwa M., additional, Ekinci, Serdar, additional, Ekwe, Stephen O., additional, Eldeeb, Hossien B., additional, El-kenawy, El-Sayed M., additional, Eslami, Nava, additional, Faraji, Amir, additional, Faridmarandi, Sepehr, additional, Ghahfarokhi, Mansoureh Shahabi, additional, Gharehchopogh, Farhad Soleimanian, additional, Ghasemi, Mojtaba, additional, Goel, Tripti, additional, Gökçe, Harun, additional, Hadi, Shahzaib Farooq, additional, Hamarashid, Hozan K., additional, Harous, Saad, additional, Hassan, Bryar A., additional, Ibrahim, Abdelhameed, additional, Işık, Gültekin, additional, Izadi, Saadat, additional, Izci, Davut, additional, Jafari-Asl, Jafar, additional, Jamil, Norziana, additional, Jatoth, Ravi Kumar, additional, Kadkhoda Mohammadi, Soleiman, additional, Karami, Hojat, additional, Kazar, Okba, additional, Khan, Muhammad Najeeb, additional, Khayyambashi, Mohammad Reza, additional, Khelili, Mohamed Akram, additional, Khodadadi, Nima, additional, Korbaa, Ouajdi, additional, Latreche, Imene, additional, Majidpour, Jaffer, additional, Majumdar, Parijata, additional, Makhadmeh, Sharif Naser, additional, Manita, Ghaith, additional, Mansoor, Majad, additional, Meraihi, Yassine, additional, MiarNaeimi, Farid, additional, Mirjalili, Seyedali, additional, Mirjalili, Seyedeh Zahra, additional, Mirjalili, Seyed Mohammad, additional, Mitra, Sanjoy, additional, Mohammadzadeh, Ali, additional, Mohammed, Naufel B., additional, Mokeddem, Diab, additional, Montazerolghaem, Ahmadreza, additional, Moosavi, Syed Kumayl Raza, additional, Murugan, R., additional, Mustaffa, Z., additional, Nadim-Shahraki, Mohammad H., additional, Nasiri, Mahdieh, additional, Naskar, Anurup, additional, Nasri, Dallel, additional, Nenavath, Hathiram, additional, Neshat, Mehdi, additional, Noori, Kaniaw A., additional, Nssibi, Maha, additional, Ohadi, Sima, additional, Oladejo, Sunday O., additional, Pandiri, D.N. Kiran, additional, Pramanik, Payel, additional, Pramanik, Rishav, additional, Qader, Shko M., additional, Rahbar, Mahdi, additional, Rahman, Chnoor Maheadeen, additional, Rajalaxmi, R.R., additional, Rajamani, D., additional, Ramadhan, Awf Abdulrahmam, additional, Ramdane-Cherif, Amar, additional, Rashid, Tarik A., additional, Reang, Salpa, additional, Refas, Souad, additional, Roopa, C., additional, Saha, Apu Kumar, additional, Saha, Ashim, additional, Şahin, İsmail, additional, Sahoo, Saroj Kumar, additional, Salehnia, Taybeh, additional, Sarkar, Ram, additional, Sergiienko, Nataliia Y., additional, Sheta, Alaa, additional, Shishehgarkhaneh, Milad Baghalzadeh, additional, Sidqi, Haval, additional, Sinha, Amit Kumar, additional, Siva Kumar, M., additional, Slatnia, Sihem, additional, Sruthi, K., additional, Sulaiman, M.H., additional, Taleb, Sylia Mekhmoukh, additional, Tayfor, Noor, additional, Thangarajan, R., additional, Ürgün, Satılmış, additional, Yahia, Selma, additional, Yazdani, Samaneh, additional, Yiğit, Halil, additional, Younis, Hassaan Bin, additional, Zafar, Muhammad Hamza, additional, Zare, Mohsen, additional, and Zitouni, Farouq, additional

Published
2024
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4. The Archerfish Hunting Optimizer: a novel metaheuristic algorithm for global optimization

Author

Zitouni, Farouq, Harous, Saad, Belkeram, Abdelghani, and Hammou, Lokman Elhakim Baba

Subjects
Computer Science - Artificial Intelligence, 90C59, G.1.6, I.2.8, I.1.2
Abstract

Global optimization solves real-world problems numerically or analytically by minimizing their objective functions. Most of the analytical algorithms are greedy and computationally intractable. Metaheuristics are nature-inspired optimization algorithms. They numerically find a near-optimal solution for optimization problems in a reasonable amount of time. We propose a novel metaheuristic algorithm for global optimization. It is based on the shooting and jumping behaviors of the archerfish for hunting aerial insects. We name it the Archerfish Hunting Optimizer (AHO). We Perform two sorts of comparisons to validate the proposed algorithm's performance. First, AHO is compared to the 12 recent metaheuristic algorithms (the accepted algorithms for the 2020's competition on single objective bound-constrained numerical optimization) on ten test functions of the benchmark CEC 2020 for unconstrained optimization. Second, the performance of AHO and 3 recent metaheuristic algorithms, is evaluated using five engineering design problems taken from the benchmark CEC 2020 for non-convex constrained optimization. The experimental results are evaluated using the Wilcoxon signed-rank and the Friedman tests. The statistical indicators illustrate that the Archerfish Hunting Optimizer has an excellent ability to accomplish higher performance in competition with the well-established optimizers., Comment: 41 pages, 14 figures, 41 pages, 132 references, 30 tables

Published
2021

6. BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems.

Author

Zitouni, Farouq, Harous, Saad, Almazyad, Abdulaziz S., Mohamed, Ali Wagdy, Xiong, Guojiang, Khechiba, Fatima Zohra, and Kherchouche, KhadidjaÂ

Subjects
METAHEURISTIC algorithms, CONSTRAINED optimization, GLOBAL optimization, MATHEMATICAL optimization, ENGINEERING design
Abstract

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems. This approach aims to leverage the strengths of multiple algorithms, enhancing solution quality, convergence speed, and robustness, thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks. In this paper, we introduce a hybrid algorithm that amalgamates three distinct metaheuristics: the Beluga Whale Optimization (BWO), the Honey Badger Algorithm (HBA), and the Jellyfish Search (JS) optimizer. The proposed hybrid algorithm will be referred to as BHJO. Through this fusion, the BHJO algorithm aims to leverage the strengths of each optimizer. Before this hybridization, we thoroughly examined the exploration and exploitation capabilities of the BWO, HBA, and JS metaheuristics, as well as their ability to strike a balance between exploration and exploitation. This meticulous analysis allowed us to identify the pros and cons of each algorithm, enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance. In addition, the BHJO algorithm incorporates Opposition-Based Learning (OBL) to harness the advantages offered by this technique, leveraging its diverse exploration, accelerated convergence, and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm. Moreover, the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems, providing a comprehensive assessment of its efficacy and applicability in diverse problem domains. Similarly, the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms, where mean and standard deviation values were utilized as evaluation metrics. This rigorous comparison aimed to assess the performance of the BHJO algorithm about its counterparts, shedding light on its effectiveness and reliability in solving optimization problems. Finally, the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn's test. The resulting numerical values revealed the BHJO algorithm's competitiveness in tackling intricate optimization problems, affirming its capability to deliver favorable outcomes in challenging scenarios. [ABSTRACT FROM AUTHOR]

Published
2024
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12. FA-SETPOWER-MRTA: A Solution for Solving the Multi-Robot Task Allocation Problem

Author

Zitouni, Farouq, Maamri, Ramdane, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Series Editor, Goedicke, Michael, Series Editor, Tatnall, Arthur, Series Editor, Neuhold, Erich J., Series Editor, Pras, Aiko, Series Editor, Tröltzsch, Fredi, Series Editor, Pries-Heje, Jan, Series Editor, Whitehouse, Diane, Series Editor, Reis, Ricardo, Series Editor, Furnell, Steven, Series Editor, Furbach, Ulrich, Series Editor, Winckler, Marco, Series Editor, Rauterberg, Matthias, Series Editor, Amine, Abdelmalek, editor, Mouhoub, Malek, editor, Ait Mohamed, Otmane, editor, and Djebbar, Bachir, editor

Published
2018
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13. Cooperative Learning-Agents for Task Allocation Problem

Author

Zitouni, Farouq, Maamri, Ramdane, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Auer, Michael E., editor, and Tsiatsos, Thrasyvoulos, editor

Published
2018
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24. Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

Author

Faculty of Information and Communication Technology (ICT), #PLACEHOLDER_PARENT_METADATA_VALUE#, Zitouni, Farouq, Saad Harous, Maamri, Ramdane, Faculty of Information and Communication Technology (ICT), #PLACEHOLDER_PARENT_METADATA_VALUE#, Zitouni, Farouq, Saad Harous, and Maamri, Ramdane

Abstract

This paper tackles the Multi-Robot Task Allocation problem. It consists of two distinct sets: a set of tasks (requiring resources), and a set of robots (offering resources). Then, the tasks are allocated to robots while optimizing a certain objective function subject to some constraints; e.g., allocating the maximum number of tasks, minimizing the distances traveled by the robots, etc. Previous works mainly optimized the temporal and spatial constraints, but no work focused on energetic constraints. Our main contribution is the introduction of energetic constraints on multi-robot task allocation problems. In addition, we propose an allocation method based on parallel distributed guided genetic algorithms and compare it to two state-of-the-art algorithms. The performed simulations and obtained results show the effectiveness and scalability of our solution, even in the case of a large number of robots and tasks. We believe that our contribution is applicable in many contemporary areas of research such as smart cities and related topics.

Published
2019

25. TOWARDS A DISTRIBUTED SOLUTION TO MULTI-ROBOT TASK ALLOCATION PROBLEM WITH ENERGETIC AND SPATIOTEMPORAL CONSTRAINTS.

Author

ZITOUNI, FAROUQ, HAROUS, SAAD, and MAAMRI, RAMDANE

Subjects
SMART cities, GENETIC algorithms, TASKS
Abstract

This paper tackles the Multi-Robot Task Allocation problem. It consists of two distinct sets: a set of tasks (requiring resources), and a set of robots (offering resources). Then, the tasks are allocated to robots while optimizing a certain objective function subject to some constraints; e.g., allocating the maximum number of tasks, minimizing the distances traveled by the robots, etc. Previous works mainly optimized the temporal and spatial constraints, but no work focused on energetic constraints. Our main contribution is the introduction of energetic constraints on multi-robot task allocation problems. In addition, we propose an allocation method based on parallel distributed guided genetic algorithms and compare it to two state-of-the-art algorithms. The performed simulations and obtained results show the effectiveness and scalability of our solution, even in the case of a large number of robots and tasks. We believe that our contribution is applicable in many contemporary areas of research such as smart cities and related topics. [ABSTRACT FROM AUTHOR]

Published
2020
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