@article {6677,
	title = {A Secret Sharing Scheme based on Integer Decomposition and Hexagonal Structure},
	journal = {International Journal of Information and Communication Technology},
	year = {Submitted},
	author = {Zender, Rouia and Lemnouar Noui and Abdessemed, Mohamed-Rida}
}
@proceedings {6707,
	title = {Discrete Large-scale Multi-Objective Teaching-Learning-Based Optimization Algorithm},
	journal = {4th International Conference on Networking, Information Systems \& Security},
	year = {2021},
	abstract = {
	This paper presents a teaching-learning-based optimization algorithm for discrete large-scale multi-objective problems (DLM-TLBO). Unlike the previous variants, the learning strategy used by each individual and the acquired knowledge are defined based on its level. The proposed approach is used to solve a bi-objective object clustering task (B-OCT) in a swarm robotic system, as a case study. The simple robots have as mission the gathering of a number of objects distributed randomly, while respecting two objectives: maximizing the clustering quality, and minimizing the energy consumed by these robots. The simulation results of the proposed algorithm are compared to those obtained by the well-known algorithm NSGA-II. The results show the superiority of the proposed DLM-TLBO in terms of the quality of the obtained Pareto front approximation and convergence speed.

},
	url = {https://dl.acm.org/doi/abs/10.1145/3454127.3456609},
	author = {Aouadj, Wafa and Abdessemed, Mohamed-Rida and Rachid Seghir}
}
@article {6697,
	title = {A Reliable Behavioral Model: Optimizing Energy Consumption and Object Clustering Quality by Na{\"\i}ve Robots},
	journal = {International Journal of Swarm Intelligence Research (IJSIR)},
	volume = {12},
	number = {4},
	year = {2021},
	abstract = {
	This study concerns a swarm of autonomous reactive mobile robots, qualified of na{\"\i}ve because of their simple constitution, having the mission of gathering objects randomly distributed while respecting two contradictory objectives: maximizing quality of the emergent heap-formation and minimizing energy consumed by aforesaid robots. This problem poses two challenges: it is a multi-objective optimization problem and it is a hard problem. To solve it, one of renowned multi-objective evolutionary algorithms is used. Obtained solution, via a simulation process, unveils a close relationship between behavioral-rules and consumed energy; it represents the sought behavioral model, optimizing the grouping quality and energy consumption. Its reliability is shown by evaluating its robustness, scalability, and flexibility. Also, it is compared with a single-objective behavioral model. Results{\textquoteright} analysis proves its high robustness, its superiority in terms of scalability and flexibility, and its longevity measured based on the activity time of the robotic system that it integrates.

},
	url = {https://www.igi-global.com/article/a-reliable-behavioral-model/290283},
	author = {Aouadj, Wafa and Abdessemed, Mohamed-Rida}
}
