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    DEMAP: differential evolution mapping for network on chip optimization
    (Intelektual Pustaka Media Utama, 2023) Bougherara, Maamar; Amara, Rafik; Kemcha, Rebiha
    Network-on-chip (NoC) is a new paradigm for system-on-chip (SoC) design, which facilitates the interconnection and integration of complex components. Since this technology is still new, significant research efforts are needed to ac-celerate and simplify the design phases. Mapping is a critical phase in the NoC design process, as a mismatch of application software components can signif-icantly impact the final system’s performance. Therefore, it is essential to develop automated tools and methods to ensure this step. The main objective of this project is to develop a new approach that can be used to map applications on the NoC architecture to reduce communication costs. To achieve this goal, we have opted for an optimization algorithm, specifically the differential evolution algorithm.
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    Improvement of system reliability in a natural gas processing facility by PSO and DE
    (Springer Nature, 2024) Saheb, Tafsouthe; Mellal, Mohamed Arezki
    The reliability of the systems as well as its optimization is the first concern of the designers. The elements of a given system can be either in series, parallel, parallel-series, or in a complex configuration. This paper addresses the reliability optimization of a natural gas processing facility. The reliability of this system is calculated and two redundancies strategies, active and standby, are optimized under the resource limits to improve reliability. Two bio-inspired optimization algorithms, namely the particle swarm optimization (PSO) and the differential evolution (DE), are implemented with penalty functions to find the optimal redundancy. The results obtained are compared.
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    Multi-objective factors optimization in fused deposition modelling with particle swarm optimization and differential evolution
    (Springer, 2022) Mellal, Mohamed Arezki; Laifaoui, Chahinaze; Ghezal, Fahima; Williams, Edward J.
    The design of any system contemplates the elaboration of a prototype of the entire system or some parts, before the manufacturing phase. Nowadays, rapid prototyping (RP) is widely used by the designers. Achieving good manufacturing performances needs to handle various process parameters. Most works deal with single objective process parameters. The reality is quite different and the processes involve conflicting objectives. This paper addresses the multi-objective factors optimization of the fused deposition modelling (FDM) technology. The problem is converted into a single one using the weighted-sum method and then solved by resorting to two nature-inspired computing techniques, namely particle swarm optimization (PSO) and differential evolution (DE). The results obtained are compared
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    Speed control of DC motor using PID and FOPID controllers based on differential evolution and PSO
    (INASS, 2018) Idir, Abdelhakim; Kidouche, Madjid; Bensafia, Yassine
    DC motors are widely used in industrial application for its different advantage such us high efficiency, low costs and flexibilities. For controlling the speed of DC motor, conventional controller PI and PID were the most widely used controllers. But due to empirically selected parameters 𝐾𝑝,𝐾𝑖,𝐾𝑑 and limitation of convention PID controller to achieve ideal control effect for higher order systems, a Fractional order Proportional-Integral-Derivative PID (FOPID) based on optimization techniques was proposed in this paper. The aim of this paper is to study the tuning of a FOPID controller using intelligent soft computing techniques such as Differential Evolution (DE) and Particle Swarm Optimization (PSO) for designing fractional order PID controller. The parameters of FOPID controller are determined by minimizing the Integral Time Absolute Error (ITAE) between the output of reference model and the plant. The performance of DE and PSO were compared with several simulation experiments. The simulation results show that the DE-based FOPID controller tuning approach provides improved performance for the setpoint tracking, error minimization, and measurement noise attenuation
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    Pure Co2-Oil system minimum miscibility pressure prediction using optimized artificial neural network by differential evolution
    (2018) Nait Amar, Menad; Zeraibi, Noureddine; Redouane, Kheireddine
    Miscible CO2 flooding is one of the most attractive enhanced oil recovery options thanks to its microscopic efficiency improvement. A successful implementation of this method depends mainly on the accurate estimation of minimum miscibility pressure (MMP) of the CO2-oil system. As the determination of MMP through experimental tests (slim tube, and rising bubble apparatus (RBA)) is very expensive and time consuming, many correlations have been developed. However, all these correlations are based on limited set of experimental data and specified range of conditions, thus making their accuracies questionable. In this research, we propose to build robust, fast and cheap approach to predict MMP for pure CO2-oil by applying hybridization of artificial neural networks with differential evolution (DE). DE is used to find best initial weights and biases of neural network. Four parameters that affecting the MMP are chosen as input variables: reservoir temperature, mole fraction of volatile-oil components, mole fraction of intermediate-oil components and molecular weight of components C5+. 105 MMP data covering wide range of conditions are considered from the published literature to establish the model. The obtained results demonstrate that our approach outperforms all the published correlations in term of accuracy and reliability