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Author: search.filters.author.Mellal, Mohamed ArezkiSubject: search.filters.subject.Multi-objective optimization

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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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    System reliability and cost optimization under various scenarios using NSGA-III
    (IEEE, 2020) Chebouba, Billal Nazim; Mellal, Mohamed Arezki; Adjerid, Smail; Benazzouz, Djamel
    Nowadays, industrial systems need to be as reliable as possible in order to ensure safety and competitiveness. This paper addresses the reliabilityredundancy allocation problem (RRAP) of an overspeed protection system in a power plant under various scenarios. Previously, this kind of optimization problems were solved using mathematical programming techniques and considered as a single objective optimization problem, however more recently, bio-inspired algorithms are used to solve this type of optimization problem. In the present work, a multi-objective evolutionary optimization algorithm, called the non-dominated sorting genetic algorithm (NSGA-III) is implemented to solve the problem under a set of nonlinear design constraints. The NSGA-III demonstrates its ability to generate a set of nondominated solutions. The results are discussed under various scenarios of minimum allowable reliability
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    Cost and Availability optimization of Overspeed Protection System in a Power Plant
    (IEEE, 2019) Mellal, Mohamed Arezki; Chebouba, Billal Nazim
    This paper addresses the cost and availability optimization of an overspeed protection system in a power plant. The literature has only treated the reliability or cost of this system as a single-objective. Therefore, the multi-objective optimization problem considering the availability and cost is presented. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to generate the Pareto front. The numerical results are discussed under two scenarios of minimum allowable availability.