Browsing by Author "Williams, Edward J."
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Item The cuckoo optimization algorithm and Its applications(2017) Mellal, Mohamed Arezki; Williams, Edward J.Item Cuckoo optimization algorithm for unit production cost in multi-pass turning operations(Springer, 2014) Mellal, Mohamed Arezki; Williams, Edward J.Item Cuckoo optimization algorithm with penalty function and binary approach for combined heat and power economic dispatch problem(Elsevier, 2020) Mellal, Mohamed Arezki; Williams, Edward J.This paper is related to a solution approach for the nonlinear and nonconvex combined heat and power economic dispatch problem (CHPED). It combines the cuckoo optimization algorithm with penalty function (PFCOA) published in “Mellal and Williams (2015)” and the binary approach published in “Geem and Cho (2012).” The binary approach discretizes the nonconvex operating feasible region into two convex regions in order to explore the whole operating region. A numerical case study involving four units is investigated and the superiority of the mixed method, i.e, the PFCOA with the binary approach is provedItem Cuckoo optimization algorithm with penalty function for combined heat and power economic dispatch problem(Elsevier, 2015) Mellal, Mohamed Arezki; Williams, Edward J.Item A discussion on “A GSO-based algorithm for combined heat and power dispatch problem with modified scrounger and ranger operators”(Elsevier, 2017) Mellal, Mohamed Arezki; Williams, Edward J.Item Erratum to : cuckoo optimization algorithm for unit production cost in multi-pass turning operations(Springer, 2017) Mellal, Mohamed Arezki; Williams, Edward J.Item 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 comparedItem Optimal conventional and nonconventional machining processes via particle swarm optimization and flower pollination algorithm(Springer Science and Business Media, 2025) Mellal, Mohamed Arezki; Tamazirt, Imene; Tiar, Maissa; Williams, Edward J.Manufacturing requires various machining processes. Nowadays, machining implies advanced technologies in order to meet more exacting process performance criteria. This paper addresses the optimization of four conventional and nonconventional machining processes: drilling, grinding, water jet machining (WJM), and wire electrical discharge machining (EDM). The input process parameters are: cutting speed, feed rate, cutting environment, depth of cut, grit size, water jet pressure, diameter of water jet nozzle, traverse rate of the nozzle, stand-off-distance, ignition pulse current, pulse-off time, pulse duration, servo reference mean voltage, servo speed variation, wire speed, wire tension, and injection pressure. The multi-objective EDM optimization problem is converted to a single-objective problem using the weighted-sum method. Two nature-inspired algorithms of artificial intelligence (AI) are implemented for solving these problems, namely the particle swarm optimization (PSO) and the flower pollination algorithm (FPA). Penalty functions are introduced to handle the constraints and to enhance the algorithms for better results. The machining outputs, required number of function evaluations, CPU time, and standard deviations are the performance metrics. The results obtained are compared and show better performance than that already documented in the literature.Item Optimal policy for the replacement of industrial systems subject to technological obsolescence using genetic algorithm(2013) Mellal, Mohamed Arezki; Adjerid, Smail; Benazzouz, Djamel; Berrazouane, Sofiane; Williams, Edward J.The technological obsolescence of industrial systems is characterized by the existence of challenger units possessing identical functionalities but with improved performance. This paper aims to define a new approach that makes it possible to obtain the optimal number of obsolete industrial systems which should be replaced by new-type units. This approach presents a new point of view compared with previous works available in the literature. The main idea and the originality of our approach is that we apply a genetic algorithm (GA) by considering the failure frequency, the influence of the environment/safety factors of the old-type systems and the purchase/implementation cost of the new-type units. These parameters are introduced in order to optimize this type of replacement in the context of engineeringItem Optimal replacement policy for obsolete components using cuckoo optimization algorithm based-approach : dependability context(NISCAIR-CSIR, India, 2012) Mellal, Mohamed Arezki; Adjerid, Smail; Williams, Edward J.; Benazzouz, DjamelItem Replacement optimization of industrial components subject to technological obsolescence using artificial intelligence(IEEE, 2017) Mellal, Mohamed Arezki; Adjerid, Smail; Williams, Edward J.