Browsing by Author "Mellal, Mohamed Arezki"

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An adaptive cuckoo optimization algorithm for system design optimization under failure dependencies
(SAGE Publications, 2019) Mellal, Mohamed Arezki; Zio, Enrico
This article presents an algorithm for optimal redundancy and repair team allocation with respect to minimum system cost and a system availability constraint. Four scenarios are considered for the failures occurring in the subsystems of the system: independence, linear dependence, weak dependence, and strong dependence. An adaptive cuckoo optimization algorithm is developed for solving the nonlinear integer optimization problem of allocation. A series–parallel system with six subsystems is considered as a case study for demonstration purposes. The results obtained highlight the good performance of the developed algorithm
Combined heat and power economic dispatch problem with binary method using flower pollination algorithm and differential evolution
(Springer, 2023) Mellal, Mohamed Arezki; Khitous, Marwa; Zemmouri, Meriem
Nowadays, the need for electrical energy became crucial in the world. The co-generation plants, which simultaneously produce electrical and heat energies, are one of the alternative solutions to supply people and industry with both energies. The present work addresses the cost minimization of the nonconvex combined heat and power dispatch problem (CHPED). The nonconvex operating region is handled using the binary method, and the optimization problem is solved using two nature-inspired algorithms, namely the flower pollination algorithm (FPA) and the differential evolution (DE). Penalty functions are adopted to handle all the operating constraints, units’ limits, and demands. The results obtained compare the algorithms and those of the literature. It is observed that the fuel cost obtained by the flower pollination algorithm (FPA) is less than the one obtained by the differential evolution (DE) and the particle swarm optimization (PSO)
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.
The cuckoo optimization algorithm and Its applications
(2017) Mellal, Mohamed Arezki; Williams, Edward J.
Cuckoo optimization algorithm for unit production cost in multi-pass turning operations
(Springer, 2014) Mellal, Mohamed Arezki; Williams, Edward J.
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 proved
Cuckoo optimization algorithm with penalty function for combined heat and power economic dispatch problem
(Elsevier, 2015) Mellal, Mohamed Arezki; Williams, Edward J.
Design of a cost and reliability optimized framework for the techno-economic analysis of a stand-alone PV/FC Li-ion battery system
(Springer Science and Business Media Deutschland GmbH, 2025) Berrazouane, Sofiane; Alem, Said; Mellal, Mohamed Arezki
The multiple advantages of the Li-ion batteries and the long-term storage capacity of hydrogen are exploited in this work to obtain an optimal system configuration and achieve energy self-sufficiency. The degradation cost of the battery, based on both calendar and cyclic aging, is included to provide a more detailed and accurate techno-economic balance and estimation. Moreover, the operating cost of the battery and hydrogen system is considered in the energy management strategy (EMS) to determine the prioritized storage system to be used. Therefore, to economize the costs and increase the reliability of the standalone photovoltaic/fuel cell (PV/FC) Li-ion battery system, an EMS is developed by customizing and adapting the Improved Grey Wolf Optimizer (IGWO), referred to as the Modified Improved Grey Wolf Optimizer (M-IGWO). The proposed system achieves a Levelized Cost of Energy (LCOE) of 0.3257 $/kWh when Loss of Power Supply Probability (LPSP) is less than 1%, with 68.18% use of hydrogen system over the time and 26.11% of the battery, demonstrating high reliability. Depending on the operating mode, the battery utilization to support H2_system is decreasing from 10.71% to 1.18%, while the hydrogen system utilization to support battery is decreasing from 29.64% to 9.16%. These findings highlight the effectiveness of the M-IGWO algorithm and EMS in optimizing PV/FC Li-ion battery system for various scenarios
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.
Erratum to : cuckoo optimization algorithm for unit production cost in multi-pass turning operations
(Springer, 2017) Mellal, Mohamed Arezki; Williams, Edward J.
Fuzzy multiobjective system reliability optimization by genetic algorithms and clustering analysis
(WILEY ONLINE Library, 2020) Chebouba, Billal Nazim; Mellal, Mohamed Arezki; Smail Adjerid
System reliability optimization is a key element for a competitive and safe industrial plant. This paper addresses the multiobjective system reliability optimization in the presence of fuzzy data. A framework solution approach is proposed and based on four steps: defuzzify the data into crisp values by the ranking function procedure, the defuzzified problems are solved by the non‐sorting genetic algorithms II and III (NSGA‐II and NSGA‐III), the Pareto fronts are compared by the spacing method for selecting the best one, and then the best Pareto front is reduced by the clustering analysis for helping the decision maker. A case study presented in the literature as a mono‐objective redundancy allocation problem with fuzzy data is investigated in the present paper as multiobjective redundancy allocation and reliability‐redundancy allocation problems show the applicability of the approach.
Genetic algorithm for multiobjective optimization : applied in high speed machining milling operation
(2012) Mokhtari, Hicham; Ouziala, Mahdi; Mellal, Mohamed Arezki; Belaidi, Idir; Alem, Said; Berrazouane, Sofiane
Genetic Algorithms (GAs) are general-purpose heuristic search algorithms that mimic the evolutionary process in order to find the fittest solutions. The algorithms were introduced by Holland in 1975. Since then, they have received growing interest due to their ability to discover good solutions quickly for complex searching and optimization problems. Simple genetic algorithms have been developed to solve the problems of multi objective optimization, such as NSGA II. The objective of this research is to apply the elitist non-dominated sorting GA (NSGA-II) for multi-objective optimization problems in case of high speed machining for the milling operation. The implemented model under Matlab, allows, from a considered space research. We have optimized the values of Vc and f, for an imposed Depth, while the production cost and time are minimized, under technical constrains of the production system
Guest editorial : reliability and quality : analysis and applications
(Emerald Group Holdings, 2022) Bhargava, Cherry; Sharma, Pardeep Kumar; Patil, Rajkumar Bhimgonda; Mellal, Mohamed Arezki
Harnessing AI for control engineering
(IGI Global, 2025) Mellal, Mohamed Arezki
In the field of control engineering, the integration of artificial intelligence (AI) has opened new avenues for innovation and efficiency. By leveraging machine learning, neural networks, and advanced optimization algorithms, AI can enhance system performance, improve decision-making, and enable real-time adaptive control. These technologies empower engineers to design more robust, efficient, and autonomous systems that can respond to complex, dynamic environments with precision. Further research of AI and control engineering may address challenges of traditional methods and pave the way for smarter, more sustainable industrial processes. Harnessing AI for Control Engineering delves into the transformative integration of artificial intelligence (AI) within the domain of control engineering. It navigates the landscape of AI applications, from classical control methods to cutting-edge machine learning algorithms and nature-inspired optimization techniques. This book covers topics such as civil engineering, fault detection and diagnosis, and robotics, and is a useful resource for engineers, business owners, academicians, researchers, and scientists
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.
Modelling and simulation of mechatronic system to integrated design of supervision : using a bond graph approach
(2011) Mellal, Mohamed Arezki; Adjerid, Smail; Benazzouz, Djamel
The research in mechatronics focuses on the design and implementation of reliable, secure and economic systems. Our study is to modeling the operative part of a CNC machine using a bond graph approach with optimal placement of sensors in order to achieve a model for an integrated design of supervision. The proposed model allows a conception technically feasible and economically realizable to be integrated into production lines. The generation of analytical redundancy relations can find the FDI (Fault Detection and Isolation) matrix, that optimizes the maintenance function
A multi-objective design optimization framework for wind turbines under altitude consideration
(Elsevier, 2020) Mellal, Mohamed Arezki; Pecht, Michael
Wind power is one of the most used renewable energy sources; however, effective wind turbine design remains a challenge. This paper proposes a framework to explore the multi-objective design optimization of wind turbines and find the best compromise solution by considering the altitude. The design objectives are minimization of the cost of energy and maximization of the rated power by considering the rotor radius and hub height with respect to the structural design constraints. The Pareto envelope based selection algorithm II (PESA-II) and two versions of the non-dominated sorting genetic algorithm II and III (NSGA-II & NSGA-III) with a mutation strategy and a constraint handling method are implemented to generate Pareto fronts. Five comparing metrics are used to identify the most efficient optimization technique, and two decision methods are adopted to find the best compromise solution. A case study is solved under two altitude scenarios. The overall results show that NSGA-II performs better than the other algorithms, and the obtained best compromise solution is within the design limits. It is also observed that when the altitude increases, the cost of energy increases, and the rated power decreases
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
Multi-objective optimization of series-parallel system with mixed subsystems failure dependencies using NSGA-II and MOHH
(SAGE, 2024) Mellal, Mohamed Arezki
In complex systems, failure dependencies play a crucial role in determining their overall performance. This paper explores the multi-objective optimization of series-parallel systems with mixed failure dependencies. By optimizing system cost and availability, the study aims to identify the most efficient redundancy and repair strategies. Two optimization algorithms, the non-dominated sorting genetic algorithm II (NSGA-II) and a novel multi-objective algorithm named the multi-objective hoopoe heuristic (MOHH), are utilized alongside constraint handling techniques to produce Pareto fronts. These fronts illustrate the trade-offs between cost and availability. Additionally, a fuzzy decision method is utilized to determine the best compromise solutions from each optimization technique. Comparing the results, NSGA-II consistently outperforms MOHH in providing better compromise solutions across five independent runs. However, MOHH demonstrates a better standard deviation in its performance.
Multi-objective Pipeline Network Reliability Optimization by Particle Swarm Optimization
(2019) Chebouba, Billal Nazim; Mellal, Mohamed Arezki; Adjerid, Smail
This paper presents the use of a multi-objective optimization algorithm for solving the reliability allocation problem in case of reliability of pipeline installations in a network configuration. Usually, this kind of problem is considered as a single objective subject to one or several nonlinear constraints and solved by using either mathematical programming techniques or more recently by special metaheuristics. In the present work, the problem is considered as a multi-objective optimization problem. The MOPSO algorithm is used and demonstrates its ability to identify the set of optimal solutions (Pareto front) providing to the decision maker the optimal solution space