CFA optimizer: A new and powerful algorithm inspired by Franklin's and Coulomb's laws theory for solving the economic load dispatch problems

Copyright © 2018 John Wiley & Sons, Ltd. This paper presents a new efficient algorithm inspired by Franklin's and Coulomb's laws theory that is referred to as CFA algorithm, for finding the global solutions of optimal economic load dispatch problems in power systems. CFA is based on the impact of electrically charged particles on each other due to electrical attraction and repulsion forces. The effectiveness of the CFA in different terms is tested on basic benchmark problems. Then, the quality of the CFA to achieve accurate results in different aspects is examined and proven on economic load dispatch problems including 4 different size cases, 6, 10, 15, and 110-unit test systems. Finally, the results are compared with other inspired algorithms as well as results reported in the literature. The simulation results provide evidence for the well-organized and efficient performance of the CFA algorithm in solving great diversity of nonlinear optimization problems

A hybrid Jaya algorithm for reliability–redundancy allocation problems

© 2017 Informa UK Limited, trading as Taylor & Francis Group. This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching–learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability–redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series–parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30–100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results

Contributions au développement de politiques de remplacement préventif pour des sytèmes multi-composants

Dans cette thèse, nous proposons de développer des politiques de remplacement préventif pour des systèmes multi-composants. Ces systèmes sont composés de plusieurs composants selon une configuration bien déterminée et dont l’état se dégrade d’une manière aléatoire. Les politiques de remplacement définissent les actions à entreprendre en fonction de l'état du système ou de ses composants et ont pour objectif de retarder l'apparition des pannes et de prolonger la durée de vie du système. Sur le plan théorique, la généralisation des modèles de remplacement des systèmes mono-composants à des systèmes multi-composants n'est pas évidente. La difficulté réside essentiellement dans l’existence d’interaction ou de dépendance entre les différents composants du système. Nous nous sommes concentrés dans cette thèse sur les dépendances stochastique et économique entre les composants. Pour la dépendance stochastique, la propagation de la panne a été modélisée par l’effet domino pour un système parallèle à deux composants. Nous avons proposé deux politiques de remplacement de type Age. Dans la première politique, nous avons supposé que la structure des coûts est constante alors que dans la deuxième politique cette hypothèse a été modifiée en prenant une structure de coûts variable. Nous avons aussi proposé dans le cadre de la dépendance stochastique un modèle de remplacement bi-objectif qui optimise à la fois le coût espéré du remplacement et la disponibilité du système. Pour la dépendance économique, nous avons proposé une politique de remplacement basée sur le comptage des pannes pour un système parallèle et nous l’avons intégrée dans un modèle d’allocation de la redondance d’un système série-parallèle. Le modèle mathématique a été résolu par une approche heuristique basée sur l’algorithme du recuit simulé.The aim of this thesis is to develop preventive replacement policies for multi-component systems. Systems are composed of several components connected under a known configuration and subject to random failures. Each replacement policy defines the actions to be taken according to the state of the system or its components and it is intended to delay the occurrence of failures and extend the lifetime of the system. From the theoretical point of view, the extension of replacement models from single-component systems to multi-component systems is not obvious. The difficulty is due primarily to the interaction or dependence between the different components of the system. In this thesis the focus has been put on the stochastic and economic dependencies between components. For stochastic dependence the propagation of the failure is modeled by the domino effect for a two-component parallel system, and two age replacement policies are investigated. In the first policy, we assumed that the cost structure is constant whereas in the second policy a variable cost structure is assumed. We proposed also a bi-objective replacement model that optimizes both expected replacement cost rate and system availability. For economic dependence, we proposed a failure counting replacement policy for a parallel system and we integrated it in a redundancy allocation model for a serie-parallel system. The mathematical model has been built taking account of this policy and Simulated Annealing algorithm has been used as resolution approach

Optimization of systems reliability by metaheuristic approach

The application of metaheuristic approaches in addressing the reliability of systems through optimization is of greater interest to researchers and designers in recent years. Reliability optimization has become an essential part of the design and operation of largescale manufacturing systems. This thesis addresses the optimization of system-reliability for series–parallel systems to solve redundant, continuous, and combinatorial optimization problems in reliability engineering by using metaheuristic approaches (MAs). The problem is to select the best redundancy strategy, component, and redundancy level for each subsystem to maximize the system reliability under system-level constraints. This type of problem involves the selection of components with multiple choices and redundancy levels that yield the maximum benefits, and it is subject to the cost and weight constraints at the system level. These are very common and realistic problems faced in the conceptual design of numerous engineering systems. The development of efficient solutions to these problems is becoming progressively important because mechanical systems are becoming increasingly complex, while development plans are decreasing in size and reliability requirements are rapidly changing and becoming increasingly difficult to adhere to. An optimal design solution can be obtained very frequently and more quickly by using genetic algorithm redundancy allocation problems (GARAPs). In general, redundancy allocation problems (RAPs) are difficult to solve for real cases, especially in large-scale situations. In this study, the reliability optimization of a series–parallel by using a genetic algorithm (GA) and statistical analysis is considered. The approach discussed herein can be applied to address the challenges in system reliability that includes redundant numbers of carefully chosen modules, overall cost, and overall weight. Most related studies have focused only on the single-objective optimization of RAP. Multiobjective optimization has not yet attracted much attention. This research project examines the multiobjective situation by focusing on multiobjective formulation, which is useful in maximizing system reliability while simultaneously minimizing system cost and weight to solve the RAP. The present study applies a methodology for optimizing the reliability of a series–parallel system based on multiobjective optimization and multistate reliability by using a hybrid GA and a fuzzy function. The study aims to determine the strategy for selecting the degree of redundancy for every subsystem to exploit the general system reliability depending on the overall cost and weight limitations. In addition, the outcomes of the case study for optimizing the reliability of the series–parallel system are presented, and the relationships with previously investigated phenomena are presented to determine the performance of the GA under review. Furthermore, this study established a new metaheuristic-based technique for resolving multiobjective optimization challenges, such as the common reliability redundancy allocation problem. Additionally, a new simulation process was developed to generate practical tools for designing reliable series–parallel systems. Hence, metaheuristic methods were applied for solving such difficult and complex problems. In addition, metaheuristics provide a useful compromise between the amount of computation time required and the quality of the approximated solution space. The industrial challenges include the maximization of system reliability subject to limited system cost and weight, minimization of system weight subject to limited system cost and the system reliability requirements and increasing of quality components through optimization and system reliability. Furthermore, a real-life situation research on security control of a gas turbine in the overspeed state was explored in this study with the aim of verifying the proposed algorithm from the context of system optimization