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

Optimal maintenance of multi-component systems: a review

In this article we give an overview of the literature on multi-component maintenance optimization. We focus on work appearing since the 1991 survey "A survey of maintenance models for multi-unit systems" by Cho and Parlar. This paper builds forth on the review article by Dekker et al. (1996), which focusses on economic dependence, and the survey of maintenance policies by Wang (2002), in which some group maintenance and some opportunistic maintenance policies are considered. Our classification scheme is primarily based on the dependence between components (stochastic, structural or economic). Next, we also classify the papers on the basis of the planning aspect (short-term vs long-term), the grouping of maintenance activities (either grouping preventive or corrective maintenance, or opportunistic grouping) and the optimization approach used (heuristic, policy classes or exact algorithms). Finally, we pay attention to the applications of the models.literature review;economic dependence;failure interaction;maintenance policies;grouping maintenance;multi-component systems;opportunistic maintenance;maintencance optimization;structural dependence

Evolution of maintenance strategies in oil and gas industries: the present achievements and future trends

Engineering Systems maintenance and reliability challenges have drawn serious attention of researchers and industrialists all over the world due to continuous evolution, innovation and complexity of modern technologies deployed in manufacturing and production systems. These systems need very high reliability and availability due to business, mission and safety critical nature of their operations. This paper reviews evolution of systems or equipment maintenance strategies practiced over the years in complex industrial and manufacturing systems such as oil and gas production systems, satellite communication system, spacecraft navigational system, nuclear power plants, etc. The paper also examines the current maintenance and reliability philosophies, their limitations and highlights major breakthroughs and achievements with regards to complex engineering systems maintenance. Intelligent maintenance, a novel approach to complex engineering systems maintenance and reliability sustainment is proposed. The proposed approach reintegrates operation and maintenance phase into system development life cycle, adopts advanced engineering tools and methodology in developing condition-based predictive maintenance, an intelligent maintenance system with resilient, autonomous and adaptive capabilities. Application of Neural network approach to multisensor data fusion for condition-based predictive maintenance system is briefly presented

System reliability optimization : a fuzzy genetic algorithm approach

System reliability optimization is often faced with imprecise and conflicting goals such as reducing the cost of the system and improving the reliability of the system. The decision making process becomes fuzzy and multi-objective. In this paper, we formulate the problem as a fuzzy multi-objective nonlinear program (FMOOP). A fuzzy multiobjective genetic algorithm approach (FMGA) is proposed for solving the multi-objective decision problem in order to handle the fuzzy goals and constraints. The approach is able flexible and adaptable, allowing for intermediate solutions, leading to high quality solutions. Thus, the approach incorporates the preferences of the decision maker concerning the cost and reliability goals through the use of fuzzy numbers. The utility of the approach is demonstrated on benchmark problems in the literature. Computational results show that the FMGA approach is promising

Redundancy, Reliability Updating, and Risk-Based Maintenance Optimization of Aging Structures

The presence of uncertainties in the structural design process requires the incorporation of system reliability and redundancy concepts in the design specifications. AASHTO Load and Resistance Factor Design specifications utilize a factor relating to redundancy from the load side in the strength limit state to account for system redundancy in the component design. However, the classification of the component redundancy level is very general and the evaluation of values for this factor is also very subjective. Moreover, this factor does not account for several parameters that have significant effects on the system redundancy. Therefore, there is room for further improvement in the classification of the redundancy level and quantification of the associated values. Structural safety is of paramount importance during the entire lifetime of a structure. Aggressive environmental conditions such as corrosion and / or extreme events such as earthquakes and scour can cause a reduced level of structural safety and functionality under uncertainties. For this reason, assessment of structural performance using probabilistic performance measures such as reliability, redundancy and risk is necessary to determine if maintenance actions need to be applied. Due to the financial constraints on the maintenance budget, optimization tools should be incorporated in the structural maintenance process for seeking the effective and economical solution. The accuracy of performance assessment affects the efficiency of decision making on the maintenance. To enhance the accuracy of the assessment results, objective data from structural health monitoring can be integrated with the prior information on resistances and / or load effects to obtain a better estimation. The main objective of this study is two-fold: firstly, to propose a redundancy factor considering the effects of several parameters to provide a rational reliability-based design of structural components; secondly, to develop general approaches for integrating the reliability- and risk-based performance indicators in the life-cycle management framework for structures. Redundancy factors for a wide range of systems consisting of different number of components are evaluated considering several correlation cases. An approach for evaluating time-variant reliability, redundancy, direct and indirect risk considering the effects of resistance deterioration, system modeling type and correlations among failure modes of components is proposed. A risk-based approach for optimum maintenance of bridges under traffic and earthquake loads is also developed. Furthermore, a methodology for assessing risk caused by partially or fully closure of bridge lanes due to traffic load and scour is proposed. Finally, approaches for incorporating structural heath monitoring data in the reliability and redundancy assessment of ship structures by updating one and two parameters using Bayesian method are developed. The proposed new definition of redundancy factor improves the classification of redundancy levels of structural components and quantification of the factor relating to redundancy used in the current AASHTO Load and Resistance Factor Design specifications by considering several parameters which have significant effects on structural redundancy. The direct, indirect and total risks caused by component failure based on the developed event-tree model can provide guidance on determining the maintenance priorities of bridge components. The proposed approaches for assessing the time-variant risk due to bridge failure / lanes closure under traffic and earthquake / scour hazards can be efficiently used for obtaining lifetime risk profiles based on which the optimum risk mitigation strategies can be determined through the proposed risk-based optimization process. Finally, the developed Bayesian updating approaches provide a way to make efficient use of the acquired SHM information to improve the accuracy in the performance assessment of naval ships and highway bridges

An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm

AbstractThe main goal of the present paper is to present a penalty based cuckoo search (CS) algorithm to get the optimal solution of reliability – redundancy allocation problems (RRAP) with nonlinear resource constraints. The reliability – redundancy allocation problem involves the selection of components' reliability in each subsystem and the corresponding redundancy levels that produce maximum benefits subject to the system's cost, weight, volume and reliability constraints. Numerical results of five benchmark problems are reported and compared. It has been shown that the solutions by the proposed approach are all superior to the best solutions obtained by the typical approaches in the literature are shown to be statistically significant by means of unpaired pooled t-test