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

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Multi-criteria reliability optimization for a complex system with a bridge structure in a fuzzy environment : A fuzzy multi-criteria genetic algorithm approach

Abstract: Optimizing system reliability in a fuzzy environment is complex due to the presence of imprecise multiple decision criteria such as maximizing system reliability and minimizing system cost. This calls for multi-criteria decision making approaches that incorporate fuzzy set theory concepts and heuristic methods. This paper presents a fuzzy multi-criteria nonlinear model, and proposes a fuzzy multi-criteria genetic algorithm (FMGA) for complex bridge system reliability design in a fuzzy environment. The algorithm uses fuzzy multi-criteria evaluation techniques to handle fuzzy goals, preferences, and constraints. The evaluation approach incorporates fuzzy preferences and expert choices of the decision maker in regards to cost and reliability goals. Fuzzy evaluation gives the algorithm flexibility and adaptability, yielding near-optimal solutions within short computation times. Results from computational experiments based on benchmark problems demonstrate that the FMGA approach is a more reliable and effective approach than best known algorithm, especially in a fuzzy multi-criteria environment

Software Reliability Prediction using Correlation Constrained Multi-Objective Evolutionary Optimization Algorithm

Software reliability frameworks are extremely effective for estimating the probability of software failure over time. Numerous approaches for predicting software dependability were presented, but neither of those has shown to be effective. Predicting the number of software faults throughout the research and testing phases is a serious problem. As there are several software metrics such as object-oriented design metrics, public and private attributes, methods, previous bug metrics, and software change metrics. Many researchers have identified and performed predictions of software reliability on these metrics. But none of them contributed to identifying relations among these metrics and exploring the most optimal metrics. Therefore, this paper proposed a correlation- constrained multi-objective evolutionary optimization algorithm (CCMOEO) for software reliability prediction. CCMOEO is an effective optimization approach for estimating the variables of popular growth models which consists of reliability. To obtain the highest classification effectiveness, the suggested CCMOEO approach overcomes modeling uncertainties by integrating various metrics with multiple objective functions. The hypothesized models were formulated using evaluation results on five distinct datasets in this research. The prediction was evaluated on seven different machine learning algorithms i.e., linear support vector machine (LSVM), radial support vector machine (RSVM), decision tree, random forest, gradient boosting, k-nearest neighbor, and linear regression. The result analysis shows that random forest achieved better performance

Using a Hybrid Evolutionary Algorithm for Solving Signal Transmission Station Location and Allocation Problem with Different Regional Communication Quality Restriction

This study aims to investigate the signal transmission station location-allocation problems with the various restricted regional constraints. In each constraint, the types of signal transmission stations and the corresponding numbers and locations are to be decided at the same time. Inappropriate set up of stations is not only causing the unnecessary cost but also making the poor service quality. In this study, we proposed a hybrid evolutionary approach integrating the immune algorithm with particle swarm optimization (IAPSO) to solve this problem where each of the regions is with different maximum failure rate restrictions. We compared the performance of the proposed method with commercial optimization software LINGO®. According to the experimental results, solutions obtained by our IAPSO are better than or as well as the best solutions obtained by LINGO®. It is expected that our research can provide the telecommunication enterprise the optimal/near-optimal strategies for the setup of signal transmission stations

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

Enhancement of Metaheuristic Algorithm for Scheduling Workflows in Multi-fog Environments

Whether in computer science, engineering, or economics, optimization lies at the heart of any challenge involving decision-making. Choosing between several options is part of the decision- making process. Our desire to make the "better" decision drives our decision. An objective function or performance index describes the assessment of the alternative's goodness. The theory and methods of optimization are concerned with picking the best option. There are two types of optimization methods: deterministic and stochastic. The first is a traditional approach, which works well for small and linear problems. However, they struggle to address most of the real-world problems, which have a highly dimensional, nonlinear, and complex nature. As an alternative, stochastic optimization algorithms are specifically designed to tackle these types of challenges and are more common nowadays. This study proposed two stochastic, robust swarm-based metaheuristic optimization methods. They are both hybrid algorithms, which are formulated by combining Particle Swarm Optimization and Salp Swarm Optimization algorithms. Further, these algorithms are then applied to an important and thought-provoking problem. The problem is scientific workflow scheduling in multiple fog environments. Many computer environments, such as fog computing, are plagued by security attacks that must be handled. DDoS attacks are effectively harmful to fog computing environments as they occupy the fog's resources and make them busy. Thus, the fog environments would generally have fewer resources available during these types of attacks, and then the scheduling of submitted Internet of Things (IoT) workflows would be affected. Nevertheless, the current systems disregard the impact of DDoS attacks occurring in their scheduling process, causing the amount of workflows that miss deadlines as well as increasing the amount of tasks that are offloaded to the cloud. Hence, this study proposed a hybrid optimization algorithm as a solution for dealing with the workflow scheduling issue in various fog computing locations. The proposed algorithm comprises Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO). In dealing with the effects of DDoS attacks on fog computing locations, two Markov-chain schemes of discrete time types were used, whereby one calculates the average network bandwidth existing in each fog while the other determines the number of virtual machines existing in every fog on average. DDoS attacks are addressed at various levels. The approach predicts the DDoS attack’s influences on fog environments. Based on the simulation results, the proposed method can significantly lessen the amount of offloaded tasks that are transferred to the cloud data centers. It could also decrease the amount of workflows with missed deadlines. Moreover, the significance of green fog computing is growing in fog computing environments, in which the consumption of energy plays an essential role in determining maintenance expenses and carbon dioxide emissions. The implementation of efficient scheduling methods has the potential to mitigate the usage of energy by allocating tasks to the most appropriate resources, considering the energy efficiency of each individual resource. In order to mitigate these challenges, the proposed algorithm integrates the Dynamic Voltage and Frequency Scaling (DVFS) technique, which is commonly employed to enhance the energy efficiency of processors. The experimental findings demonstrate that the utilization of the proposed method, combined with the Dynamic Voltage and Frequency Scaling (DVFS) technique, yields improved outcomes. These benefits encompass a minimization in energy consumption. Consequently, this approach emerges as a more environmentally friendly and sustainable solution for fog computing environments