Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Optimal Sizing and Power Management Strategies of Islanded Microgrids for Remote Electrification Systems

Over the past few years, electrification of remote communities with an efficient utilization of on-site energy resources has entered a new phase of evolution. However, the planning tools and studies for the remote microgrids are considered inadequate. Moreover, the existing techniques have not taken into account the impact of reactive power on component sizes. Thus, this thesis concentrates on optimal sizing design of an islanded microgrid (IMG), which is composed of renewable energy resources (RERs), battery energy storage system (BESS), and diesel generation system (DGS), for the purpose of electrifying off-grid communities. Owing to the utilization of both BESS and DGS, four power management strategies (PMSs) are modeled upon analyzing the impacts of reactive power to chronologically simulate the IMG. In this work, two single-objective optimization (SOO) and two multiobjective optimization (MOO) approaches are developed for determining the optimal component sizes in an IMG. Chronological simulation and an enumeration-based search technique are adopted in the first SOO approach. Then, an accelerated SOO approach is proposed by adopting an improved piecewise aggregate approximation (IPAA)-based time series and a genetic algorithm (GA). Next, an adaptive weighted sum (AWS) method, in conjunction with an enumeration search technique, is adopted in a bi-objective optimization approach. Finally, an elitist non-dominated sorting GA-II (NSGA-II) technique is proposed for MOO of the IMG by introducing three objective functions. The enumeration-based SOO approach ensures a global optimum, determines the optimal sizes and PMSs simultaneously, and offers a realistic solution. The accelerated SOO approach significantly reduces the central processing unit (CPU) time without largely deviating the life cycle cost (LCC). The bi-objective optimal sizing approach generates a large number of evenly spread trade-off solutions both in regular and uneven regions upon adopting the LCC and renewable energy penetration (REP) as the objective functions. Using the MOO approach, one can produce a diversified set of Pareto optimal solutions, for both the component sizes and PMSs, at a reduced computational effort. The effectiveness of the proposed approaches is demonstrated by simulation studies in the MATLAB/Simulink software environment