Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flam Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches

Optimum design of steel building structures using migration-based vibrating particles system

Acknowledgment This research is supported by a research grant of the University of Tabriz (Number: 1615). We sincerely express our gratitude to Assoc. Prof. Saeid Kazemzadeh Azad for providing the required data for the design examples.Peer reviewedPostprin

The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)

The structural health monitoring (SHM) approach plays a key role not only in structural engineering but also in other various engineering disciplines by evaluating the safety and performance monitoring of the structures. The structural damage detection methods could be regarded as the core of SHM strategies. That is because the early detection of the damages and measures to be taken to repair and replace the damaged members with healthy ones could lead to economic advantages and would prevent human disasters. The optimization-based methods are one of the most popular techniques for damage detection. Using these methods, an objective function is minimized by an optimization algorithm during an iterative procedure. The performance of optimization algorithms has a significant impact on the accuracy of damage identification methodology. Hence, a wide variety of algorithms are employed to address optimization-based damage detection problems. Among different algorithms, the particle swarm optimization (PSO) approach has been of the most popular ones. PSO was initially proposed by Kennedy and Eberhart in 1995, and different variants were developed to improve its performance. This work investigates the objectives, methodologies, and results obtained by over 50 studies (2005-2020) in the context of the structural damage detection using PSO and its variants. Then, several important open research questions are highlighted. The paper also provides insights on the frequently used methodologies based on PSO, the computational time, and the accuracy of the existing methodologies

Ensemble of four metaheuristic using a weighted sum technique for aircraft wing design

Recently, metaheuristics (MHs) have become increasingly popular in real-world engineering applications such as in the design of airframes structures and aeroelastic designs owing to its simple, flexible, and efficient nature. In this study, a novel hybrid algorithm is termed as Ensemble of Genetic algorithm, Grey wolf optimizer, Water cycle algorithm and Population base increment learningusing Weighted sum (E-GGWP-W), based on the successive archive methodology of the weighted population has been proposed to solve the aircraft composite wing design problem. Four distinguished algorithms viz. a Genetic algorithm (GA), a Grey wolf optimizer (GWO), a Water cycle algorithm (WCA), and Population base increment learning (PBIL) were used as ingredients of the proposed algorithm. The considered wing design problem is posed for overall weight minimization subject to several aeroelastic and structural constraints along with multiple discrete design variables to ascertain its viability for real-world applications. The algorithms are validated through the standard test functions of the CEC-RW-2020 test suite and composite Goland wing aeroelastic optimization. To check the performance, the proposed algorithm is contrasted with eight well established and newly developed MHs. Finally, a statistical analysis is done by performing Friedman’s rank test and allocating respective ranks to the algorithms. Based on the outcome, ithas been observed that the suggested algorithm outperforms the others

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

Performance Analysis of Metaheuristic Optimization Algorithms in Estimating the Interfacial Heat Transfer Coefficient on Directional Solidification

In this paper is proposed an evaluation of ten metaheuristic optimization algorithms applied on the inverse optimization of the Interfacial Heat Transfer Coefficient (IHTC) coupled on the solidification phenomenon. It was considered an upward directional solidification system for Al-7wt.% Si alloy and, for IHTC model, a exponential time function. All thermophysical properties of the alloy were considered constant. Scheil Rule was used as segregation model ahead phase-transformation interface. Optimization results from Markov Chain Monte Carlo method (MCMC) were considered as reference. Based on average, quantiles 95% and 5%, kurtosis, average iterations and absolute errors of the metaheuristic methods, in relation to MCMC results, the Flower Pollination Algorithm (FPA) and Moth-Flame Optimization (MFO) presented the most appropriate results, outperforming the other methods in this particular phenomenon, based on these metrics. The regions with the most probable values for parameters in IHTC time function were also determined.Comment: 27 pages, 7 figures, 4 tables, 67 references cited, preprin