Optimal power flow using Hybrid Particle Swarm Optimization and Moth Flame Optimizer approach

In this study, the most common problem of the current power system named optimal power flow (OPF) is optimized using the recently hybrid meta-heuristic optimization technique Particle Swarm Optimization-Moth Flame Optimizer (PSO-MFO) algorithm. Hybrid PSO-MFO is an incorporation of PSO used for exploitation stage and MFO for exploration stage in an uncertain environment. The position and velocity of the particle are restructured according to Moth and flame location in each iteration. The hybrid PSO-MFO technique is carried out to solve the OPF problem. The performance of this technique is deliberated and evaluated on the standard IEEE 30-bus and IEEE 57-bus test system. The problems considered in the OPF are fuel cost reduction, Voltage stability enhancement and Active power loss minimization. The results obtained with hybrid PSO-MFO technique is compared with original PSO and MFO

An Enhanced Moth-Flame Optimization with Multiple Flame Guidance Mechanism for Parameter Extraction of Photovoltaic Models

How to accurately and efficiently extract photovoltaic (PV) model parameters is the primary problem of photovoltaic system optimization. To accurately and efficiently extract the parameters of PV models, an enhanced moth-flame optimization (EMFO) with multiple flame guidance mechanism is proposed in this study. In EMFO, an adaptive flame number updating mechanism is used to adaptively control the flame number, which enhances the local and global exploration capabilities of MFO. Meanwhile, a multiple flame guidance mechanism is designed for the full use of the position information of flames, which enhances the global diversity of the population. The EMFO is evaluated with other variants of the MFO on 25 benchmark functions of CEC2005, 28 functions of CEC2017, and 5 photovoltaic model parameter extraction problems. Experimental results show that the EMFO has obtained a better performance than other compared algorithms, which proves the effectiveness of the proposed EMFO. The method proposed in this study provides MFO researchers with ideas for adaptive research and making full use of flame population information

Evolutionary approach to construct robust codes for DNA-based data storage

DNA is a practical storage medium with high density, durability, and capacity to accommodate exponentially growing data volumes. A DNA sequence structure is a biocomputing problem that requires satisfying bioconstraints to design robust sequences. Existing evolutionary approaches to DNA sequences result in errors during the encoding process that reduces the lower bounds of DNA coding sets used for molecular hybridization. Additionally, the disordered DNA strand forms a secondary structure, which is susceptible to errors during decoding. This paper proposes a computational evolutionary approach based on a synergistic moth-flame optimizer by Levy flight and opposition-based learning mutation strategies to optimize these problems by constructing reverse-complement constraints. The MFOS aims to attain optimal global solutions with robust convergence and balanced search capabilities to improve DNA code lower bounds and coding rates for DNA storage. The ability of the MFOS to construct DNA coding sets is demonstrated through various experiments that use 19 state-of-the-art functions. Compared with the existing studies, the proposed approach with three different bioconstraints substantially improves the lower bounds of the DNA codes by 12–28% and significantly reduces errors

Recent meta-heuristic algorithms with a novel premature covergence method for determining the parameters of pv cells and modules

Currently, the incorporation of solar panels in many applications is a booming trend, which necessitates accurate simulations and analysis of their performance under different operating conditions for further decision making. In this paper, various optimization algorithms are addressed comprehensively through a comparative study and further discussions for extracting the unknown parameters. Efficient use of the iterations within the optimization process may help meta-heuristic algorithms in accelerating convergence plus attaining better accuracy for the final outcome. In this paper, a method, namely, the premature convergence method (PCM), is proposed to boost the convergence of meta-heuristic algorithms with significant improvement in their accuracies. PCM is based on updating the current position around the best-so-far solution with two-step sizes: the first is based on the distance between two individuals selected randomly from the population to encourage the exploration capability, and the second is based on the distance between the current position and the best-so-far solution to promote exploitation. In addition, PCM uses a weight variable, known also as a controlling factor, as a trade-off between the two-step sizes. The proposed method is integrated with three well-known meta-heuristic algorithms to observe its efficacy for estimating efficiently and effectively the unknown parameters of the single diode model (SDM). In addition, an RTC France Si solar cell, and three PV modules, namely, Photowatt-PWP201, Ultra 85-P, and STM6-40/36, are investigated with the improved algorithms and selected standard approaches to compare their performances in estimating the unknown parameters for those different types of PV cells and modules. The experimental results point out the efficacy of the PCM in accelerating the convergence speed with improved final outcomes

MSA for Optimal Reconfiguration and Capacitor Allocation in Radial/Ring Distribution Networks

This work presents a hybrid heuristic search algorithm called Moth Swarm Algorithm (MSA) in the context of power loss minimization of radial distribution networks (RDN) through optimal allocation and rating of shunt capacitors for enhancing the performance of distribution networks. With MSA, different optimization operators are used to mimic a set of behavioral patterns of moths in nature, which allows for flexible and powerful optimizer. Hence, a new dynamic selection strategy of crossover points is proposed based on population diversity to handle the difference vectors Lévy-mutation to force MSA jump out of stagnation and enhance its exploration ability. In addition, a spiral motion, adaptive Gaussian walks, and a novel associative learning mechanism with immediate memory are implemented to exploit the promising areas in the search space. In this article, the MSA is tested to adapt the objective function to reduce the system power losses, reduce total system cost and consequently increase the annual net saving with inequity constrains on capacitor size and voltage limits. The validation of the proposed algorithm has been tested and verified through small, medium and large scales of standard RDN of IEEE (33, 69, 85-bus) systems and also on ring main systems of 33 and 69-bus. In addition, the obtained results are compared with other algorithms to highlight the advantages of the proposed approach. Numerical results stated that the MSA can achieve optimal solutions for losses reduction and capacitor locations with finest performance compared with many existing algorithms

A Survey of Feature Selection Strategies for DNA Microarray Classification

Classification tasks are difficult and challenging in the bioinformatics field, that used to predict or diagnose patients at an early stage of disease by utilizing DNA microarray technology. However, crucial characteristics of DNA microarray technology are a large number of features and small sample sizes, which means the technology confronts a "dimensional curse" in its classification tasks because of the high computational execution needed and the discovery of biomarkers difficult. To reduce the dimensionality of features to find the significant features that can employ feature selection algorithms and not affect the performance of classification tasks. Feature selection helps decrease computational time by removing irrelevant and redundant features from the data. The study aims to briefly survey popular feature selection methods for classifying DNA microarray technology, such as filters, wrappers, embedded, and hybrid approaches. Furthermore, this study describes the steps of the feature selection process used to accomplish classification tasks and their relationships to other components such as datasets, cross-validation, and classifier algorithms. In the case study, we chose four different methods of feature selection on two-DNA microarray datasets to evaluate and discuss their performances, namely classification accuracy, stability, and the subset size of selected features. Keywords: Brief survey; DNA microarray data; feature selection; filter methods; wrapper methods; embedded methods; and hybrid methods. DOI: 10.7176/CEIS/14-2-01 Publication date:March 31st 202

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

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

MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems

Many-objective optimization (MaO) deals with a large number of conflicting objectives in optimization problems to acquire a reliable set of appropriate non-dominated solutions near the true Pareto front, and for the same, a unique mechanism is essential. Numerous papers have reported multi-objective evolutionary algorithms to explain the absence of convergence and diversity variety in many-objective optimization problems. One of the most encouraging methodologies utilizes many reference points to segregate the solutions and guide the search procedure. The above-said methodology is integrated into the basic version of the Moth Flame Optimization (MFO) algorithm for the first time in this paper. The proposed Many-Objective Moth Flame Optimization (MaOMFO) utilizes a set of reference points progressively decided by the hunt procedure of the moth flame. It permits the calculation to combine with the Pareto front yet synchronize the decent variety of the Pareto front. MaOMFO is employed to solve a wide range of unconstrained and constrained benchmark functions and compared with other competitive algorithms, such as non-dominated sorting genetic algorithm, multi-objective evolutionary algorithm based on dominance and decomposition, and novel multi-objective particle swarm optimization using different performance metrics. The results demonstrate the superiority of the algorithm as a new many-objective algorithm for complex many-objective optimization problems