Continuous Genetic Algorithm Research Articles

Modeling of electric springs and their multi-objective voltage control based on continuous genetic algorithm for unbalanced distribution networks

This work proposes a multi-objective voltage control for multiple Electric Springs (ESs) in active unbalanced distribution systems, where the goal is to provide a suitable simulation scheme to control the ESs in real-time applications. This multi-objective voltage control relies on a robust ES model, a modified backward/forward solution method (BFSM), and a continuous genetic algorithm (CGA). The proposed ES model is suitable for unbalanced conditions and can be implemented at any network location in single-, two-, and three-phase connections. Also, it considers the self and mutual effects of the distribution line (DL) and the power flow of the elements connected at the point of common coupling (PCC) to determine the ES output voltage. The ES model is adapted to the BFSM to quickly and reliably solve the power flow in the distribution network. Besides, the CGA simultaneously solves the mode of operation and the amount of reactive power exchange of the ESs to minimize power losses, voltage unbalance, and voltage deviation. The proposed voltage control is evaluated on an active distribution network based on a modified IEEE 13-bus for considering both load variations and power intermittences.

Optimization of fuzzy similarity by genetic algorithm in user‐based collaborative filtering recommender systems

AbstractThe most important subjects in the memory‐based collaborative filtering recommender system (RS) are to accurately calculate the similarities between users and finally finding interesting recommendations for active users. The main purpose of this research is to provide a list of the best items for recommending in less time. The fuzzy‐genetic collaborative filtering (FGCF) approach recommends items by optimizing fuzzy similarities in the continuous genetic algorithm (CGA). In this method, first, the crisp values of user ratings are converted to fuzzy ratings, and then the fuzzy similarities are calculated. Similarity values are placed into the genes of the genetic algorithm, optimized, and finally, they are used in fuzzy prediction. Therefore, the fuzzy system is used twice in this process. Experimental results on RecSys, Movielens 100 K, and Movielens 1 M datasets show that FGCF improves the collaborative filtering RS performance in terms of quality and accuracy of recommendations, time and space complexities. The FGCF method is robust against the sparsity of data due to the correct choice of neighbours and avoids the users' different rating scales problem but it not able to solve the cold‐start challenge.

Optimal Investments in PV Sources for Grid-Connected Distribution Networks: An Application of the Discrete–Continuous Genetic Algorithm

The problem of the optimal siting and sizing of photovoltaic (PV) sources in grid connected distribution networks is addressed in this study with a master–slave optimization approach. In the master optimization stage, a discrete–continuous version of the Chu and Beasley genetic algorithm (DCCBGA) is employed, which defines the optimal locations and sizes for the PV sources. In the slave stage, the successive approximation method is used to evaluate the fitness function value for each individual provided by the master stage. The objective function simultaneously minimizes the energy purchasing costs in the substation bus, and the investment and operating costs for PV sources for a planning period of 20 years. The numerical results of the IEEE 33-bus and 69-bus systems demonstrate that with the proposed optimization methodology, it is possible to eliminate about 27% of the annual operation costs in both systems with optimal locations for the three PV sources. After 100 consecutive evaluations of the DCCBGA, it was observed that 44% of the solutions found by the IEEE 33-bus system were better than those found by the BONMIN solver in the General Algebraic Modeling System (GAMS optimization package). In the case of the IEEE 69-bus system, the DCCBGA ensured, with 55% probability, that solutions with better objective function values than the mean solution value of the GAMS were found. Power generation curves for the slack source confirmed that the optimal siting and sizing of PV sources create the duck curve for the power required to the main grid; in addition, the voltage profile curves for both systems show that voltage regulation was always maintained between ±10% in all the time periods under analysis. All the numerical validations were carried out in the MATLAB programming environment with the GAMS optimization package.

Developing adaptive neuro-fuzzy inference system-based models to predict the bending strength of polyurethane foam-cored sandwich panels

The aim of this paper was to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) and to predict the flexural strength of the sandwich panels made with thin medium density fiberboard as surface layers, and polyurethane foam as a core layer, by applying metaheuristic optimization methods. For this purpose, various models, namely ant colony optimization for the continuous domain (ACOR), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) were applied and compared, as different efficient bio-inspired paradigms, to assess their suitability for training the adaptive neuro-fuzzy inference system model. The predicted values of the flexural strength resulting from applying adaptive neuro-fuzzy inference system trained by ACOR, DE, GA, and PSO, were compared with the values derived from adaptive neuro-fuzzy inference system classical model. The molar ratio of formaldehyde to melamine and urea, sandwich panel thickness, and the weight ratio of the modified starch to MUF resin (OS/MUF weight ratio) were used as an input variables and the modulus of rupture was used as an output one. The developed hybrid models were used to predict the values of the modulus of rupture obtained from experimental tests. In order to evaluate and compare the performance of the models, three performance criteria were employed namely, determination coefficient (R2), root mean square error, and mean absolute percentage error. It was found that ANFIS–ACOR, ANFIS–DE, ANFIS–GA, and ANFIS–PSO showed different performance ratios compared to the predicting model. In addition, the ANFIS–GA model is found to be by far more accurate than the other hybrid models.

Nearest neighbors algorithm and genetic‐based collaborative filtering

AbstractConventional recommender systems often utilize similarity formulas to identify similarities between active users and others to predict the rating of the unseen items. Existing optimization algorithms seek to find the weights and coefficients affecting these similarities. Our proposed method, implemented in R in the GACFF package, shifts away from this view and directly uses the continuous genetic algorithm to find optimal similarities in big data (e.g., Movielens 1M and Netflix datasets) to improve the performance of user‐based collaborative filtering recommendation systems. First, by identifying the users who are the nearest neighbors along with their number, the number of genes in a chromosome is determined. Each gene represents the similarity between a neighboring user and an active user. This genetic algorithm is independent of the size of the data. Our method provides optimal solutions more quickly by estimating the starting points. Moreover, the genetic metric provides better results and recommendations than previous ones in terms of runtime and quality measures (i.e., mean absolute error, coverage, precision, and recall).

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

Intelligent geochemical exploration modeling using multiclass support vector machine and integration it with continuous genetic algorithm in Gonabad region, Khorasan Razavi, Iran

Intelligent geochemical exploration modeling using multiclass support vector machine and integration it with continuous genetic algorithm in Gonabad region, Khorasan Razavi, Iran

Modifying genetic algorithm by dynamic memory and solution reconstructing mechanism for selectivity control of chemical sensors

Signals acquired from non-specific chemical sensors contain significant amount of irrelevant information regarding the targeted characteristics of the experimental analytes. Feature selection process weeds out the redundant information and supplies a dimensionally reduced informative dataset for the system to achieve an improved performance. Considering the complex interaction among the features and also the large number of possible subsets, it is necessary to develop advanced strategies to handle the difficult feature selection problem. In this study, genetic algorithm (GA) was equipped with a dynamic memory, and an adaptive mechanism was developed to reconstruct potential chromosomes from the memory. The modified model was implemented on binary and continuous GAs, and they were applied for feature selection from a metal-oxide semiconductor gas sensor array to increase the sensors’ selectivity in classifying storage duration and type of rice crops. When there was no prior knowledge on the optimal (or required) number of features, different switch parameters were embedded in the algorithms to control the feature subset size and the selectivity of the sensors. It was observed that the system error was decreased by utilizing smaller subset of features rather than the whole responses, revealing that the signals were conveying great amount of noise. The modified GAs outperformed the conventional methods in reducing the response dimensionality as well as the system error. The incorporated switch parameters gave the decision maker the ability of balancing the number of features and the accuracy of the system. Finally, the developed algorithms could be applied as successful strategies to select optimum responses from sensor array signals.

Genetic algorithms for the design and optimization of horizontal axis wind turbine (HAWT) blades: A continuous approach or a binary one?

The continuous and the binary techniques are the main types of genetic algorithms that could be employed for an optimization problem. The study investigates the robustness and accuracy of each technique for the wind turbine blades design and optimization problem. For that purpose, the geometry of the blade was designed for the maximization of the output power, the desirable goal of the blade design. The design variables consist of the distribution of the chord and twist along the blade. Results indicate that the continuous genetic algorithm outperforms the binary one from the standpoint of the accuracy and also the computational time. However, the use of uniform crossover could improve the convergence rate of the binary genetic algorithm. Moreover, the sensitivity analysis of the applied genetic algorithms with respect to the population size and the mutation rate was performed to find the appropriate values for those parameters. The outcomes emphasized that adopting a small number of population size together with a large number of generations could speed up the convergence rate of the problem. Also approved in the study was the efficiency of the so-called “Superblade” operator which improved the convergence rate of the algorithm and resulted in the powerful blades.

A Simple and Effective Approach for Tackling the Permutation Flow Shop Scheduling Problem

In this research, a new approach for tackling the permutation flow shop scheduling problem (PFSSP) is proposed. This algorithm is based on the steps of the elitism continuous genetic algorithm improved by two strategies and used the largest rank value (LRV) rule to transform the continuous values into discrete ones for enabling of solving the combinatorial PFSSP. The first strategy is combining the arithmetic crossover with the uniform crossover to give the algorithm a high capability on exploitation in addition to reducing stuck into local minima. The second one is re-initializing an individual selected randomly from the population to increase the exploration for avoiding stuck into local minima. Afterward, those two strategies are combined with the proposed algorithm to produce an improved one known as the improved efficient genetic algorithm (IEGA). To increase the exploitation capability of the IEGA, it is hybridized a local search strategy in a version abbreviated as HIEGA. HIEGA and IEGA are validated on three common benchmarks and compared with a number of well-known robust evolutionary and meta-heuristic algorithms to check their efficacy. The experimental results show that HIEGA and IEGA are competitive with others for the datasets incorporated in the comparison, such as Carlier, Reeves, and Heller.

Optimal Design of Transmission Shafts Using a Vortex Search Algorithm

This paper analyzes the problem of optimally sizing a transmission shaft via the vortex search algorithm (VSA) optimizer. The objective function was to minimize the shaft weight through an adequate selection of the diameters of each section of the device, and the constraints were the physical conditions that should be met to design safe, fatigue-proof shafts. The solution and the mathematical model were validated using Autodesk Inventor. In addition, the performance of the VSA was compared to that of the continuous genetic algorithm . The numerical results show that the programmed model has the physical and methodological characteristics needed to produce a better output than conventional design techniques. Therefore, this model can be a powerful tool to solve nonlinear non-convex optimization problems such as the case investigated here.

Maritime Buoyage Inspection System Based on an Unmanned Aerial Vehicle and Active Disturbance Rejection Control

The unmanned aerial vehicle (UAV) is inexpensive and offers a fast response speed and robust flexibility; thus, it is a promising tool in the maritime buoyage inspection scenario, which involves monitoring and accessing a lateral mark system far away from the coast. However, two main problems can occur during inspection. The first is extreme weather conditions, resulting in a deviation between the inspection route and the design route. The second is that the buoyage beacons are visited only once. Therefore, this article proposes a buoyage inspection system consisting of a single UAV and random coastal buoys. The UAV automatically takes off from the depot, performs a self-check on the buoy beacons, and then returns to the depot. A cascade active disturbance rejection controller (ADRC) is designed to adjust the real-time trajectory of the UAV system. A feasible trajectory planning method is also designed based on the continuous Hopfield neural network (CHNN) and genetic algorithm (GA) to minimize the inspection distance. Extensive simulations are conducted to demonstrate the effectiveness of the proposed method.

Beam Selection Assisted UAV‐BS Deployment and Trajectory for Beamspace mmWave Systems

Exploiting unmanned aerial vehicles (UAVs) as base stations (UAV‐BS) can enhance capacity, coverage, and energy efficiency of wireless communication networks. To fully realize this potential, millimeter wave (mmWave) technology can be exploited with UAV‐BS to form mmWave UAV‐BS. The major difficulty of mmWave UAV‐BS, however, lies in the limited energy of UAV‐BS and the multiuser interference (MUI). Beam division multiple access with orthogonal beams can be employed to alleviate the MUI. Since each user has dominant beams around the line of sight direction, beam selection can reduce the power consumption of radio frequency chain. In this paper, we formulate the problem of maximizing the sum rate of all users by optimizing the beam selection for beamspace and UAV‐BS deployment in the mmWave UAV‐BS system. This nonconvex problem is solved in two steps. First, we propose a signal‐to‐interference plus noise ratio‐based greedy beam selection scheme to ensure that all the ground users in the given area can be served by the UAV‐BS, where a zero forcing precoding scheme is used to eliminate the MUI. Then, we utilize the continuous genetic algorithm to find the optimal UAV‐BS deployment and beam pattern to maximize the sum rate of all users. Moreover, considering the mobility of the UAV‐BS, the UAV‐BS trajectory and beam selection for beamspace are optimized in the mmWave UAV‐BS system. The simulation results demonstrate the effectiveness of the proposed design for the mmWave UAV‐BS system.

Modeling of Electric Springs and Their Multi-Objective Voltage Control Based on Continuous Genetic Algorithm for Unbalanced Distribution Networks

Modeling of Electric Springs and Their Multi-Objective Voltage Control Based on Continuous Genetic Algorithm for Unbalanced Distribution Networks

Continuous Genetic Algorithms in the Optimization of Logistic Networks: Applicability Assessment and Tuning

Globalization opens up new perspectives for handling goods distribution in logistic networks. However, establishing an efficient inventory policy is challenging by virtue of the analytical and computational complexity. In this study, the goods distribution process that was governed by the order-up-to policy, implemented in either a distributed or centralized way, was investigated in the logistic systems with complex interconnection topologies. Uncertain demand may be imposed at any node, not just at conveniently chosen contact points, with a lost-sales assumption that introduces a non-linearity into the node dynamics. In order to adjust the policy parameters, the continuous genetic algorithm (CGA) was applied, with the fitness function incorporating both the operational costs and customer satisfaction level. This study investigated how to select the parameters of the popular inventory management policy when operating in the non-trivial networked structures. Moreover, precise guidelines for the CGA tuning in the considered class of problems were provided and evaluated in extensive numerical experiments.

Design optimization of a high-temperature fin-and-tube heat exchanger manifold – A case study

High-temperature fin-and-tube heat exchangers are widely used in many industries such as petrochemical industry, automotive, energy, and many others. The major advantage of those heat exchanger is a large heat transfer area within a compact shape. However, it is necessary to ensure uniform distribution of velocity in all the tubes. Failing that, it leads to large differences in mean temperature in the tubes. Consequently, excessive thermal stress occurs, that may cause the heat exchanger to break down. The small volume of collectors of the heat exchangers implicates possibility of improper flow condition inside the tubes, causing an unsuitable inner distribution of thermal and mechanical loads. This case study presents a design optimization of high temperature fin and tube heat exchanger manifold. The modified design of heat exchanger manifold is proposed. To optimize the manifold shape, the Particle Swarm Optimization and Continuous Genetic Algorithms are used. The design consists of tube used for a typical manifold, welded to the wedge, that enlarges the volume of the fluid and improves the flow distribution to tubular space of heat exchanger. The ANSYS CFX based CFD simulations are performed in order to determine the flow distribution in the tubes of fin-and-tube heat exchanger. The structural analysis is performed with ANSYS to determine the occurring thermal stresses. The new design of heat exchanger manifolds allowed one to reduce the tube wall temperature from 185 °C to 134 °C and compressible stresses in the construction of heat exchanger by nearly five times (from 105 MPa to 23 MPa), compared to the traditional fin-and-tube heat exchanger manifold.

Optimized micro-hydro power plants layout design using messy genetic algorithms

Micro Hydro-Power Plants (MHPP) represent a powerful and effective solution to address the problem of energy poverty in rural remote areas, with the advantage of preserving the natural resources and minimizing the impact on the environment. Nevertheless, the lack of resources and qualified manpower usually constitutes a big obstacle to its adequate application, generally translating into sub-optimal generation systems with poor levels of efficiency. Therefore, the study and development of expert, simple and efficient strategies to assist the design of these installations is of especial relevance. This work proposes a design methodology based on a tailored messy evolutionary computational approach, with the objective of finding the most suitable layout of MHPP, considering several constraints derived from a minimal power supply requirement, the maximum flow usage, and the physical feasibility of the plant in accordance with the real terrain profile. This profile is built on the basis of a discrete topographic survey, by means of a shape-preserving interpolation, which permits the application of a continuous variable-length Messy Genetic Algorithm (MGA). The optimization problem is then formulated in both single-objective (cost minimization) and multi-objective (cost minimization and power supply maximization) modes, including the study of the Pareto dominance. The algorithm is applied to a real scenario in a remote community in Honduras, obtaining a 56.96% of cost reduction with respect to previous works.

An energy management system for optimal operation of BSS in DC distributed generation environments based on a parallel PSO algorithm

Abstract This paper proposes an energy management system (EMS) for the day-ahead dispatch of battery storage systems (BSS) under a distributed generation environment for direct current (DC) networks, with the main objective of reducing the cost of the energy purchased to the utility grid. This approach considers the state-of-charge (SOC) of the BSS and the production variation of the renewable generators, in particular of wind and photovoltaic technologies, and the variations in the power consumption and energy costs. The proposed EMS uses a master-slave strategy formed by a parallel implementation of the particle swarm optimizer (PPSO) and a multi-period power flow method based on successive approximations (SA), with the aim of achieving the optimal daily operation of the BSS. The objective function selected for the optimization was the reduction of the energy purchasing costs, also including the power balance, devices capabilities and voltage regulation. The effectiveness of the EMS is evaluated in a test system of 21 buses, comparing the solution quality and speed with three optimization techniques published in literature: a black hole optimizer, a continuous genetic algorithm with matrix structure, and a traditional Chu & Beasley genetic algorithm. In addition, two simulation scenarios were used to identify the optimal final SOC conditions for the BSS. Finally, the results show that the proposed EMS provides the best trade-off between quality solution and speed. The simulations are conducted in MATLAB software using sequential quadratic programming.

Optimal Network Reconfiguration to Reduce Power Loss Using an Initial Searching Point for Continuous Genetic Algorithm

In this paper, an effective method to determine an initial searching point (ISP) of the network reconfiguration (NR) problem for power loss reduction is proposed for improving the efficiency of the continuous genetic algorithm (CGA) to the NR problem. The idea of the method is to close each initial open switch in turn and solve power flow for the distribution system with the presence of a closed loop to choose a switch with the smallest current in the closed loop for opening. If the radial topology constraint of the distribution system is satisfied, the switch opened is considered as a control variable of the ISP. Then, ISP is attached to the initial population of CGA. The calculated results from the different distribution systems show that the proposed CGA using ISP could reach the optimal radial topology with better successful rate and obtained solution quality than the method based on CGA using the initial population generated randomly and the method based on CGA using the initial radial configuration attached to the initial population. As a result, CGA using ISP can be a favorable method for finding a more effective radial topology in operating distribution systems.

Secure Economic Emission Dispatch Using Multi-objective Continuous Genetic Algorithm Incorporating Reliability Indices

Secure Economic Emission Dispatch Using Multi-objective Continuous Genetic Algorithm Incorporating Reliability Indices