Continuous Genetic Algorithm Research Articles

Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making

In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article introduces a scenario network-based approach for managing cybersecurity projects, utilizing fuzzy linguistic models and a Takagi–Sugeno–Kanga fuzzy neural network. Drawing upon L. Zadeh’s theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. Fuzzy production rules are employed for decision-making, while the Mamdani fuzzy inference algorithm enhances interpretability. This approach enables multi-scenario planning and adaptability across multi-stage cybersecurity projects. Preliminary results from a research prototype of an intelligent expert system—designed to analyze project stages and adaptively construct project trajectories—suggest the proposed approach is effective. In computational experiments, the use of fuzzy procedures resulted in an over 25% reduction in errors compared to traditional methods, particularly in adjusting project scenarios from pessimistic to baseline projections. While promising, this approach requires further testing across diverse cybersecurity contexts. Future studies will aim to refine scenario adaptation and optimize system response in high-risk project environments.

Optimal integration of PV generators and D-STATCOMs into the electrical distribution system to reduce the annual investment and operational cost: A multiverse optimization algorithm and matrix power flow approach

In this work, we address the problem of optimally integrating photovoltaic distributed generators (PV) and distributed static compensators (D-STATCOMs) in distributed electrical systems (DES). Previous methods have struggled with the complexities of non-linear mathematical models and the integration of operating and investment costs while meeting the operational constraints of these devices. We propose a solution using a master-slave methodology that combines a discrete-continuous version of the multiverse optimization algorithm (MVO), with a matrix power flow method based on successive approximation (MAX). This approach aims to reduce annual costs by efficiently managing the grid’s operating costs and the investment and operational costs of PV and D-STATCOMs. Various researchers have suggested methodologies for solving the problem of optimal D-STATCOM integration in DES, focusing on minimizing investment and operating costs as the objective function. However, these studies often lack comparative methodologies and statistical analysis to validate the performance of their proposed approaches. Additionally, they do not consider the impact of variable power demand. We compared our methodology with other master-slave approaches that utilize different optimization algorithms, including the vortex search algorithm (VSA), crow search algorithm (CSA), a continuous genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). These comparisons were made using two test systems with 33 and 69 nodes, which incorporate variations in power demand and PV production from a region in Colombia. Our statistical analysis included evaluations of the best and average solutions, standard deviations, differences between the best and average solutions, and a dispersion analysis using box-and-whisker plots. The results demonstrate that MVO outperforms other methods, providing the best results with reduced processing times in both test systems.

Enhancing DC distribution network efficiency through optimal power coordination in lithium-ion batteries: A sparse nonlinear optimization approach

This work deals with the problem regarding the optimal operation of lithium-ion batteries to improve the economic, technical, and environmental indices of standalone and grid-connected direct current (DC) distribution networks.To this effect, a general nonlinear programming model has been developed, which considers three objective functions: (i) the daily energy purchasing costs at the terminals of the substation, considering the maintenance costs of renewable generation (photovoltaic) and energy storage technologies (batteries); (ii) the daily energy losses associated with energy transport; and (iii) the CO2 emissions per day of operation, associated with conventional generators. This is done by integrating all the operations and technical constraints of the DC network and the devices it comprises. The sparse nonlinear optimization (SNOPT) solution method is employed, comparing its results against those of three recently reported metaheuristic optimization methodologies: the continuous genetic algorithm and the parallel versions of the particle swarm optimizer and the vortex search algorithm. The performance of the proposed methodology is validated on standalone and grid-connected networks, considering the technical and economic conditions of two regions of Colombia (rural and urban territories) and evaluating its effectiveness in terms of solution quality, standard deviation, and processing times. The results show that the SNOPT tool of the General Algebraic Modeling System (GAMS) achieves the global optimum in all test scenarios, exhibiting a standard deviation of zero and requiring less than three seconds on average to generate a solution for an entire day of operation.

An impedance matching section based on composite right/left‐handed transmission line model in viewpoint of phase shift and noise figure

AbstractIn this paper, a genetic‐based synthesis approach for an impedance matching section based on a composite right‐/left‐handed (CRLH) transmission line model called CRLH transmission line impedance matching section (CRLHTLIMS) in viewpoint of transmission phase shift and noise figure is newly presented. To find the unknown values of right‐/left‐handed inductances and capacitances of a CRLHTLIMS, a type of global optimization algorithm called continuous parameter genetic algorithm is used. The length, transmission phase shift, and noise figure of the CRLHTLIMS are computed by using simple equations. Different examples for a uniform CRLHTLIMS are given. According to the results, this approach is an easy and effective way for the synthesis of transmission line impedance matching sections in viewpoint of transmission phase shift and noise figure.

A multi-objective optimization framework for functional arrangement in smart floating cities

Before the terms “smart city” and “floating city” were introduced, the world's population had increased and land shortage across the world was already widely recognized. As a first challenge, the previous studies have developed the concept of a smart city as a creative answer, following that, several scientists proposed the floating city concept in the literature as a solution to the increased sea levels. Moreover, engineers, architects, and designers deal with city planning, for smart and floating settlements as a difficult design challenge, and evolutionary algorithms could be employed to address this complex problem by optimizing residents' needs. As a continuation of our previous studies on this topic, this time, we develop a multi-objective continuous genetic algorithm with differential evolution (DE) mutation strategy (MO_CGADE) and a multi-objective ensemble differential evolution algorithm (MO_EDE) to solve the problem on hand. Then, we compare the performance of the MO_CGADE and MO_EDE algorithms with the non-dominated sorting genetic algorithm (NSGAII) to maximize two conflicted objective functions, namely, scenery, and walkability in the proposed smart floating city model created in the Grasshopper Algorithmic Modeling Environment. The parametric model that we create in the Grasshopper software includes 64 decision variables, area constraints and objective functions to be optimized by MO_CGADE, MO_EDE, and NSGAII algorithms. Computational results show that MO_CGADE and MO_EDE algorithms generate better Pareto ranking results than the traditional NSGAII algorithm in terms of cardinality, distribution spacing, and coverage metrics.

Analysis of Rateless Multiple Access Scheme With Maximum Likelihood Decoding in an AWGN Channel

The rateless multiple access (RMA) scheme is a promising distributed multiple access scheme to achieve simultaneous high reliability, low latency and massive connectivity. In this paper, we investigate the maximum likelihood (ML) decoding performance of the RMA scheme in an Additive white Gaussian noise (AWGN) channel with binary phase-shift keying (BPSK) modulation. For the first time, this paper derives the ensemble weight distribution of the RMA scheme. We derive an upper bound on the decoding error performance of the RMA scheme under ML decoding in an AWGN channel with BPSK modulation. Using the derived bound as the fitness function, we adopt the continuous genetic algorithm to optimize the parameters of the RMA scheme. Simulation results show the tightness of the derived bound and the superiority of the optimized degree distribution over the conventional degree distributions.

Rapid quantification of the surface overflow and underground infiltration in sewer pipes based on computer vision and continuous optimization

The overloading of the sewer network caused by unwarranted infiltration of stormwater may lead to waterlogging and environmental pollution. The accurate identification of infiltration and surface overflow is essential to predict and reduce these risks. To retrieve the limitations of infiltration estimation and the failure of surface overflow perception using the common stormwater management model (SWMM), a surface overflow and underground infiltration (SOUI) model is proposed to estimate the infiltration and overflow. First, the precipitation, water level of the manhole, surface water depth and images of the overflowing point, and volume at the outfall are collected. Then, the surface waterlogging area is identified based on computer vision to reconstruct the local digital elevation model (DEM) by spatial interpolation, and the relationship between the waterlogging depth, area and volume is established to identify the real-time overflow. Next, a continuous genetic algorithm optimization (CT-GA) model is proposed for the underground sewer system to determine the inflow rapidly. Finally, surface and underground flow estimations are combined to perceive the state of the urban sewer network accurately. The results show that, compared with the common SWMM simulation, the accuracy of the water level simulation is improved by 43.5% during the rainfall period, and the time cost of the computational optimization is reduced by 67.5%. The proposed method can effectively diagnose the operation state and overflow risk of the sewer networks in real time during rainfall seasons.

Damage Detection and Assessment of Stiffness and Mass Matrices in Curved Simply Supported Beam Using Genetic Algorithm

In this study, a genetic algorithm (GA) is used to detect damage in curved beam model, stiffness as well as mass matrices of the curved beam elements is formulated using Hamilton's principle. Each node of the curved beam element possesses seven degrees of freedom including the warping degree of freedom. The curved beam element had been derived based on the Kang and Yoo’s thin-walled curved beam theory. The identification of damage is formulated as an optimization problem, binary and continuous genetic algorithms(BGA, CGA) are used to detect and locate the damage using two objective functions (change in natural frequencies, Modal Assurance Criterion MAC). The results show the objective function based on change in natural frequency is the best objective and no error was recorded in prediction of location and small error in detecting damage value. Also the result show that the genetic algorithm method are efficient indicating and quantifying single and multiple damage with high precision, and the prediction error for the CGA are less than corresponding value for the BGA.

Outage Probability Minimization in Secure NOMA Cognitive Radio Systems With UAV Relay: A Machine Learning Approach

This paper considers a multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) cognitive radio (CR) system with an unmanned aerial vehicle relay (UR). In this system, a secondary transmitter (ST) uses licensed spectrum from the primary network to transmit signals to its secondary receivers (SRs) based on NOMA. The UR is used as a relay to forward the signals from the ST to the SRs. As a result, the system can achieve significant improvements in spectral efficiency and network capacity. However, such a MIMO NOMA CR system faces issues of interference and security, i.e., eavesdropping attacks, due to the shared spectrum use and the UR. Therefore, we aim to minimize the outage probability of the secondary network, subject to constraints on the outage probability of the primary network and the intercept probabilities of eavesdroppers. Then, we attempt to optimize the transmit power of the UR, the coordinates of the UR, and the power allocation factors for NOMA. We further derive closed-form expressions for the outage probabilities of the secondary and primary networks and the intercept probabilities at the eavesdroppers. We propose using a machine learning algorithm based on a constrained continuous genetic algorithm to solve the optimization problem.

Continuous genetic algorithm for grasping an object of a priori unknown shape by a robotic manipulator

Objectives . The problem of providing the interaction of a robotic manipulator with a priori unknown objects in a given workspace is of great interest both to the research community and many industries. By developing a solution to this problem, it will be possible to reduce the time taken for robots to adapt to new environments and objects therein. One of the primary stages of providing the interaction of the robotic manipulator with objects is the search for the target position of the robot gripper based on the onboard sensor subsystem, which can be carried out by a number of methods. Methods associated with machine learning and self-learning technologies may not be suitable for some applications (for example, during rescue operations) when it is necessary to quickly search for the target position of the gripper for an a priori unknown object, about which there is no relevant information in the robot database. Therefore, for this problem, heuristic approaches – for example, genetic algorithms – seem to be applicable. The objectives of this work are to implement a search based on a continuous genetic algorithm for the target position of the robot gripper including collision avoidance and study its performance under virtual simulation. Methods . A heuristic search algorithm (continuous genetic algorithm) is used. The complex scene analysis algorithm uses classical image processing methods. In order to evaluate the effectiveness of the algorithm, virtual simulation is used. Results . The possibility of using a continuous genetic algorithm is analyzed in the problem of grasping an object of an a priori unknown shape avoiding collisions with other objects of a static scene. A complex scene analysis algorithm and implementation of a continuous genetic algorithm are presented for finding the target position of the gripper of a Kuka LBR iiwa 7 R800 robotic control system with redundant kinematics. The results of an experimental virtual simulation of the obtained algorithm are presented. Conclusions . The conducted research demonstrates the effectiveness of the continuous genetic algorithm in obtaining the target position of the gripper of the robotic manipulator under conditions when the static scene represents randomly located objects of various shapes.

Throughput Optimization for Noma Energy Harvesting Cognitive Radio With Multi-UAV-Assisted Relaying Under Security Constraints

This paper investigates the throughput of a non-orthogonal multiple access (NOMA)-based cognitive radio (CR) system with multiple unmanned aerial vehicle (UAV)-assisted relays under system performance and security constraints. We propose a communication protocol that includes an energy harvesting (EH) phase and multiple communication phases. In the EH phase, the multiple UAV relays (URs) harvest energy from a power beacon. In the first communication phase, a secondary transmitter (ST) uses the collected energy to send confidential signals to the first UR using NOMA. Simultaneously, a ground base station communicates with a primary receiver (PR) under interference from the ST. In the subsequent communication phases, the next URs apply the decode-and-forward technique to transmit the signals. In the last communication phase, the Internet of Things destinations (IDs) receive their signals in the presence of an eavesdropper (EAV). Accordingly, the outage probability of the primary network, the throughput of the secondary network, and the leakage probability at the EAV are analyzed. On this basis, we propose a hybrid search method combining particle swarm optimization (PSO) and continuous genetic algorithm (CGA) to optimize the UR configurations and the NOMA power allocation to maximize the throughput of the secondary network under performance and security constraints.

Optimal Power Dispatch of PV Generators in AC Distribution Networks by Considering Solar, Environmental, and Power Demand Conditions from Colombia

This paper deals with the problem regarding the optimal operation of photovoltaic (PV) generation sources in AC distribution networks with a single-phase structure, taking into consideration different objective functions. The problem is formulated as a multi-period optimal power flow applied to AC distribution grids, which generates a nonlinear programming (NLP) model with a non-convex structure. Three different objective functions are considered in the optimization model, each optimized using a single-objective function approach. These objective functions are (i) an operating costs function composed of the energy purchasing costs at the substation bus, added with the PV maintenance costs; (ii) the costs of energy losses; and (iii) the total CO2 emissions at the substation bus. All these functions are minimized while considering a frame of operation of 24 h, i.e., in a day-ahead operation environment. To solve the NLP model representing the studied problem, the General Algebraic Modeling System (GAMS) and its SNOPT solver are used. Two different test feeders are used for all the numerical validations, one of them adapted to the urban operation characteristics in the Metropolitan Area of Medellín, which is composed of 33 nodes, and the other one adapted to isolated rural operating conditions, which has 27 nodes and is located in the department of Chocó, Colombia (municipality of Capurganá). Numerical comparisons with multiple combinatorial optimization methods (particle swarm optimization, the continuous genetic algorithm, the Vortex Search algorithm, and the Ant Lion Optimizer) demonstrate the effectiveness of the GAMS software to reach the optimal day-ahead dispatch of all the PV sources in both distribution grids.

Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints

Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance.

Mineral exploration modeling by convolutional neural network and continuous genetic algorithm: a case study in Khorasan Razavi, Iran

Mineral exploration modeling by convolutional neural network and continuous genetic algorithm: a case study in Khorasan Razavi, Iran

Optimal Power Dispatch of DGs in Radial and Mesh AC Grids: A Hybrid Solution Methodology between the Salps Swarm Algorithm and Successive Approximation Power Flow Method

In this paper, we address the problem of the optimal power dispatch of Distributed Generators (DGs) in Alternating Current (AC) networks, better known as the Optimal Power Flow (OPF) problem. We used, as the objective function, the minimization of power losses (Ploss) associated with energy transport, which are subject to the set of constraints that compose AC networks in an environment of distributed generation. To validate the effectiveness of the proposed methodology in solving the OPF problem in any network topology, we employed one 10-node mesh test system and three radial text systems: 10, 33, and 69 nodes. In each test system, DGs were allowed to inject 20%, 40%, and 60% of the power supplied by the slack generator in the base case. To solve the OPF problem, we used a master–slave methodology that integrates the optimization method Salps Swarm Algorithm (SSA) and the load flow technique based on the Successive Approximation (SA) method. Moreover, for comparison purposes, we employed some of the algorithms reported in the specialized literature to solve the OPF problem (the continuous genetic algorithm, the particle swarm optimization algorithm, the black hole algorithm, the antlion optimization algorithm, and the Multi-Verse Optimizer algorithm), which were selected because of their excellent results in solving such problems. The results obtained by the proposed solution methodology demonstrate its superiority and convergence capacity in terms of minimization of Ploss in both radial and mesh systems. It provided the best reduction in minimum Ploss in short processing times and showed excellent repeatability in each test system and scenario under analysis.

Application of the arithmetic optimization algorithm to solve the optimal power flow problem in direct current networks

This article presents a methodology to solve to the Optimal Power Flow (OPF) problem in Direct Current (DC) networks using the Arithmetic Optimization Algorithm (AOA) and Successive Approximation (SA). This master-slave methodology solves the OPF problem in two stages: the master stage estimates the solution to the OPF problem considering its constraints and variables, and the slave stage assesses the fitness of the solution proposed by the master stage. To validate the methodology suggested in this article, three test systems cited multiple times in the literature were used: the 10, 21 and the 69 nodes test systems. In addition, three scenarios varying the allowable power limits for the Distributed Generators (DGs) are presented; thus, the methodology explores solutions under different conditions. To prove its efficiency and robustness, the solution was compared with four other methods reported in the literature: Ant Lion Optimization (ALO), Black Hole Optimization (BHO), the Continuous Genetic Algorithm (CGA), and Particle Swarm Optimization (PSO). The results show that the methodology proposed here to reduce power losses presents the best solution in terms of standard deviation.

Multi-objective optimization of a sandwich structure with a hybrid composite grid core

This study evaluated two multi-objective optimization problems of a hybrid composite grid core sandwich structure, aiming at maximizing the critical buckling load while minimizing the structural weight or the costs of raw materials. The sandwich structure had a core with a composite isogrid pattern covered by two composite skins, and design variables included the space between core ribs, the core rotation angle, and the dimensions and materials of ribs. The stiffeners of the core were considered with I and box cross sections to investigate the cross section effect of ribs. The results showed that materials with high density and low mechanical properties are not suitable for reducing cost and weight. Geometrically, the use of cross-section and box has a significant effect on the buckling load capacity of the structure. Although two different criteria have been used to design the structure, the end results are almost the same. Moreover, the First-order Shear Deformation Plate Theory and the Ritz method were utilized to obtain the buckling load. Finally, the optimal alternative was found among the existing ones using the continuous genetic algorithm approach.

Optimal Allocation of Directional Relay for Efficient Energy Optimization in a Radial Distribution System

The optimal allocation of protective devices is a serious issue in an electrical power system; in order to reduce the possibility of faults, the protection devices should be optimally placed. The paper presents a continuous genetic algorithm (CGA) for the optimal allocation of directional relays for the efficient energy minimization in a radial distribution system (DG). The algorithm is flexible to use for the changes and improvements in the optimal location for a DG unit and can optimize the energy consumption in the radial distribution system. The proposed algorithm has been implemented on IEEE 33 and 69-bus system using MATLAB (R2014b, MathWorks). Low energy consumption is a common design objective in an energy-constrained distribution system. Engineers, power utilities, and network operators can profit from the proposed methodology to enhance the use of DG in distribution networks.

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

In this paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master–slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks based on successive approximation (slave stage). The master stage determines the level of active power to be injected by each distributed generator in the network, and the slave stage evaluates the impact of the proposed solution on each distributed generator in terms of the objective function and the constraints. For the simulations, we used the 10-, 33-, and 69-node radial test systems and the 10-node mesh test system with three levels of distributed generation penetration: 20%, 40%, and 60% of the power provided by the slack generator in a scenario without DGs. In order to validate the robustness and convergence of the proposed optimization algorithm, we compared it with four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: Particle Swarm Optimization, the Continuous Genetic Algorithm, the Black Hole Optimization algorithm, and the Ant Lion Optimization algorithm. The results obtained demonstrate that the proposed master–slave methodology can find the best solution (in terms of power loss reduction, repeatability, and technical conditions) for networks of any size while offering excellent performance in terms of computation time.

Estimation of the parameters of the mathematical model of an equivalent diode of a photovoltaic panel using a continuous genetic algorithm

This document presents the implementation of a con- tinuous population genetic optimization algorithm (CGA) as a solution method to the parameter estimation problem of a diode model (SDM) of a photovoltaic panel (PV) from experimental data of voltage versus current (V-I). The parameters to be estimated by means of the CGA are: the photoinduced current, the diode saturation current, the ideality factor, the series resistance and the parallel resistance. The estimation of the SDM parameters is carried out in order to obtain the real values that represent the power profile of the panel and thus carry out an analysis of its physical state. For which, the mean square error of the PV current estimated by the solution method from the selected parameters is used as the objective function, with the real curve of the PV panel used as the test scenario. All of the above subject to the set of restrictions that limits the problem under analysis. To validate the effectiveness and robustness of the proposed method, in this document two comparison methods have been used: the particle swarm optimization method (PSO) and a traditional genetic algorithm (GAT). In addition, four different panel types were used to generate the test scenarios: the MSX60, the SOLAR SJ65, the KYOCERA KC200GT, and the STP245S. All simulations were obtained using MATLAB 2019b. The results obtained in this document show that the proposed method presents the best relationship between the estimation of parameters and the computation time required to solve the SDM problem.