Binary-coded Genetic Algorithm Research Articles

Evaluation of combinatorial algorithms for optimizing highly nonlinear structural problems

Optimizing highly nonlinear structural problems can be very challenging due to the large number of parameters. Classical compliance minimization does not work for such problems. Common optimization algorithms also do not find good solutions. This work evaluates both commonly used optimization algorithms and algorithms not yet used in topology optimization. The algorithms are evaluated using a simple nonlinear problem: minimizing the end displacement of a cantilever beam fixed on one side and loaded by gravity. The global optimum for a coarse mesh grid is computed by simulating nearly 60 million possible topology designs using a Brute-Force search. We use this benchmark to evaluate the computational cost and objective values of known and newly developed optimization methods. The known methods are binary-coded Genetic Algorithm, Simulated Annealing, and Free Shape Optimization. The Reduced Variable Neighborhood Search (RVNS) has not yet been applied to topology optimization. We provide two implementations of RVNS: Breadth-First Search with a limited search depth (BFSL) and with an optional restriction for the size of the simultaneously modified area (TBFSL). According to the benchmark, TBFSL is the most efficient approach. For the optimization on a finer mesh grid, TBFSL is combined with a multi-grid approach to further increase efficiency.

Breast Cancer Mass Classification Using Machine Learning, Binary-Coded Genetic Algorithms and an Ensemble of Deep Transfer Learning

Abstract The diagnosis of breast cancer (BC) as early as possible is crucial for increasing the survival rate. Mammography enables finding the breast tissue changes years before they could develop into cancer symptoms. In this study, machine learning methods for BC mass pathology classification have been investigated using the radiologists’ mass annotations on the screen-film mammograms of the Breast Cancer Digital Repository (BCDR). The performances of precomputed features in the BCDR and discrete wavelet transform followed by Radon transform have been investigated by using four sequential feature selections and three genetic algorithms. Feature fusion from craniocaudal and mediolateral oblique views was shown to increase the performance of the classifier. Mass classification has been implemented by deep transfer learning (DTL) using the weights of ResNet50, NASNetLarge and Xception networks. An ensemble of DTL (EDTL) was shown to have higher classification performance than the DTL models. The proposed EDTL has area under the receiver operating curve (AUC) scores of 0.8843 and 0.9089 for mass classification on the region of interest (ROI) and ROI union datasets, respectively. The proposed EDTL has the highest BC mass classification AUC score on the BCDR to date and may be useful for other datasets.

Synthesis of Quadband mm-Wave Microstrip Antenna Using Genetic Algorithm for Wireless Application

Antennas with multifunctional capabilities integrated into a single device that demonstrates a high performance are in demand, and microstrip antennas with quadband coverage are very useful for a wide range of mm-wave applications. Antennas and propagation at mm-wave frequencies, on the other hand, poses several challenges which can be overcome by applying performance enhancement techniques to meet design objectives. This article presents the use of a binary-coded genetic algorithm for developing an improved quadband mm-wave microstrip patch antenna. The patch shape was optimized by dividing a conducting surface into 6 × 6 tiny rectangular blocks. The algorithm generated the solution space by introducing conducting and nonconducting features for each radiating cell on the patch surface and then greedily searched for the best-fitted individual based on the cost function. With the combination of High-Frequency Structure Simulator (HFSS) and MATLAB, candidate antennas were iteratively modeled by applying the suggested algorithm. The optimized antenna resonated at four frequencies centered at 28.3 GHz, 38.1 GHz, 46.6 GHz, and 60.0 GHz. The antenna realized a peak broadside directivity of 7.8 dB, 8.8 dB, 7.3 dB, and 7.1 dB, respectively, with a total operating bandwidth of 11.5 GHz. The research findings were compared with related works presented in the literature and found that the optimized antenna outperformed them in terms of bandwidth, directivity, and efficiency.

Joint path planning and scheduling for vehicle-assisted multiple Unmanned Aerial Systems plant protection operation

Unmanned Aircraft Systems (UAS) for plant protection have been playing increasingly important roles in agricultural field management. Nevertheless, constrained by pesticide loading capacity and battery endurance limit, the operation area of UAS or UAS swarm is quite limited, prohibiting them from serving large-scale applications. Previous works in the UAS or UAS swarm operation never considered vehicle scheduling, thus unable to cover multiple segmented agricultural fields distributed large-scale. Therefore, we propose a joint path planning and scheduling for vehicle-assisted multiple plant protection Unmanned Aerial Systems (UASs) operation. With the help of vehicle, UASs are able to spray pesticide on numerous fields distributed large-scale. Vehicle stop selection, the coordination between vehicle scheduling and UASs tasks assignment along with path planning could be determined and optimized. The test results show that the overall efficiency and the vehicle stop selection are greatly affected by the UAS number and loading capacity. Vehicle-assisted multi-UASs total operation time and non-spraying flight distance could be saved, with optimized vehicle stops utilizing a binary-coded genetic algorithm (GA). The total operation time difference could be minimal in certain cases when deploying different UAS swarms with the same UAS number but different loading capacity. A significant reduction in total operation time and UASs non-spraying flight distance could be achieved by using the proposed method. Compared with other methods, the total operation time could be reduced by 2 h when controlling 2 or 3 UASs; moreover, a sharp decrease (20–30%) in UAS swarm non-spraying flight distance wastage could be attained.

Multi-Band mm-Wave Wearable Antenna Synthesized with a Genetic Algorithm

This paper presents the design of a novel fabric-based multi-band microstrip antenna in mm-wave frequencies for wearable applications. The reference patch antenna was etched on a flexible polytetrafluoroethylene (PTFE) fabric substrate with an overall dimension of 18 mm × 18 mm × 0.6 mm and optimized the patch geometry using a binary-coded genetic algorithm. The algorithm iteratively creates a new shape of the path surface, evaluates the cost function, and returns the best-fitted geometry based on the formulated fitness function. The free space and on-body simulation of the best-fitted antenna performance parameter was investigated and analyzed. In free space, the proposed antenna is resonant at five distinct frequencies: 27.8 GHz, 30.3 GHz, 40.1 GHz, 47.2 GHz, and 56.7 GHz. The antenna achieves a wide bandwidth of 0.69, 2.32, 2.22, 1.76, and 8.11 GHz and an improved broadside directivity of 10.3, 8.5, 7.8, 9.6, and 8.9 dB in free space, respectively. For on-body analysis, the antenna was simulated using a three-layer human body phantom model at three distinct distances. The gain and radiation efficiency were significantly reduced when the antenna was close to the phantom model and gradually enhanced as the gap increased. Moreover, the antenna performances were evaluated and compared by using four additional fabric substrates. Because of its excellent on-body performance with flexible textile-based substrates, the optimized antenna is a suitable candidate for multi-band body-centric communications.

Work alone then meet: Cognitive attributes and team interaction in engineering design using evolutionary algorithms

Engineering products are becoming increasingly more complex and require skills from multiple disciplines to design, something hardly realizable by a single individual. As a result, most organizations form design teams, assembling which, is a challenging task. This work presents an agent-based model along with a framework for investigating team dynamics as the designers work on a representative engineering design problem. The model simulates evolutionary systems wherein, designs are generated, selected, and improved upon, as in biological evolution. A binary-coded genetic algorithm evolves the designs under the influence of the designers’ cognitive attributes, both individually and in a team setting. The results show that product complexity generally decreases the quality of designs, while increasing the number of team members initially increases team performance. However, as the number of team members increases further, their performance levels out and finally declines. An interesting find is that the longer the designers work individually on a design before a team meeting, the better the overall performance. Furthermore, increasing the number of candidate solutions brought to the team meeting initially increases team performance followed by a decrease, indicating that there is an optimal number. The paper concludes with proposed directions that future work can take.

Intelligent reactive power control of a renewable integrated hybrid energy system model using static synchronous compensators and soft computing techniques

Modern power system faces a severe problem of instability, largely due to inconsistent reactive power. It causes damage to the power grid within a few milliseconds. Therefore, proper management of reactive power under disturbing situations plays a key role in its safe operation. Devices such as flexible alternating current transmission systems (FACTS) accurately manage the system’s reactive power in accordance with the load demand. In this study, a new reactive power control strategy is employed for optimization of the reactive power along with the stability improvement of the system under different small perturbed conditions. Therefore, this study focuses on controlling the reactive power for an isolated wind-diesel hybrid power system model (WDHPSM) with the aid of a static synchronous compensator (STATCOM) together with the use of an integral minus proportional derivative (IPD) controller keeping a derivative-based filter (IPDF) as a secondary controller for better utilization of its purpose. The obtained results are compared when the no-control strategy is applied in the model. Another comparison has been done between the multiple applied soft computing techniques (oppositional harmonic search, ant lion optimization, binary-coded genetic algorithm, and symbiosis organisms search) that optimize the parameters of the controller of WDHPSM.

A multi-stage joint planning and operation model for energy hubs considering integrated demand response programs

Energy hub systems improve energy efficiency and reduce emissions due to the coordinated operation of different infrastructures. Given that these systems meet the needs of customers for different energies, their optimal design and operation is one of the main challenges in the field of energy supply. Hence, this paper presents a two-stage stochastic model for the integrated design and operation of an energy hub in the presence of electrical and thermal energy storage systems. As the electrical, heating, and cooling loads, besides the wind turbine’s (WT’s) output power, are associated with severe uncertainties, their impacts are addressed in the proposed model. Besides, demand response (DR) and integrated demand response (IDR) programs have been incorporated in the model. Furthermore, the real-coded genetic algorithm (RCGA), and binary-coded genetic algorithm (BCGA) are deployed to tackle the problem through continuous and discrete methods, respectively. The simulation results show that considering the uncertainties leads to the installation of larger capacities for assets and thus a 8.07% increase in investment cost. The results also indicate that the implementation of shiftable IDR program modifies the demand curve of electrical, cooling and heating loads, thereby reducing operating cost by 15.1%. Finally, the results substantiate that storage systems with discharge during peak hours not only increase system flexibility but also reduce operating cost.

Prediction and optimization of geometrical quality for pulsed laser cutting of non-oriented electrical steel sheet

Prediction and optimization of geometrical qualities, namely roundness of circular cut and kerf width of square cut, for pulsed laser cutting of non-oriented electrical steel are performed using deep neural network (DNN) and genetic algorithm (GA). Analyses using random forest method and response surface method show that laser power, pulse frequency, and cutting speed significantly affect the cutting qualities and are properly used as input variables in the prediction models. A real-coded GA is employed to determine the optimal DNN architecture, and the final DNN models are obtained through pre-training and fine-tuning processes. A binary-coded GA is utilized to determine the optimal combination of process parameters for generating the optimum geometrical qualities. The developed DNN-GA models show great ability in prediction of the roundness and the kerf width, as demonstrated by a very low mean absolute percentage error (≤2.60%) and a very high absolute fraction of variation (≥0.9972) for training, validation, and testing datasets. In addition, the performance of the DNN-GA models is evaluated by means of nine statistical criteria in comparison with other artificial intelligence based models, namely random vector functional link network (RVFL) and support vector machine for regression (SVR) integrated with equilibrium optimizer (EO) and grey wolf optimizer (GWO). The results indicate that the performance of the DNN-GA models is better than that of the RVFL-EO, SVR-EO, RVFL-GWO, and SVR-GWO models. The predicted optimal geometrical qualities of the DNN-GA models are verified by validation experiments in which a combination of the smallest roundness and kerf width is generated.

Genetic Algorithm-Based Cumulative Sum Method for Jamming Attack Detection of Cyber-Physical Power Systems

Jamming attack is one of the commonly malicious attacks in cyber-physical power systems (CPPSs) to intervene measurements and systems. Timely detection of jamming attacks is crucial for real-time monitoring and security of CPPSs. With this goal, this paper proposes genetic algorithm-based cumulative sum methods for online detection of jamming attacks in both centralized and distributed CPPSs. The proposed detectors are robust to time-varying jamming magnitudes and time-varying attacked locations. The time-varying attack magnitudes are estimated by a maximum likelihood estimation. The attacked locations are determined by a binary-coded genetic algorithm, where the locations are encoded into a binary string. Moreover, in the distributed setting, we propose two parallel information filters to estimate states in the case of jamming attacks. One distributed and four centralized IEEE bus systems are used to testify the proposed detectors. The numerical results show the effectiveness of the proposed detectors for detection jamming attacks in CPPSs.

Intelligent Scheduling of Smart Home Appliances Based on Demand Response Considering the Cost and Peak-to-Average Ratio in Residential Homes

With recent developments, smart grids assured for residential customers the opportunity to schedule smart home appliances’ operation times to simultaneously reduce both the electricity bill and the PAR based on demand response, as well as increasing user comfort. It is clear that the multi-objective combinatorial optimization problem involves constraints and the consumer’s preferences, and the solution to the problem is a difficult task. There have been a limited number of investigations carried out so far to solve the indicated problems using metaheuristic techniques like particle swarm optimization, mixed-integer linear programming, and the grey wolf and crow search optimization algorithms, etc. Due to the on/off control of smart home appliances, binary-coded genetic algorithms seem to be a well-fitted approach to obtain an optimal solution. It can be said that the novelty of this work is to represent the on/off state of the smart home appliance with a binary string which undergoes crossover and mutation operations during the genetic process. Because special binary numbers represent interruptible and uninterruptible smart home appliances, new types of crossover and mutation were developed to find the most convenient solutions to the problem. Although there are a few works which were carried out using the genetic algorithms, the proposed approach is rather distinct from those employed in their work. The designed genetic software runs at least ten times, and the most fitting result is taken as the optimal solution to the indicated problem; in order to ensure the optimal result, the fitness against the generation is plotted in each run, whether it is converged or not. The simulation results are significantly encouraging and meaningful to residential customers and utilities for the achievement of the goal, and they are feasible for a wide-range applications of home energy management systems.

Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

With the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

Modeling and solving the last-shift period train scheduling problem in subway networks

Train arrivals and departures should be scheduled over a certain period when talking about train timetabling problems. For the midnight train operations, passengers significantly concern about the network transfer issue. Currently, some existing studies address the last train timetabling problem by only optimizing the timetable for the last train on a single subway line whereas this study takes into consideration the complete last-shift period. We first put forward a last-shift train scheduling model aiming to minimize the transfer waiting time and maximize the network connectivity. Two genetic-based algorithms, an integer-coded genetic algorithm (ICGA) and a binary-coded genetic algorithm (BCGA) are developed. The relevance and applicability of the algorithms have been demonstrated by several testing networks and real-world implementation. The ICGA and the branch-and-bound approaches show high efficiency in obtaining the optimal solutions for a small network, while the BCGA approach that bases on an integer-programming model shows low efficiency in addressing problems of sparse solution spaces. However, the branch-and-bound approach has limited ability in solving medium-sized networks. On the contrary, the ICGA generates satisfactory results in solution quality and computational efficiency when applied to large-sized networks.

An extensive study of statistical, rough, and hybridized rough computing in bankruptcy prediction

An extensive amount of data are generated from the electronic world each day. Possessing useful knowledge from this data is challenging, and it became a prime area of current research. Much research has been carried out in these directions initiating from statistical techniques to intelligent computing and further to hybridized computing. The foremost objective of this article is making a comparative study between statistical, rough computing, and hybridized computing approaches. Financial bankruptcy dataset of Polish companies is considered for comparative analysis. Results show that rough hybridization of the binary-coded genetic algorithm provides an accuracy of 98.3% and it is better as compared to other descriptive and rough computing techniques.

Adaptive Genetic Algorithm for Hydro-thermal Unit Commitment Considering the Security Constraints

This paper proposes a new approach with two efficient metaheuristic algorithms, in combination with quadratic programming, to solve the nonlinear optimization problem Unit Commitment in a complex hydro-thermal power system i.e. Hydrothermal Unit Commitment (HTUC). The main goal is to minimize the total costs (which are a very non-linear and non-convex problem), while satisfying the many hydro-thermal constraints. Such constraints, together with the nonlinear non-convex and mixed-integer objective function, make the search space extremely complex. To solve such a complicated system, the paper proposes a hybridization of a developed binary-coded genetic algorithm (in which quadratic programming is integrated), with a particle swarm optimization (PSO) algorithm. PSO is applied to the final economic load dispatch (ELD), based on the optimal binary combination obtained from the genetic algorithm. A new approach has been proposed through the application of a repair mechanism, which is based on a priority list, in order to maintain the diversity of the population and prevent premature convergence. The entire algorithm was developed and tested in MATLAB and then applied to the IEEE 30 BUS test system. The experimental results show better performance of the proposed algorithm compared to the recently published algorithms, in terms of convergence, constraint handling, as well as better solution quality.

Best groyne series as countermeasure against river bend scour

A new approach for finding a cost-effective combination of groynes in a channel bend for the prevention of bend scour is presented. A linked simulation–optimisation model is proposed for this purpose. The morphological flow simulation model was developed by solving the shallow-water equations and the Exner sediment continuity equation in a boundary-fitted coordinate system. The optimisation model was developed utilising a binary-coded genetic algorithm. The proposed model can automatically provide a cost-effective groyne series in terms of their number, length and position in any vulnerable channel bend. A constraint in terms of bend scour is also incorporated in the optimisation model, which allows the optimisation model to obtain the best groyne combination for no bend scour. To appraise the performance of the model, it was applied to a hypothetical vulnerable channel bend. The model produced the minimised cost of installation of the groyne system at that location, at the same time as making the location free from bend scour.

An ISO-based security constrained generation maintenance coordination considering long-term SCOPF

Generation maintenance scheduling plays a crucial role in power system operation and planning; as it makes a significant effect on the outage time of generating units, and subsequently, to have a more secure and reliable power system with an optimal maintenance plan. Conventional maintenance scheduling methods are no longer applicable when the restructured power systems introduced. It is thus essential to develop novel approaches for the maintenance coordination. In this paper, an ISO-based security-constrained generation maintenance coordination approach is proposed. The approach comprises two main phases, the security-based maintenance scheduling, and the bidding based maintenance coordination. Moreover, the N−1 contingency analysis is considered in this study. The network security constraints are also incorporated in the investigation, using the security-constrained optimal power flow model. The model is solved by a binary-coded genetic algorithm which is combined with a primal-dual interior-point method. For systematic analysis, the IEEE 30-Bus and the IEEE-118 test systems are employed. The presented results emphasize the effectiveness and applicability of the proposed approach.

Optimal GENCO's bidding strategy in a power exchange facilitating combined power and emission trading using Intelligent Programmed Genetic Algorithm

Currently, for developing optimal bidding strategy considering carbon credit trading, Generating Company (GENCO) separately participates in electric power and emission market. Recently, researchers proposed a new scheme in which emission trading is facilitated within Power Exchange (PX). Developing optimal GENCO's bidding strategy considering carbon credit trading within PX is a complex single-objective optimization problem with generator's surplus as the main objective function. Obtaining its solution using a simple evolutionary optimization algorithm is difficult and time-consuming. Therefore, this paper presents an application of Intelligent Programmed Genetic Algorithm (IPGA) equipped with advanced deterministic diversity creating operator for solving optimal GENCO's bidding strategy problem with minimum number of objective function evaluations. The performance of IPGA is first tested on three different categories of standard test functions by comparing its simulation results with that obtained using binary-coded GA, simulated annealing, particle swarm optimization, biogeography-based optimization, teaching learning-based optimization, and differential evolution-based hybrid GA. A system comprising of six GENCOs and two buyers is used to validate the applicability of IPGA. IPGA along with other algorithms was used to obtain optimal bidding curve for 24 hours for GENCO GC-1. Simulation results clearly show much faster convergence for IPGA in terms of the number of objective function evaluations required to reach optima. Result also shows that GENCO GC-1 can obtain much higher GC-1 surplus, carbon credit limit, and social welfare by utilizing the obtained bidding strategy using IPGA as compared with the bidding strategy obtained by other algorithms.

A security-based observability method for optimal PMU-sensor placement in WAMS

Optimal PMU placement (OPP) problem is one of the most important issues in wide area measurement systems (WAMS). Delivering the measured data to the control center is possible through communication infrastructure (CI) that is a part of the WAMS. However, to well estimate the state of the system, observability of the measurement system is necessary. In this paper, a security-based observability (SBO) method for the PMUs and related CI is proposed. A hybrid wireless sensor-based structure including energy harvesting sensor nodes (EHSNs) and plug-in powered sensor nodes (PPSNs) is utilized as the communication system. The contingency-constraint optimal PMU placement and related sensor-based CI is proposed and formulated. To increase the reliability of the CI, a reliability index for the wireless sensors is added as a constraint to the objective function which includes the total cost minimization subject to power system observability and communication constraints. The effect of zero-injection buses (ZIBs) and radial buses in the power system are also included in the model to get more practical results. A mixed-integer linear programming (MILP) is used for contingency constraint PMU placement and a binary-coded genetic algorithm (BCGA) is accordingly used for solving the examined communication optimization. The IEEE 13-bus and IEEE 37-bus test feeder systems are utilized to evaluate the effectiveness of the proposed method.

An Intelligent Programmed Genetic Algorithm with advanced deterministic diversity creating operator using objective surface visualization

This paper presents a new fast Intelligent Programmed Genetic Algorithm (IPGA) based evolutionary optimization algorithm which requires lesser number of objective function evaluation for reaching optima. The proposed algorithm, apart from using probabilistic genetic operator, i.e. crossover and mutation, also uses a deterministic diversity creating operator for generating new solution in the current population. This is done by first projecting objective surface from higher dimension to lower dimension for visualization purpose and then deterministically generates new solution using some predefined rules in the region with higher objective function value. As the newly generated solution is in lower-dimensional space, these solutions are again projected back to higher dimensional space and then the objective function is evaluated at that point. The proposed IPGA is tested on three different categories of standard test functions viz. Unimodal function (2 Test Function), Unrotated Multimodal function (6 Test Function) and Rotated Multimodal function (5 Test Function). Simulation results were compared with that obtained using Binary Coded GA, Real Coded GA, recently proposed GA with Differential Evolution crossover operator (GA–DEx) and another success-history-based adaptive GA with aging mechanism (GA–aDExSPS) in terms of mean and standard deviation of the objective function, average number of objective function evaluation required to reach optima and algorithmic complexity. Simulation results clearly demonstrate better performance of the proposed IPGA when compared with other variants of GAs.