Real-coded Genetic Algorithm Research Articles

Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique

To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a complex objective. In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD). Due to trade-offs between water supply and flood management, the HHO and OBL-HHO models have configurable thresholds to optimise the KGD reservoir operation. To determine the efficacy of the HHO and OBL-HHO in reservoir optimisation, reliability, vulnerability, and resilience are risk measures evaluated. If inflow categories are omitted, the OBL-HHO meets 71.49% of demand compared to 54.83% for the standalone HHO. The HHO proved superior to OBL-HHO in satisfying demand during medium inflows, achieving 38.60% compared to 20.61%, even though the HHO may have experienced water loss at the end of the storage level. The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. For resilience, the ABC scored 0.16, PSO scored 0.15, and real coded GA scored 0.14 whilst the binary-GA has the worst failure recovery algorithm with 0.09.

Optimising Characteristics of an Intelligent Transport System Using a Real-Coded Genetic Algorithm Based on Adaptive Mutation

A novel real-coded genetic algorithm (FCGA-AM) that uses the proposed adaptive mutation (AM) operator is presented. The algorithm is designed to optimise the characteristics of the developed intelligent transportation system. The performance of the proposed genetic algorithm was evaluated in comparison with other methods of multicriteria heuristic optimization with the use of various test instances and well-known quality metrics for Pareto frontiers. At the next stage, the FCGA-AM was applied to find the best road safety trade-offs involving unmanned ground vehicles (UAVs) interacting with conventional ground vehicles (UTVs), pedestrians and other agents

Multiple-Rack Strategies Using Optimization of Location Assignment Based on MRCGA in Miniload Automated Storage and Retrieval System

This paper aimed to introduce multiple-rack strategies in miniload automated storage and retrieval systems (AS/RSs), which included first fit (FF) and best fit (BF) assignment methods based on a matrix real-coded genetic algorithm (MRCGA) in the storage and retrieval process. We validated the probability occurrence of item sizes as a contributory factor in multiple-rack strategies, and compared their capacities, utilization of units and space by equal probabilities or the 80/20 law. According to the analytical methods, BF showed a reduction of more than 11.2% than FF on travel distance, and Type B-FF, Type B-BF and Type C-BF were better able to meet high-density requirements. These strategies provide diversified storage and retrieval solutions for the manufacturing and express delivery industry.

Valuation of an unexplored oilfield under uncertain oil price and reservoir condition: A stochastic dynamic programming approach with simulation-based reward function

We develop an unexplored oilfield valuation model under uncertain exploration outcomes, reservoir conditions, and oil prices. The exploration outcomes follow a binomial process, while oil prices are represented using the Schwartz-Smith model. The reservoir conditions are characterized by the joint probability distribution of post-discovery parameters estimated using a machine learning approach. The corresponding valuation model is a typical stochastic dynamic programming problem with a simulation-based reward function. The net cash flow lattice takes the form of a recombining quadrinomial derived from the discrete representation of the Schwartz-Smith oil price model. We apply backward induction with embedded real-coded genetic algorithms to the net cash flow lattice to calculate the oilfield value. The model allows for field abandonment before lease expiration if the remaining reserve is uneconomical. To improve computational efficiency, we combine the Latin hypercube sampling and antithetic variates to reduce the variances. The model is implemented in an unexplored oilfield under two scenarios of oil presence probability: 100% and 75%. In the first scenario, we obtained a mean oilfield value of US$5.5 million with a CVaR of US$0.79 million, while in the second, we came up with a mean of -US$0.77 million and a CVaR of US$31.74 million.

Cooperative coevolutionary differential evolution with linkage measurement minimization for large-scale optimization problems in noisy environments

Many optimization problems suffer from noise, and the noise combined with the large-scale attributes makes the problem complexity explode. Cooperative coevolution (CC) based on divide and conquer decomposes the problems and solves the sub-problems alternately, which is a popular framework for solving large-scale optimization problems (LSOPs). Many studies show that the CC framework is sensitive to decomposition, and the high-accuracy decomposition methods such as differential grouping (DG), DG2, and recursive DG (RDG) are extremely sensitive to sampling accuracy, which will fail to detect the interactions in noisy environments. Therefore, solving LSOPs in noisy environments based on the CC framework faces unprecedented challenges. In this paper, we propose a novel decomposition method named linkage measurement minimization (LMM). We regard the decomposition problem as a combinatorial optimization problem and design the linkage measurement function (LMF) based on Linkage Identification by non-linearity check for real-coded GA (LINC-R). A detailed theoretical analysis explains why our proposal can determine the interactions in noisy environments. In the optimization, we introduce an advanced optimizer named modified differential evolution with distance-based selection (MDE-DS), and the various mutation strategy and distance-based selection endow MDE-DS with strong anti-noise ability. Numerical experiments show that our proposal is competitive with the state-of-the-art decomposition methods in noisy environments, and the introduction of MDE-DS can accelerate the optimization in noisy environments significantly.

Support vector machine-based rapid detection and quantification of butter yellow adulteration in mustard oil using NIR spectra

Butter yellow (BY; 3′-methyl-4-dimethyl-aminoazobenzene) is a carcinogenic adulterant of edible mustard oil. In this study, Fourier-transform near-infrared (FT-NIR) spectral data was combined with multivariate data analysis to both detect and quantify BY adulteration in mustard oil. Fifteen pure mustard oil and one hundred fifty mustard oil samples with BY (0.1–1% w/v) as the adulterant were evaluated. It was observed that sonication- based processing of adulterated samples provides sensitive fingerprints during FT-NIR analysis when compared to heat- based processing. Principal component analysis (PCA) of spectral regions 6000–6064 cm−1 showed well-defined discrimination of pure and adulterated mustard oil with 100% variability. For quantification of BY both NIPALS (non-linear iterative partial least squares) based partial least square (PLS) and LS-SVM (least squares support vector machine) were carried for full-spectrum as well as selected fingerprints using RCGA (real coded genetic algorithm), a variable selection method. Overall, RCGA-LS-SVM model showed best calibration and prediction model with high precision and accuracy based on low root mean square error of cross validation (RMSECV = 0.0073) and prediction (RMSEP = 0.0060). RCGA has indeed lowered the number of variables to mere 30 variables to predict BY in the range of (0.1–1% w/v) with a correlation coefficient of calibration (RC2 = 0.9997) and prediction (RP2 = 0.9996). The key fingerprints detect CH3 and CH functional groups (1st overtone) of BY. This study demonstrated that FT-NIR spectroscopy, combined with selected 30 variables using RCGA-PLS procedure could be a robust technique for the rapid quantification of butter yellow in mustard oil samples. The study would certainly help in quality control and quality assurance of mustard oil and could be emulated for other edible oils.

Performance of optimization methods for energy efficiency in cooperative communication

In cooperative communication the effect of channel fading can be improved by cooperation between the user terminals and the relay nodes in wireless networks. In a Wireless Sensor Network (WSN), cooperative relaying improves the link quality with a relatively high Energy Efficiency Gain (EEG). In this paper, optimized parameters are used in WSN to enhance the EEG using particle swarm optimization (PSO) and Real-Coded Genetic Algorithm (RGA). Maximum enhancements of EEG obtained using RGA for M-ary Quadrature Amplitude Modulation (M-QAM) is 64% for M=16, 87% for M=32, and 97% for M=64 compared to EEG obtained without optimization. The superiority proposed optimization methods are verified by comparing with results without optimization and by comparing with the published results for Energy Efficiency (EE).

Optimal planning and operation of energy hub by considering demand response algorithms and uncertainties based on problem-solving approach in discrete and continuous space

• Energy hub optimal planning and operation based on stochastic-probability indices. • Implement and evaluate the impact of DR/IDR algorithms. • Load uncertainty analysis and probability behaviour of renewable energy sources. In this research, the two-level structure of optimal planning and operation of the energy hub (EH) based on demand uncertainty and renewable energy resources (RES) is presented. The optimal planning based on stochastic-probability models is presented at the primary and optimal operation based on stochastic-probability models is also presented at the secondary level. The proposed method is planned based on the problem-solving approach in continuous and discrete space. The optimal planning objectives include determining the optimal capacity of EH equipment and minimizing investment costs. Optimal operation objectives are also planned and formulated based on minimizing EH operation cost, reducing greenhouse gas emissions, increasing RES utilization based on stochastic-probability modelling, and examining the demand response/integrated demand response (DR/IDR) effect. The DR programs implementation causes a reduction of 14.3% of the EH total cost, and the IDR programs implementation also causes a reduction of 16.56% of the EH total cost. The use of the proposed optimal two-level model, in addition to efficiency in different operation scenarios, has also reduced the calculation time. The optimization problem is solved based on Mixed Integer Linear Programming (MILP) and Binary Real-Coded Hybrid Genetic Algorithm (HRBC-GA).

A robust fitness function and genetic algorithm to morphologically constrain the dynamics of interacting galaxies

Given observational data from systems of interacting galaxies, we seek to determine the values of various dynamical parameters through the optimization of numerical models via genetic algorithms. However, fitting these models can be quite difficult. In this paper, we show how naive implementations of fitness functions can yield unintuitive results. We then propose a novel fitness function which was developed by utilizing data from the Galaxy Zoo: Mergers project (GZM). The human-scored models obtained from GZM were used to validate our fitness function and led to the adoption of a tidal distortion term which dramatically improved results. We also give a characterization of various geometric and dynamical symmetries inherent within the system and show how the knowledge of these symmetries can be used to reduce the volume of the parameter search space when performing optimization. Lastly, we implement a real-coded genetic algorithm with features designed to address these symmetries. Using simulated target systems with known parameters as a surrogate for observational data, we test our fitness function and genetic algorithm for robustness, accuracy, and convergence. We discuss the link between the degree of tidal distortion present in a target image and the constraints on the dynamical parameters using three different target systems with varying morphology.

Phase-Transition Behaviors of Crystalline Hypophosphorous Acid and a Feasible Incommensurate Wave-like Array of the Proton Displacements from Each Center of Hydrogen Bonds along the 1-Dimensional Chain

Crystalline hypophosphorous acid, composed of strongly hydrogen-bonded chains, was investigated by adiabatic calorimetry in a temperature range between 20 and 306 K and by dielectrometry as a supplemental means. The heat-capacity curve showed subsequent phase transitions: a second-order one at 210.4 K followed by a first-order one at 159 K and the other two ones at 149 and 139.4 K with decreasing temperature. The total entropy of the transitions was assessed to be 0.35 J K–1 mol–1 by using the baseline heat capacities that were determined by means of a real-coded genetic algorithm. The small value was interpreted as the transitions were associated with displacements of the protons forming hydrogen bonds: namely, while they are located at the center between two oxygen atoms above 210.4 K, the protons are displaced close to one of the oxygen atoms with the bond length enlarged below it. Temperature dependence of dielectric constants displayed no sign, around 210 K, indicating the existence of (anti-)ferroelectricity. It was thus expected that the manner of off-center proton displacements along each hydrogen-bonded chain reveals a wave-like and incommensurate appearance below 210.4 K, referred to as the normal crystal lattice above it, and following locking to a wave-like but superlattice commensurate one at 159 K.

Transmission Congestion Management in Deregulated Power System Using Adaptive Restarting Genetic Algorithm

Power systems in a deregulated environment have more intense and recurrent transmission line congestion than conventionally regulated power systems. With the help of generation rescheduling, this article shows how to effectively manage congestion in the day-ahead energy market by taking corrective measures to reduce congestion. The research employs an adaptive restarting genetic algorithm (ARGA) to provide an effective congestion management strategy in a deregulated power market (DPM). The study makes two significant contributions. First, the generator sensitivity factors (GSF) are calculated to choose re-dispatched generators. Second, the least congestion cost is calculated using the adaptive restarting genetic algorithm. Several different line outage contingency cases on IEEE 30 bus systems are used to examine the suggested algorithm’s implementation efficacy. The simulation results demonstrate a significant reduction in net congestion costs, resulting in a more reliable and secure power system operation. The proposed algorithm was tested in a python environment, and power flow analysis was done using the PANDAPOWER tool. The acquired results are contrasted using several contemporary optimization approaches to validate the suggested technique’s validity. The ARGA technique gives a lower congestion cost solution than the particle swarm optimization (PSO), real coded genetic algorithm (RCGA), and differential evolution (DE) algorithm.

Numerical and experimental studies on a self-protected energy concentrator for water waves

Wave concentrators have significant value for ocean energy absorbers to reduce power generation costs. Besides, the protection problem of the concentrator itself should also be carefully considered. In this study, a self-protected energy concentrator (SPEC) for water waves consisting of several truncated cylinders arranged in a concentric circle is proposed by adopting the cloaking technology based on the idea of manipulating scattered waves. To design the SPEC, an optimization program built by a combination of the higher-order boundary element method, the wave interaction theory, and the real-coded genetic algorithm is utilized. To address the manipulation mechanism of the scattered waves as well as to investigate the performance of the SPEC against various wave steepness and a spectrum of waves, the scattered waves and the total waves both inside and outside the concentrator are numerical and experimentally analyzed. High energy density inside the concentrator and the self-protection of the concentrator are validated. It is found that the large wave steepness can reduce the efficiency of concentration and self-protection due to the wave breaking. The SPEC has a high performance at a moderate bandwidth. Besides, the wave frequencies perform a more significant effect on self-protection than the concentration characteristic.

Optimal allocation of irrigation water in a single-reservoir and a single-pumping-station system under deficit irrigation conditions

Abstract Aiming at the optimal allocation of irrigation water in a multi-water source project in a water resource shortage area, this study developed a water resource joint scheduling optimization model for the reservoir and the pumping station under deficit irrigation conditions. In the model, the maximum annual yield of the irrigation area was the objective function; the water supply, water spill of the reservoir and replenishment water of the pump station at each stage were the decision variables; and the total annual water supply of the system, the reservoir operation criteria, the water rights of the pumping station, and the water demand of the crop during the entire growth period were the constraint conditions. According to the characteristics of the model, a large system decomposition aggregation dynamic programming (DADP) method is proposed to transform the N + 1 dimensional dynamic programming problem into a N + 1 one-dimensional dynamic programming problem for solution. In addition, this study also uses the real-coded genetic algorithm (RGA) and DADP to compare the algorithms, and discusses the performance of the two algorithms from the optimization of the algorithm and the applicability of the algorithm.

Prediction of rail-wheel contact parameters for a metro coach using machine learning

Dynamics of metro coach is complex with frequent acceleration, braking, sharp turns and gradients. Due to sharp turns and twists in track, metro coaches are more prone to the derailment, frictional energy loss and wear. Since contact parameters are difficult to obtain experimentally and are time consuming to determine even computationally through multibody dynamics simulations, in this work, artificial neural network (ANN) and recurrent neural network (RNN) models are used to predict rail-wheel contact parameters. The determined parameters include contact forces, derailment ratio, frictional power, contact location and contact area. These models can be used for reducing derailment tendency, wheel wear and energy consumption. To tackle local extreme values, four meta-heuristic techniques namely Improved Real-coded Genetic Algorithm (IRGA), Success History based Adaptive Differential Evolution (SHADE), Bonobo Optimizer (BO) and Grey Wolf Optimizer (GWO) algorithms are used to optimize the ANN and RNN models. During training, inputs and outputs to the machine learning techniques are generated from a multibody dynamics model built in commercial software Simpack. The multibody dynamics model used is validated using instrumented field trials. Mean absolute percentage error (MAPE) is used for evaluating the performance of the machine learning techniques. Results demonstrate that SHADE is the best optimizer among the four meta-heuristic techniques considered. MAPE in tests, for SHADE-ANN and SHADE-RNN are found to be less than 7%. The developed machine learning models, particularly SHADE-RNN, can be used for onboard monitoring and can help the pilots in running the trains safely and efficiently.

Optimization of Material-Specific Parameters for Accurate Foaming Flow Analysis

In recent years, various models of polyurethane foam have been modeled using CAE analysis. Material properties are important for accurate modeling of polyurethane flow. However, it is difficult to measure accurate polyurethane material properties. Therefore, in CAE analysis, materialspecific parameters are incorporated in the density evaluation equation to correlate with the experimental results. However, since there is a little detail on how the material-specific parameters are determined, it is necessary to construct a method for determining the optimum parameters. In this study, we propose the genetic algorithm optimization method (real-coded GA) that can handle complex nonlinearities. In order to verify the validity of real-coded GA, the time-density evaluation empirical equation proposed by Kono et al. (2005) was used to find the optimal parameters for the material-specific parameters. It was possible to obtain optimum material-specific parameters that fit well with the experiment results. Furthermore, a three-dimensional fluid flow analysis was performed by incorporating Kono's empirical formula. The filling flow behavior and the peak height of the foam obtained in the experiment agreed with the CAE analysis results. It was shown that accurate CAE analysis is possible by this method.

Performance Comparison of Optimization Methods for Flat-Top Sector Beamforming in a Cellular Network

The flat-top radiation pattern is necessary to form an appropriate beam in a sectored cellular network and to pro vide users with best quality services. The flat-top pattern offers sufficient power and allows to minimize spillover of signal to adjacent sectors. The flat-top sector beam pattern is relied upon In sectored cellular networks, in multiple-input multiple-output (MIMO) systems and ensures a nearly constant gain in the desired cellular sector. This paper presents a comparison of such optimization techniques as real-coded genetic algorithm (RGA) and particle swarm optimization (PSO), used in cellular networks in order to achieve optimum flat-top sector patterns. The individual parameters of flat-top sector beams, such as cellular coverage, ripples in the flat-top beam, spillover of radiation to the adjacent sectors and side lobe level (SLL) are investigated through optimization performed for 40◦ and 60◦ sectors. These parameters are used to compare the performance of the optimized RGA and PSO algorithms. Overall, PSO outperforms the RGA algorithm.

Optimization of agricultural product storage using real-coded genetic algorithm based on sub-population determination

<span lang="EN-US">The storage of fresh agricultural products is a combinatorial problem that should be solved to to maximize number of items in the storage and also maximize the total profit without exceed the capacity of storage. The problem can be addressed as a knapsack problem that can be classified as NP-hard problem. We propose a genetic algorithm (GA) based on sub-population determination to address the problem. Sub-population GA can naturally divide the population into a set of sub-population with certain mechanism in order to obtain a better result. GA based on sub-population is applied by generating a set of sub-population which is happened in the process of initializing population. A special migration mechanism is developed to maintain population diversity. The experiment shows GA based on sub-population determination provide better results comparable to those achieved by classical GA.</span>

A kernel mixing strategy for use in adaptive Markov chain Monte Carlo and stochastic optimization contexts

Performing Markov chain Monte Carlo parameter estimation on complex mathematical models can quickly lead to endless searching through highly multimodal parameter spaces. For computationally complex models, one rarely has prior knowledge of the optimal proposal distribution. In such cases, the Markov chain can become trapped near a suboptimal mode, lowering the computational efficiency of the method. With these challenges in mind, we present a novel MCMC kernel which incorporates both mixing and adaptation. The method is flexible and robust enough to handle parameter spaces that are highly multimodal. Other advantages include not having to locate a near-optimal mode with a different method beforehand, as well as requiring minimal computational and storage overhead from standard Metropolis. Additionally, it can be applied in any stochastic optimization context which uses a Gaussian kernel. We provide results from several benchmark problems, comparing the kernel's performance in both optimization and MCMC cases. For the former, we incorporate the kernel into a simulated annealing method and real-coded genetic algorithm. For the latter, we incorporate it into the standard Metropolis and adaptive Metropolis methods.

Identification of delamination severity in a tapered FRP composite plate

The typical failure in fibre-reinforced polymer (FRP) laminated composites is delamination. It reduces structural stiffness and changes in dynamic responses, which can be successfully used for assessing structural health monitoring (SHM) of composite structures. SHM often requires the solution of an inverse problem. This study focuses on solving the inverse problem with minimal computational time to predict delamination size, in-plane location, and interface. For the inverse model, two computational intelligence algorithms, the surrogate-assisted real-coded genetic algorithm (SARGA) and an artificial neural network (ANN) were used. A surrogate model is a fast-executing model for predicting the shift in the natural frequency using a forward ANN. For ANN training, the database size was optimized. The layerwise theory was solved using the finite element method for a laminated tapered FRP plate with ply drop-off, and ‘constrained mode’ is used to model the delamination. The experimental modal analysis was conducted on a glass-fibre reinforced polymer tapered composite plate with one intact plate and three artificially delaminated plates. The algorithms are validated using numerically simulated and experimentally measured frequencies. The numerical validation showed a better prediction of delamination size, in-plane location, and interface when compared with the experimental validation. Nevertheless, SARGA can reasonably predict the size, in-plane (x) location and interface of the delamination with an average overlapping ratio of 72.89%. The comparison of algorithms demonstrates the application of SARGA with the frequency-based approach for delamination detection and real-time condition monitoring in real-world applications.

Improvement of Maneuverability Within a Multiagent Fuzzy Transportation System With the Use of Parallel Biobjective Real-Coded Genetic Algorithm

Over the past two decades, several simulation-based approaches have been developed to seek optimal solutions in complex multiagent systems (MASs). One example of these complex systems is the proposed multiagent fuzzy transportation system (FTS), in which agents, such as manned ground vehicles (MGVs) and unmanned ground vehicles (UGVs), use fuzzy logic for maneuvering while interacting with each other. The complexity of an FTS as an optimization problem is caused by two major factors, viz., inability to apply gradient-based optimization methods and uncertainty present in the FTS model. In these MASs, the objective functions are computed as an outcome of multiple agent-to-agent interactions. For the proposed FTS, two interconnected objectives are of prime importance while modeling the system: the number of potential traffic accidents, which should be minimized, and the output traffic that needs to be maximized. The resulting model is a simulation-based biobjective optimization problem. In an attempt to improve the maneuverability at various road network configurations (RNCs), in this work, a new parallel real-coded genetic algorithm based on fuzzy clustering (FCGA) is proposed that considers the specific requirements of the FTS model. Use of the FCGA aggregated with the FTS overcomes the difficulties associated with the optimization model and the limitations caused by simulation-based optimization (the large dimensionality of decision space, uncertainty present in the MAS environment, etc.). After showing superior performance in approximating the Pareto-optimal solutions for known test instances, the proposed FCGA is applied to the proposed multiagent FTS to approximate the optimal parameters. Because of the optimization of FTS parameters with the FCGA and the use of the proposed fuzzy clustering algorithm (FCA) for the traffic density estimate, the suggested approach substantially improves the maneuverability of UGVs and MGVs to change lanes while considering turning based on fuzzy rules and overtaking.