Hybrid Genetic Algorithm And Particle Swarm Optimization Research Articles

Wind farm layout optimization through optimal wind turbine placement using a hybrid particle swarm optimization and genetic algorithm.

The placement and configuration of wind turbines (WTs) are the key factors in determining the performance and energy output of a wind farm (WF). This involves considering various elements such as wind speed, wind direction, and the interspacing between turbines in the design process. To achieve an optimized and consistent wind farm layout optimization (WFLO) for maximum output power, a novel hybrid algorithm hybrid particle swarm optimization and genetic algorithm (HPSOGA), combining particle swarm optimization (PSO) and genetic algorithm (GA), is proposed. HPSOGA can effectively handle problems with multiple local optima, as PSO explores multiple regions and GA refines solutions found by PSO. The framework has two phases, where PSO improves initial parameters in the first phase, and parameters are adjusted in the second phase for improved fitness. The wake effect is analyzed using the Jenson-Wake model, and the objective function considers the total cost of WTs and the power output of the WF. The interspacing of WTs is evaluated by the rule of thumb. HPSOGA outperforms other methods such as GA, BPSO-TVAC, L-SHADE, BRCGA, and EO-PS, producing better results in terms of total output power generation. The simulation results validate the reliability of HPSOGA in WFLO.

Optimized Ambulance Allocation Using Hybrid PSOGA for Improving the Ambulance Service

Emergency Medical Service (EMS) is an integral part of the healthcare system that works dedicatedly to save the lives of people. The prime responsibility of any EMS provider is to offer a timely response to the person in need. The timely assistance increases the chances of survivability of a person. In India, escalating count of road accidents has increased the demand for ambulance services to provide pre-hospital treatment or transportation of victims to the hospital. This paper presents a new optimization strategy of Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) for ambulance allocation to reduce the ambulance response time. Considering a set of assumptions, the authors have applied the new strategy for allocating 50 ambulances to 11 base stations in Southern Delhi. The working environment of EMS which includes stochastic requests, travel time, and dynamic traffic conditions have been taken into account to attain accurate results. The new optimization strategy of HPSOGA has been implemented in a MATLAB environment to find an optimized allocation plan with minimum response time. With the proposed algorithm the authors have been able to reduce the average response time by 11.61%. The paper also presents the comparison of HPSOGA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the stated problem. The algorithms are compared in terms of objective value (response time), convergence rate, and constancy repeatability to conclude that HPSOGA performs better than the other two algorithms.

Using hybrid GA-PSO algorithm to solve problem in machine scheduling

In this paper, we describe new hybrid genetic algorithm and particle swarm optimization to solve the sum of earliness and the number of tardy job on two-machines flow shop schedule problem is NP- hard. The study discusses a hybrid genetic algorithm and particle swarm optimization (HGA-PSO) to tackle the presented mission. Extensive experiments, based on computers, suggest that the proposed mathematical models are efficient in solving flow shop problems with GA solved to n = 3000 while PSO solved to n = 500 job.

Integration of advanced optimization algorithms into least-square support vector machine (LSSVM) for water quality index prediction

Abstract Machine learning models hybridized with optimization algorithms have been applied to many real-life applications, including the prediction of water quality. However, the emergence of newly developed advanced algorithms can provide new scopes and possibilities for further enhancements. In this study, the least-square support vector machine (LSSVM) integrated with advanced optimization algorithms is presented, for the first time, in the prediction of the water quality index (WQI) at the Klang River of Malaysia. Thereafter, the LSSVM model using RBF kernel was optimized using the hybrid particle swarm optimization and genetic algorithm (HPSOGA), whale optimization based on self-adapting parameter adjustment and mix mutation strategy (SMWOA) as well as ameliorative moth-flame optimization (AMFO) separately. It was found that the SMWOA-LSSVM model had the better performance for WQI prediction by having the best achievement root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2) and mean absolute percentage error (MAPE). Comprehensive comparison was done using the global performance indicator (GPI), whereby the SMWOA-LSSVM had the highest average score of 0.31. This could be attributed to the internal architecture of the SMWOA, which was catered to avoid local optima within short optimization period.

A Coscheduling Model of Automated Single-Beam Cranes and AGVs in Assembly Workshop

The simultaneous scheduling of cranes and automated guided vehicles (AGVs) has been a critical problem, especially in traditional heavy-duty manufacturing enterprises. Previous studies mainly focus on the coscheduling problem for the container terminals rather than workshop. Hence, this study evaluated the coscheduling problem of automated single-beam cranes and AGVs incurred by an actual manufacturing environment. A joint scheduling model was established by considering the constraints of conflict-free, noncrossing, and safety distance as well as the feature of the material. This model considers the process and minimum makespan as the scheduling unit and optimization objective, respectively, including handling time and process time. Subsequently, a hybrid genetic algorithm and particle swarm optimization (HGA-PSO) was developed, and a check-repair procedure was designed to guarantee the feasibility of optimal solution. Finally, numerical illustrative experiments and nonparametric tests were conducted to verify the effectiveness of the proposed model and algorithm, and the results show that the proposed algorithm has better performance than PSO and GA in solving the proposed model.

APPLICATION OF HYBRID GA-PSO TO IMPROVE THE PERFORMANCE OF DECISION TREE C5.0

Data mining is a data extraction process with large dimensions and information with the aim of obtaining information as knowledge to make decisions. Problems in the data mining process often occur in high-dimensional data processing. The solution to handling problems in high-dimensional data is to apply the hybrid genetic algorithm and particle swarm optimization (HGAPSO) method to improve the performance of the C5.0 decision tree classification model to make decisions quickly, precisely and accurately on classification data. In this study, there were 3 datasets sourced from the University of California, Irvine (UCI) machine learning repositories, namely lymphography, vehicle, and wine. The HGAPSO algorithm combined with the C5.0 decision tree testing method has the optimal accuracy for processing highdimensional data. The lymphography and vehicle data obtained an accuracy of 83.78% and 71.54%. The wine dataset has an accuracy of 0.56% lower than the conventional method because the data dimensions are smaller than the lymphography and vehicle dataset.

Find optimal capacity and location of distributed generation units in radial distribution networks by using enhanced coyote optimization algorithm

This paper proposes a novel effective optimization algorithm called enhanced coyote optimization algorithm (ECOA). This proposed method is applied to optimally select the position and capacity of distributed generators (DGs) in radial distribution networks. It is a multi-objective optimization problem where properly installing DGs should simultaneously reduce the power loss, operating costs as well as improve voltage stability. Based on the original coyote optimization algorithm (COA), ECOA is developed to be able to expand the search area and retain a good solution group in each generation. It includes two modifications to improve the efficiency of the original COA approach where the first one is replacing the central solution by the best current solution in the first new solution generation technique and the second focuses on reducing the computation burden and process time in the second new solution generation step. In this research, various experiments have been implemented by applying ECOA, COA as well as salp swarm algorithm (SSA), Sunflower optimization (SOA) for three IEEE radial distribution power networks with 33, 69 and 85 buses. Obtained results have been statistically analyzed to investigate the appropriate control parameters and to verify the performance of the proposed ECOA method. In addition, the performance of ECOA is also compared to various similar meta-heuristic methods such as genetic algorithm (GA), particle swarm optimization (PSO), hybrid genetic algorithm and particle swarm optimization (HGA-PSO), simulated annealing, bacterial foraging optimization algorithm, backtracking search optimization algorithm, harmony search algorithm, whale optimization algorithm (WOA) and combined power loss index-whale optimization algorithm (PLI-WOA). Detailed comparisons show that ECOA can determine more effective location and size of DGs with faster speed than other methods. Specifically, the improvement levels of the proposed method over compared to SFO, SSA, and COA can be up to 2.1978%, 0.7858% and 0.2348%. Furthermore, as compared to other existing methods in references, ECOA achieves the significant improvements which are up to 31.7491%, 20.2143% and 22.7213% for the three test systems, respectively. Thus, the proposed method is a favorable method in solving the optimal determination of DGs in radial distribution networks.

Modeling of CO2 absorption capabilities of amino acid solutions using a computational scheme

AbstractIn this communication, modeling of carbon dioxide absorption by various amino acid solutions is presented as a function of operational parameters using the Least‐Squares Support Vector Machine (LSSVM) algorithm integrated with three different evolutionary algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Hybrid GA and PSO (HGAPSO). A databank containing 255 data of carbon dioxide absorption by amino acids of potassium taurate, potassium glycinate, potassium prolinate, and potassium lysinate at different temperatures, partial pressures, and concentrations was prepared from different sources to train and test the proposed algorithms. The models were applied to estimate carbon dioxide absorption by amino acid solutions in terms of temperature, molar concentration, equilibrium partial pressure, molecular weight, number of hydrogen bond donor, number of hydrogen bond acceptor, and number of rotatable bond. The R2 values of LSSVM optimized by HGAPSO, PSO, and GA are 0.9944, 0.9915, and 0.9891, respectively, and the various errors were determined close to zero. On the other hand, the visual comparison of models outputs and actual carbon dioxide adsorption data was used to clarify performances of the models. By comparison analysis, it was found that the LSSVM‐HGAPSO is the most accurate model for estimation of carbon dioxide loading. Also, comparison of our proposed models results with previously reported artificial neural network results indicates the impressive estimation capability of LSSVM algorithm. According to sensitivity analysis, it becomes obvious that pressure is the most effective parameter on carbon dioxide absorption.

Machine learning models to predict bottom hole pressure in multi‐phase flow in vertical oil production wells

The precise estimation of a pressure drop in vertical multiphase flowing oil wells plays a crucial role in designing robust production facilities and evaluating optimum production plans. A significant amount of research has been conducted on determining a pressure drop via calculating the bottom hole pressure (BHP); these different methods as well as numerical, analytical, semi‐analytical, and empirical correlations can be used for doing this task. Unfortunately, most of those correlations are unable to provide reasonable precision when calculating BHP and, consequently, improvements are still required. To predict BHP in vertical wells, several hybrids of meta‐heuristic optimization methods and a bio‐inspired connectionist approach, i.e., artificial neural network (ANN), are employed. The main goal of these optimization algorithms is to optimize the parameters of the ANN models, i.e., weights and biases, to improve their performance. Based on the obtained outputs and various robustness indexes, a hybrid genetic algorithm and particle swarm optimization (HGAPSO) is highly precise and has a maximum of 10 % error compared to measured pressure data. The results of this work reveal the capability of those hybrid connectionist models for predicting the BHP of multi‐phase flow in vertical wells; these results demonstrate the promising use of connectionist methods in commercial production software in the near future.

A Dynamic Scheduling Method for Logistics Tasks Oriented to Intelligent Manufacturing Workshop

Aiming at the logistics dynamic scheduling problem in an intelligent manufacturing workshop (IMW), an intelligent logistics scheduling model and response method with Automated Guided Vehicles (AGVs) based on the mode of “request-scheduling-response” were proposed, and they were integrated with Internet of Things (IoT) to meet the demands of dynamic and real time. Correspondingly, a mathematical model was developed and integrated with a double-level hybrid genetic algorithm and ant colony optimization (DLH-GA-ACO) to minimize the finish time with the minimum AGVs and limited time. The mathematical model optimized the logistics scheduling process on two dimensions which include the sequence of tasks assigned to an AGV and the matching relation between transfer tasks and AGVs (AGV-task). The effectiveness of the model was verified by a set of experiments, and comparison among DLH-GA-ACO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO), and tabu search algorithm (TSA) was performed. In the experiments, the DLH-GA-ACO ran in a distributed environment for a faster computing speed. According to the comparisons, the superiority and effectiveness of DLH-GA-ACO on dynamic simultaneous scheduling problem were proved and the intelligent logistics scheduling model was also proved to be an effective model.

A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

Aimed at the problem of the green scheduling problem with automated guided vehicles (AGVs) in flexible manufacturing systems (FMS), the multi-objective and multi-dimensional optimal scheduling process is defined while considering energy consumption and multi-function of machines. The process is a complex and combinational process, considering this characteristic, a mathematical model was developed and integrated with evolutionary algorithms (EAs), which includes a sectional encoding genetic algorithm (SE-GA), sectional encoding discrete particle swarm optimization (SE-DPSO) and hybrid sectional encoding genetic algorithm and discrete particle swarm optimization (H-SE-GA-DPSO). In the model, the encoding of the algorithms was divided into three segments for different optimization dimensions with the objective of minimizing the makespan and energy consumption of machines and the number of AGVs. The sectional encoding described the sequence of operations of related jobs, the matching relation between transfer tasks and AGVs (AGV-task), and the matching relation between operations and machines (operation-machine) respectively for multi-dimensional optimization scheduling. The effectiveness of the proposed three EAs was verified by a typical experiment. Besides, in the experiment, a comparison among SE-GA, SE-DPSO, H-SE-GA-DPSO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO) and a tabu search algorithm (TSA) was performed. In H-GA-PSO and TSA, the former just takes the sequence of operations into account, and the latter takes both the sequence of operations and the AGV-task into account. According to the result of the comparison, the superiority of H-SE-GA-DPSO over the other algorithms was proved.

Secure data in cloud with multimodal key generation

Data Security is the Major problem in Cloud Computing. In order to overcome the data security problem the proposed technique utilizes effective data storage using biometric-based cryptographic authentication to support the user authentication for the cloud environment. For user authentication here we are considering iris and fingerprint. Initially the feature values are extracted from the iris and fingerprint using local binary pattern and Minutiae extraction respectively. Local binary pattern operator works with the eight neighbors of a pixel, using the value of this center pixel as a threshold. Minutiae points are the major features of a fingerprint image and are used in the matching of fingerprints. These minutiae points are used to determine the uniqueness of a fingerprint image. Based on that the proposed feature values are extracted from the iris and fingerprint image. In order to improve the security, the suggested technique utilizes the optimal features. For selecting the optimal features hybrid particle swarm optimization and genetic algorithm (HPSOGA) is utilized. Particle swarm optimization (PSO) is a population based stochastic optimization technique. The system is initialized with a population of random solutions and searches for optima by updating generations. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics. In our proposed method these two optimization algorithm is hybrid for more secure. From the optimization algorithm the suggested technique selects the optimal features. and then the optimal features are used to encrypt the input data. For encryption and decryption, the proposed technique utilizes Triple DES algorithm. Finally the encrypted data is stored in cloud. The performance of the proposed technique is evaluated in terms of encryption and decryption time, memory utilization and overall execution time. Our proposed data storage using biometric-based authentication is implemented with the help of Cloud simulator in the working platform of java.

A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems

Abstract In this paper, a new hybrid particle swarm optimization and genetic algorithm is proposed to minimize a simplified model of the energy function of the molecule. The proposed algorithm is called Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA). The HPSOGA is based on three mechanisms. The first mechanism is applying the particle swarm optimization to balance between the exploration and the exploitation process in the proposed algorithm. The second mechanism is the dimensionality reduction process and the population partitioning process by dividing the population into sub-populations and applying the arithmetical crossover operator in each sub-population in order to increase the diversity of the search in the algorithm. The last mechanism is applied in order to avoid the premature convergence and avoid trapping in local minima by using the genetic mutation operator in the whole population. Before applying the proposed HPSOGA to minimize the potential energy function of the molecule size, we test it on 13 unconstrained large scale global optimization problems with size up to 1000 dimensions in order to investigate the general performance of the proposed algorithm for solving large scale global optimization problems then we test the proposed algorithm with different molecule sizes with up to 200 dimensions. The proposed algorithm is compared against the standard particle swarm optimization to solve large scale global optimization problems and 9 benchmark algorithms, in order to verify the efficiency of the proposed algorithm for solving molecules potential energy function. The numerical experiment results show that the proposed algorithm is a promising and efficient algorithm and can obtain the global minimum or near global minimum of the molecular energy function faster than the other comparative algorithms.

Hybrid genetic algorithm and particle swarm for optimal power flow with non-smooth fuel cost functions

This paper presents a hybrid genetic algorithm and particle swarm optimization (HGAPSO) for solving optimal power flow problem with non-smooth cost function and subjected to limits on generator real, reactive power outputs, bus voltages, transformer taps and power flow of transmission lines. In (HGAPSO), individuals in a new generation are created, not only by crossover and mutation operation as in (GA), but also by (PSO). The effectiveness of this algorithm is examined and tested for standard IEEE 30 bus system with six generating units. The results of the proposed technique are compared with that of PSO and other methods reported in the literature.

Process parameters optimization for multiple quality characteristics in plastic injection molding using Taguchi method, BPNN, GA, and hybrid PSO-GA

This paper presents a two stage optimization system to find optimal process parameters of multiple quality characteristics in plastic injection molding. Taguchi method, Back-Propagation Neural Network (BPNN), Genetic Algorithm (GA), and combination of Particle Swarm Optimization and Genetic Algorithm (PSO-GA) are used in this study to find optimum parameter settings. Melt temperature, injection velocity, packing pressure, packing time, and cooling time are selected as initial process parameters in the experiment. First, experimental work is conducted using Taguchi orthogonal array. According to the result from the Taguchi experiment, S/N ratio is calculated to find the best combination settings for product quality. Then, ANOVA is used to determine significant factors of the control parameters. Moreover, the S/N ratio predictor and quality predictor are constructed using BPNN. In the first stage optimization, S/N ratio predictor and GA are used to reduce variance of quality characteristic. In the second stage optimization, the S/N ratio predictor and quality predictor with hybrid PSO-GA are used to find optimal parameter settings for quality characteristic and stability of the process. Finally, three confirmation experiments are conducted to assess the effectiveness of the proposed system. Upon optimization, it is seen that the proposed system not only improves the quality of plastic parts, but also reduces variability of the process effectively.

Hybrid Genetic Algorithm and Particle Swarm Optimization (HGAPSO) Algorithm for Solving Optimal Reactive Power Dispatch Problem

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. This paper introduces, an evolutionary algorithm based on the hybrid genetic algorithm (GA) and particle swarm optimization (PSO), denoted by HGAPSO is used to solve reactive power dispatch problem.

Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion

Asphaltene is the heaviest component in crude oil. Initially, asphaltene is dissolved in crude oil. Once asphaltene is separated from the crude oil due to pressure loss or composition change in addition of solvents, asphaltene may deposit over surface rock and plug some pore throats that produce more flow resistance for oil in porous medium. In this work, the model based on a feed-forward artificial neural network (ANN) optimized by hybrid genetic algorithm and particle swarm optimization (HGAPSO) as an intelligent approach to forecast asphaltene precipitation due natural depletion is proposed. Hybrid genetic algorithm and Particle swarm optimization (HGAPSO) is carried out to decide the initial weights of the neural network. The HGAPSO-ANN model is implemented to the experimental data from one of northern Persian Gulf oil field. The forecasted outputs from the HGAPSO-ANN model and BP-ANN were compared to the experimental precipitation data. Low deviation between forecasted results of proposed model and experimental data validate good precision and accuracy of the model. The good performance of the proposed HGAPSO-ANN model ascertain by comparison between the prediction of this model and corresponding experimental data.

Prediction of minimum miscible pressure by using neural network based hybrid genetic algorithm and particle swarm optimization

Miscible gas injection is considered as an effective enhanced oil recovery (EOR) technique for conventional oil reservoirs. Minimum miscibility pressure (MMP) is an important parameter in design of a gas injection project as sweep efficiency from gas injection highly depends on the value of MMP. Obtaining MMP using laboratory techniques is cost, labor, and time intensive. Hence, finding a rapid, inexpensive, and robust technique to estimate the gas–oil MMP is inevitable. In this work, the model based on a feed-forward artificial neural network (ANN) optimized by Hybrid Genetic Algorithm and Particle Swarm Optimization (HGAPSO) to estimate gas–oil MMP is proposed. Genetic Algorithm is used to decide the initial weights of the neural network. The performance of the HGAPSO–ANN model is compared with experimental gas–oil MMP and the calculated results for the common gas–oil MMP correlations. The results demonstrate the effectiveness of the HGAPSO–ANN model.