A Hybrid Sperm Swarm Optimization and Genetic Algorithm for Unimodal and Multimodal Optimization Problems

Hybrid PSO and GA approach for optimizing surveyed asphalt pavement inspection units in massive network

This article proposes the hybrid Nelder–Mead (NM)–Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM–PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search method. In a suite of 17 multi-optima test functions taken from the literature, the computational results via various experimental studies showed that the hybrid NM–PSO approach is superior to the two original search techniques (i.e. NM and PSO) in terms of solution quality and convergence rate. In addition, the presented algorithm is also compared with eight other published methods, such as hybrid genetic algorithm (GA), continuous GA, simulated annealing (SA), and tabu search (TS) by means of a smaller set of test functions. On the whole, the new algorithm is demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for multimodal functions.

This paper studies the two-stage assembly flow shop problem (TAFSP) considering aging effects of the machines and preventive maintenance activities. At the first stage, m-1 parallel machines process parts of each jobs, and at the second stage, related parts of the jobs are assembled by one assembly machine. As the machines work on the jobs, their tools get aged. Aging effects on the machines causes that they will not be able to complete the jobs in the same time could as they were new or when they are operating jobs immediately after their preventive maintenance activity. Processing times of the job are related to the positions, in which it is located after the last preventive maintenance. The job that is operated in a position immediately after the preventive maintenance activity on a machine has its standard processing time. However, the processing time of the jobs operated in the further positions increase based on the number of the positions. The machines return to the initial condition after each preventive maintenance activity. The objective is to schedule the jobs on the machines and determine when the preventive maintenance activities get done on them in order to minimize the total weighted tardiness and maintenance costs. An integer mathematical model is presented for the problem and its validation is shown by solving an example in small scale. Since two-stage assembly flow shop problem is NP-hard, in order to solve the problem in medium and large scale two meta-heuristic algorithms, hybrid genetic algorithm (HGA) and hybrid particle swarm optimization (HPSO) are proposed. These algorithms are the hybrid version of genetic algorithm and particle swarm optimization representatively with simulated annealing. The algorithms are tuned by using Taguchi method, and are used to solve many numerical examples. Finally, the statistical analysis illustrates that the performance of HPSO is better than HGA.

ABSTRACTReservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability. Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data. Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil. To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools. The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions. Permeability is a petrophysical property that relates the amount of fluids in place and their potential for displacement. This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management. In this paper, a soft sensor on the basis of a feed‐forward artificial neural network was implemented to forecast permeability of a reservoir. Then, optimization of the neural network‐based soft sensor was performed using a hybrid genetic algorithm and particle swarm optimization method. The proposed genetic method was used for initial weighting of the parameters in the neural network. The developed methodology was examined using real field data. Results from the hybrid method‐based soft sensor were compared with the results obtained from the conventional artificial neural network. A good agreement between the results was observed, which demonstrates the usefulness of the developed hybrid genetic algorithm and particle swarm optimization in prediction of reservoir permeability.

PurposeThe purpose of this paper is to report the design of a lightweight tree-shaped support structure for fused deposition modeling (FDM) three-dimensional (3D) printed models when the printing path is considered as a constraint. Design/methodology/approachA hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) is proposed to address the topology optimization of the tree-shaped support structures, where GA optimizes the topologies of the trees and PSO optimizes the geometry of a fixed tree-topology. Creatively, this study transforms each tree into an approximate binary tree such that GA can be applied to evolve its topology efficiently. Unlike FEM-based methods, the growth of tree branches is based on a large set of FDM 3D printing experiments. FindingsThe hybrid of GA and PSO is effective in reducing the volume of the tree supports. It is shown that the results of the proposed method lead to up to 46.71% material savings in comparison with the state-of-the-art approaches. Research limitations/implicationsThe proposed approach requires a large number of printing experiments to determine the function of the yield length of a branch in terms of a set of critical parameters. For brevity, one can print a small set of tree branches (e.g. 30) on a single platform and evaluate the function, which can be used all the time after that. The steps of GA for topology optimization and those of PSO for geometry optimization are presented in detail. Originality/valueThe proposed approach is useful for the designers and manufacturers to save materials and printing time in fabricating complex models using the FDM technique. It can be adapted to the design of support structures for other additive manufacturing techniques such as Stereolithography and selective laser melting.

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.

The history matching, a key step in the reservoir numerical simulation, is performed to obtain a set of appropriate reservoir parameters to match the modeling results with the oilfield historical production data. The history matching problem is solved conventionally with Gauss-Newton, L-M, BFGS, and LBFGS methods, which require calculation of the gradient of the objective function, and is very slow in solving large-scale problems. In recent years, modern optimization algorithms, such as the genetic algorithm, particle swarm optimization, and differential evolution algorithm, have been applied in the reservoir history matching. In this paper, the hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) is used to carry out the research on the automatic reservoir history matching. First, the basic concepts of the particle swarm optimization (PSO) and the gravity search algorithm (GSA) were introduced. Then, the concept and algorithm of PSOGSA were described. Moreover, the actual oil field data were used to establish a geological model and an objective function, and the PSOGSA was used to carry out the automatic history matching. The PSOGSA was compared with other algorithms. Finally, the effects of parameters on the PSOGSA were analyzed. The results show that the PSOGSA has a good performance in the automatic reservoir history matching, increases the diversity of particles, improves the global search capability, and accelerates the convergence. The PSOGSA is superior to the PSO or the GSA.

— In this paper, two optimization algorithms i.e., Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are combined to produce a hybrid population-based algorithm PSOGSA. The approach of HPSOGSA is to use the assessment of PSO using the social thinking capability and the manipulation of GSA using the local search proficiency. To increase the performance of HPSOGSA, some benchmark functions are used. Two standard database ORL and YALE is used to assess the classification performance of the proposed method. Classification accuracy is compared for diverse number of training samples per class. Further lead of proposed method is confirmed by analyzing percentage improvement in classification accuracy. With limited accessibility of training sample, percentage enhancement is very successful for face recognition applications. Keywords: Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (HPSOGSA). Cite this Article Purvaa Saxena, Gargi Mishra, Apoorva Aggarwal, Ayush Priyadarshi, Nitin Kumar, Rahul. Pattern Recognition Using Hybrid Meta-Heuristic. Journal of Artificial Intelligence Research & Advances. 2019; 6(2): 100–108p.

Electric Vehicles (EVs) have a promising future as a means of transportation. During charging, EVs act as a load on the microgrid and during discharging; they serve as mobile generators. EVs require communication with roadside units (RSUs) and the microgrid controllers to navigate to the nearest charging station (CS) for charging/discharging operations. Standardising this communication can ensure interoperability and reliability. Furthermore, the handshaking operation for charging/discharging services must be completed within 2 s. This paper proposes IEC 61850-based communication to optimise EV charging/discharging costs. Cost optimisation is carried out using different metaheuristic algorithms, such as particle swarm optimisation (PSO), hybrid particle swarm optimisation and genetic algorithm (hybrid PSO-GA), grey wolf optimisation (GWO), red fox optimisation (RFO), Jaya algorithm (JA) among which PSO demonstrated superior performance when implemented using MATLAB 2022a. PSO exhibits superior convergence speed and global optimisation capabilities surpassing other algorithms by an average margin of 66.35%. Furthermore, the performance of the communication design is evaluated using Riverbed Modeller 18.5 and is found to be fast and well within the time frame set by the IEC 61850 standard.

This paper investigates the use of a Hybrid Energy Storage System (HESS) that contains a battery and fuel cell system with a microgrid (MG) to perform multiple functions such as supply-demand matching and energy arbitrage. The main objective of the proposed study is to obtain the optimal size of the MG system which minimizes the total costs and the total emissions. The problem is formulated as a nonlinear optimization problem with different constraints and solved using Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm. To validate the effectiveness of the HFPSO, the HFPSO is compared with the Particle Swarm Optimization (PSO) algorithm and the Firefly algorithm (FA). The result of the comparison proves the effectiveness of the HFPSO, and this is considered the main reason for using the HFPSO to solve the proposed difficult optimization problem. The problem is implemented using MATLAB software. The policy of the MG system is illustrated, and the results and the curves are discussed to validate this policy.

Wireless Localization based on Received Signal Strength Indication (WL-RSSI) consists of predicting the localization of a particular device given the radio signals it receives. WL-RSSI methods are suitable for specific scenarios where Global Positioning System (GPS) is unstable or unavailable, such as indoor localization. Developing more efficient WL-RSSI methods is necessary to supplement GPS localization in such applications. Feedforward neural network trained by hybrid Particle swarm optimization and Gravitational search algorithm (FPG) is an optimization strategy that aims at better exploring the network weight-space when compared to methods such as Backpropagation (BP). Feedforward neural network trained by hybrid Particle swarm optimization and Gravitational search algorithm (FPG) is a kind of machine learning model with better exploring ability in the solution search space compared with conventional neural network training methods such as Backpropagation (BP). This article investigates a method to solve the slow convergence problem of conventional FPG and further improve its performance. Extreme Learning Machines (ELMs) are used to pre-train initial particles of the FPG (EP-FPG). This article also presents the application of EP-FPG to classification and regression WL-RSSI problems. Experimental results demonstrate that the proposed EP-FPG performs better on WL-RSSI problems than conventional FPG and BP.

In this paper, an improved approach integrating genetic algorithm and adaptive particle swarm optimization with feed forward neural networks for image compression is proposed. The hybrid genetic algorithm with a novel mutation strategy and particle swarm optimization is used to train the neural network to near global optimum weights and thresholds at first. Then the network is trained with gradient descending learning algorithm to obtain the optimal network parameters. Then, the trained network is applied to the image compression. Results show that at the same compression rate the application of optimized neural network in image compression will achieve better image quality compared with the application of traditional neural network.

The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the limitations of mobile devices. Offloading heavy data size to a remote node introduces the problem of additional delay due to transmission. Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. The proposed method uses a rank accuracy estimation model to decide the rank-1 value to be applied for the decomposition. Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. The proposed algorithm improved the response time, delay, number of offloaded tasks, throughput, and energy consumption of the IoT requests. The simulation is implemented with iFogSim and java programming language. The proposed method can be applied in smart cities, monitoring, health delivery, augmented reality, and gaming among others.

In this study, a new design approach using HFPSO algorithm, which is obtained by hybridization of firefly algorithm (FA) with particle swarm optimization (PSO) algorithm is presented for the adjustment of proportional-integral-derivative (PID) controller parameters in a buck converter. Using the proposed approach, it is aimed to improve the stability profile of the relevant system. In order to demonstrate the superiority of the proposed HFPSO-based PID (HFPSO/PID) controller, stability analyzes in time domain and frequency domain, and disturbance suppression analysis were performed comparatively. The results obtained from analyzes showed that the proposed HFPSO/PID controller is more efficient than the whale optimization algorithm (WOA) based PID (WOA/PID) controller and genetic algorithm (GA) based PID (GA/PID) controller to improve the control performance of the buck converter.

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability