Hybrid Particle Swarm Genetic Algorithm Research Articles

Design Optimization of a Plate‐Fin Heat Exchanger With Metaheuristic Hybrid Algorithm

ABSTRACTThis paper introduces a novel approach to enhance the heat transfer efficiency of a plate‐fin heat exchanger. The metaheuristic hybrid approach combines particle swarm optimization (PSO) with the genetic algorithm (GA). Seven critical design parameters are used as variables. The research demonstrates the effectiveness of this hybrid method through a case study based on existing literature. The numerical results show the superior performance of the hybrid genetic algorithm particle swarm optimization over conventional GA and PSO techniques. The hybrid method achieves an optimal configuration with increased accuracy in a shorter computational time, offering significant time and cost savings in the design process.

Optimal Sizing, Energy Balance, Load Management and Performance Analysis of a Hybrid Renewable Energy System

This work utilizes the particle swarm optimization (PSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two-step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained are then compared with results from the Non-dominant Sorting Genetic Algorithm II (NGSA-II), hybrid genetic algorithm–particle swarm optimization (GA-PSO) and the proprietary derivative-free optimization algorithm. An energy management system monitors the energy balance and ensures that the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consists of solar panels (151), wind turbine (3), inverter (122) and batteries (31). This results in a minimized TEC, TAC and LCOE of USD 469,200, USD 297,100 and 0.007/kWh, respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient conditions.

Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography.

During robot-assisted rehabilitation, failure to recognize lower limb movement may efficiently limit the development of exoskeleton robots, especially for individuals with knee pathology. A major challenge encountered with surface electromyography (sEMG) signals generated by lower limb movements is variability between subjects, such as motion patterns and muscle structure. To this end, this paper proposes an sEMG-based lower limb motion recognition using an improved support vector machine (SVM). Firstly, non-negative matrix factorization (NMF) is leveraged to analyze muscle synergy for multi-channel sEMG signals. Secondly, the multi-nonlinear sEMG features are extracted, which reflect the complexity of muscle status change during various lower limb movements. The Fisher discriminant function method is utilized to perform feature selection and reduce feature dimension. Then, a hybrid genetic algorithm-particle swarm optimization (GA-PSO) method is leveraged to determine the best parameters for SVM. Finally, the experiments are carried out to distinguish 11 healthy and 11 knee pathological subjects by performing three different lower limb movements. Results demonstrate the effectiveness and feasibility of the proposed approach in three different lower limb movements with an average accuracy of 96.03% in healthy subjects and 93.65% in knee pathological subjects, respectively.

Auto-setting multi-soliton temporal spacing in a fiber laser by a hybrid GA-PSO algorithm.

Multi-soliton operation in fiber lasers is a promising platform for the investigation of soliton interaction dynamics and high repetition-rate pulse. However, owing to the complex interaction process, precisely manipulating the temporal spacing of multiple solitons in a fiber laser is still challenging. Herein, we propose an automatic way to control the temporal spacing of multi-soliton operation in an ultrafast fiber laser by a hybrid genetic algorithm-particle swarm optimization (GA-PSO) algorithm. Relying on the intelligent adjustment of the electronic polarization controller (EPC), the on-demand temporal spacing of the double solitons can be effectively achieved. In particular, the harmonic mode locking with equal temporal spacing of double solitons is also obtained. Our approach provides a promising way to explore nonlinear soliton dynamics in optical systems and optimize the performance of ultrafast fiber lasers.

Operational stability control of a buried pipeline maintenance robot using an improved PSO-PID controller

Ensuring the structural safety of buried pipelines is critical for urban areas due to their capacity for transporting important liquid supplies and wastes. Due to the dangerous (i.e., harmful gas) and narrow environment of pipelines, using robots instead of manual work has become an excellent solution to maintain the pipelines. However, because of the poor perception (i.e., vision and global positioning system signal), the robots controlled manually are easily yawed or even overturned, resulting in an unstable and dangerous maintaining process. Therefore, our research aims to address the following research question: How can we evaluate and improve the operational stability of pipeline maintenance robots in the poor-perception pipeline environment? Aiming to address this question, this paper (i) proposes a framework of operational stability and its control strategies to evaluate and reduce the robot’s overturning probability, (ii) analyzes qualitatively the operational stability and its influential factors using zero-point moments and fuzzy theories, and (iii) implements a hybrid genetic algorithm-particle swarm optimization (GA-PSO) and proportional integral derivative (PID) controller to improve the operational stability of the buried pipeline robot. The proposed framework is simulated by the case of the Dadong Lake deep sewage tunnel system in Wuhan and its maintenance robot. Compared with the ZN-PID and BPSO-PID methods, this method can improve tracking accuracy by 88.9% and 78.6% during verification, respectively. Furthermore, laboratory-level experiments using a scaled-down robot are carried out to validate the effectiveness of the controller by using it in practice. The results of simulations and experiments show that the buried pipeline robots can be effectively controlled by the proposed framework.

Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm

PurposeThree-axis accelerometers play a vital role in monitoring the vibrations in aircraft machinery, especially in variable flight temperature environments. The sensitivity of a three-axis accelerometer under different temperature conditions needs to be calibrated before the flight test. Hence, the authors investigated the efficiency and sensitivity calibration of three-axis accelerometers under different conditions. This paper aims to propose the novel calibration algorithm for the three-axis accelerometers or the similar accelerometers.Design/methodology/approachThe authors propose a hybrid genetic algorithm–particle swarm optimisation–back-propagation neural network (GA–PSO–BPNN) algorithm. This method has high global search ability, fast convergence speed and strong non-linear fitting capability; it follows the rules of natural selection and survival of the fittest. The authors describe the experimental setup for the calibration of the three-axis accelerometer using a three-comprehensive electrodynamic vibration test box, which provides different temperatures. Furthermore, to evaluate the performance of the hybrid GA–PSO–BPNN algorithm for sensitivity calibration, the authors performed a detailed comparative experimental analysis of the BPNN, GA–BPNN, PSO–BPNN and GA–PSO–BPNN algorithms under different temperatures (−55, 0 , 25 and 70 °C).FindingsIt has been showed that the prediction error of three-axis accelerometer under the hybrid GA–PSO–BPNN algorithm is the least (approximately ±0.1), which proved that the proposed GA–PSO–BPNN algorithm performed well on the sensitivity calibration of the three-axis accelerometer under different temperatures conditions.Originality/valueThe designed GA–PSO–BPNN algorithm with high global search ability, fast convergence speed and strong non-linear fitting capability has been proposed to decrease the sensitivity calibration error of three-axis accelerometer, and the hybrid algorithm could reach the global optimal solution rapidly and accurately.

Lévy-flight moth-flame optimisation algorithm-based micro-grid equipment sizing: An integrated investment and operational planning approach

Bridging the gap between simulation and reality for successful micro-grid (MG) implementation requires accurate mathematical modelling of the underlying energy infrastructure and extensive optimisation of the design space defined by all possible combinations of the size of the equipment. While exact mathematical optimisation approaches to the MG capacity planning are highly computationally efficient, they often fail to preserve the associated problem nonlinearities and non-convexities. This translates into the fact that the available MG sizing tools potentially return a sub-optimal (inferior) MG design. This brings to light the importance of nature-inspired, swarm-based meta-heuristic optimisation algorithms that are able to effectively handle the nonlinear and non-convex nature of the MG design optimisation problem – and better approximate the globally optimum solution – though at the expense of increased computational complexity. Accordingly, this paper introduces a robust MG capacity planning optimisation framework based on a state-of-the-art meta-heuristic, namely the Lévy-flight moth-flame optimisation algorithm (MFOA). An intelligent linear programming-based day-ahead energy scheduling design is, additionally, integrated into the proposed model. A case study is presented for a real grid-tied community MG in rural New Zealand. A comparison of the modelling results with those of the most popular tool in the literature and industry, HOMER Pro, verifies the superiority of the proposed meta-heuristic-based MG sizing model. Additionally, the efficiency of the Lévy-flight MFOA is compared to nine well-established meta-heuristics in the MG capacity planning literature. The comparative analyses have revealed the statistically significant outperformance of the Lévy-flight MFOA to the examined meta-heuristics. Notably, its superiority to the original MFOA, the hybrid genetic algorithm-particle swarm optimisation, and the ant colony optimiser, by at least ~6.5%, ~8.4%, and ~12.8%, is demonstrated. Moreover, comprehensive capital budgeting analyses have confirmed the financial viability of the test-case system optimised by the proposed model.

Study on prediction model of diesel engine with regulated two-stage turbocharging system based on hybrid genetic algorithm-particle swarm optimization method at different altitudes

ABSTRACT Aiming at the problem of complicated mechanism modeling for regulated two-stage turbocharging (RTST) system of diesel engine, a hybrid genetic algorithm–particle swarm optimization (GA-PSO) method was applied to optimize back propagation neural network (BPNN) performance prediction model of diesel engine. First, using a high-altitude (low-pressure) simulation test bench for diesel engines, a full-load performance test of RTST diesel engine at different altitudes was carried out, and the regulation rules of diesel engine intake characteristics and two-stage turbocharger (TST) matching characteristics with altitude were obtained. Based on the test data, the BPNN is used to establish the boost pressure prediction model of the RTST system of the diesel engine. For the problem of slow convergence and low prediction accuracy of the BPNN, adaptive improvement measures such as momentum and learning rate are introduced, and the GA-PSO optimizes the weights and thresholds of BPNN. The performance comparison shows that hybrid GA-PSO is superior to particle swarm optimization (PSO) and genetic algorithm (GA) in terms of evolution speed and convergence accuracy. Genetic algorithm–particle swarm optimization–back propagation neural network (GA-PSO-BPNN) model has higher prediction accuracy and higher accuracy than particle swarm optimization–back propagation neural network (PSO-BPNN) model and genetic algorithm–back propagation neural network (GA-BPNN) model. Compared with the original model, the root-mean-square error of the model is reduced by 50%, the mean error percentage (MEP) is reduced by 38%, and the sum of the squared errors is reduced by 79%. The performance verification of the GA-PSO-BPNN model shows that the MEP of the model prediction results is 5.5%, and the GA-PSO-BPNN model has higher prediction accuracy.

Partial Disassembly Line Balancing Problem Analysis Based on Sequence-Dependent Stochastic Mixed-Flow

Abstract Given the characteristics of decommissioning product diversity, wear difference, and structural complexity, we analyzed the disassembly line balance problems of minimum disassembly workstation, station load balancing, priority disassembly of high-demand parts and the lowest cost of invalid operations, and further considered the influence of random factors on actual disassembly operations. The purpose of this paper is to establish a model of sequence-dependent stochastic mixed-flowed partial disassembly line balancing problem and propose an adaptive hybrid particle swarm genetic algorithm to solve the model. The algorithm replaces the fixed value genetic operator with an adaptive crossover and mutation operator to improve the global optimization ability. The introduction of adaptive weighted particles leads to improved local optimization ability of the algorithm so that the algorithm has strong global and regional optimization ability to improve algorithm accuracy. The effectiveness of the proposed algorithm is verified by solving the classic disassembly line balancing problem with different scales and comparing it with the solution results of underlying genetic and particle swarm optimization algorithm. Meanwhile, the proposed model and algorithm are applied to the mixed-flow disassembly engineering project of a 25-task reducer. The results indicate that the proposed model is superior to the control group in terms of minimal disassembly workstation, station load balancing, invalid operation cost, and overall performance of the disassembly line, which verifies the validity of the model.

Multi-AGV scheduling for conflict-free path planning in automated container terminals

Path planning and integrated scheduling are two important problems to be resolved in the design of any automated container terminal. There has been relatively little research, however, on automated guided vehicles (AGVs) conflict-free path planning with quay cranes (QCs) and rail-mounted gantry (RMG) cranes. This paper combines the two problems to realize the integrated scheduling of multi-AGV with conflict-free path planning. A mixed integer programming model based on path optimization, integrated scheduling, and conflicts and deadlocks is established to minimize AGVs delay time under the condition that the task allocation is known. A series of small-scale and large-scale experiments are conducted to validate the availability of Hybrid Genetic Algorithm-Particle Swarm Optimization (HGA-PSO) with fuzzy logic controller to adaptive auto tuning. Dynamic simulation of the path nodes indicates that the proposed model indeed can resolve the AGV conflict and deadlock problem and may be practically applicable to existing automated container terminals.

Novel Naphtha Molecular Reconstruction Process Using a Self-Adaptive Cloud Model and Hybrid Genetic Algorithm–Particle Swarm Optimization Algorithm

Naphtha is an important product of crude oil and has widespread industrial applications. Additionally, the composition of naphtha is influenced by the procurement and operations of crude oil, which...

Design and energy management optimization for hybrid renewable energy system- case study: Laayoune region

The major objectives of this work are: 1) to develop new efficient optimization algorithm to solve NP-hard problems, 2) to show the potential of integrating renewable energy technologies for Laayoune region- Morocco taken as case study. In this work, new parallel hybrid Genetic Algorithm-Particle Swarm Optimization algorithm (P-GA-PSO) is developed to solve both sizing and energy management problems for micro-grids. The studied micro-grid is composed of four different renewable energy technologies and energy storage system. The objectives of both optimization problems are to satisfy typical load demand, to minimize energy production cost, to maximize renewable energy integration, to avoid energy losses and overload. P-GA-PSO performances are evaluated and compared with the ordinary optimization methods based on: 1) a set of benchmark functions, 2) various scenarios used to solve the proposed micro-grid optimization problems. Obtained results demonstrated that P-GA-PSO outperforms ordinary optimization algorithms in terms of convergence time and solution quality. Moreover, the proposed micro-grid is very promising in terms of load demand satisfaction, cost and pollutant emissions reduction. Indeed, the obtained energy cost does not exceed 0.17 US$/kWh, which is close to fossil fuel energy cost, and fossil fuel replacement rate exceeds 50% during all periods.

Mountain railway alignment optimization using stepwise & hybrid particle swarm optimization incorporating genetic operators

Optimizing railway alignments is a quite complex and time-consuming engineering problem. The huge continuous search space, complex constraints, implicit objective function and infinite potential alternatives of this problem pose many challenges. Especially in mountainous regions, finding a near-optimal alignment for extremely complex terrain and constraints is a most arduous task, which cannot be solved satisfactorily with most existing methods. In this study, a stepwise & hybrid particle swarm-genetic algorithm is developed for railway alignment optimization in mountainous regions. It is a continuous search method suitable for railway alignment design. A stepwise horizontal–vertical–integral approach which defines the horizontal and vertical alignments as two kinds of particles, is proposed to solve the three-dimensional railway alignment optimization problem. To enhance the initial diversity and momentum, butterfly-shaped areas are preset on a path generated with a bidirectional distance transform for initializing horizontal particles. For the solution method, specific genetic operators, including roulette wheel selection, four crossovers and two mutations are integrated into the stepwise particle swarm method to address parameter-dependent performance and avoid premature convergence. In addition, a cubic polynomial weight update strategy is employed for thoroughly searching the problem space. This synthesis method has been applied to a real-world case in a very mountainous region. The detailed data analyses demonstrate that it can offer more promising solutions compared with alternatives designed by experienced designers and those generated with a genetic algorithm or non-stepwise particle swarm algorithm.

An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

Abstract Properties prediction of crude oil remains an essential issue for refineries. In this communication, an exhaustive and extendable support vector machine (SVM) intelligent prediction process has been proposed to solve this problem. A novel hybrid genetic algorithm-particle swarm optimization (GA-PSO) method was applied to optimize the SVM model. The optimization process and result demonstrated that the newly proposed GA-PSO-SVM method was more accurate and time-saving than the classical GA or PSO method. Compared with the classical Grid-search SVM, the combined GA-PSO-SVM model appeared to be more applicable for the properties prediction task. The TBP distillation curve fitting was exampled to evaluate the performance of the developed model. The regression result demonstrated the high accuracy and efficiency of the proposed process. The model can be applied in the Industrial Internet as a plug-in, and the adaptability and flexibility is demonstrated by the implement of crude oil molecular reconstruction employing the intelligent prediction process.

A Comparative Study of Genetic and Particle Swarm Optimization Algorithms and Their Hybrid Method in Water Flooding Optimization

Optimization of oil production from petroleum reservoirs is an interesting and complex problem which can be done by optimal control of well parameters such as their flow rates and pressure. Different optimization techniques have been developed yet, and metaheuristic algorithms are commonly employed to enhance oil recovery projects. Among different metaheuristic techniques, the genetic algorithm (GA) and the particle swarm optimization (PSO) have received more attention in engineering problems. These methods require a population and many objective function calls to approach more the global optimal solution. However, for a water flooding project in a reservoir, each function call requires a long time reservoir simulation. Hence, it is necessary to reduce the number of required function evaluations to increase the rate of convergence of optimization techniques. In this study, performance of GA and PSO are compared with each other in an enhanced oil recovery (EOR) project, and Newton method is linked with PSO to improve its convergence speed. Furthermore, hybrid genetic algorithm-particle swarm optimization (GA-PSO) as the third optimization technique is introduced and all of these techniques are implemented to EOR in a water injection project with 13 decision variables. Results indicate that PSO with Newton method (NPSO) is remarkably faster than the standard PSO (SPSO). Also, the hybrid GA-PSO method is more capable of finding the optimal solution with respect to GA and PSO. In addition, GA-PSO, NPSO, and GA-NPSO methods are compared and, respectively, GA-NPSO and NPSO showed excellence over GA-PSO.

A production inventory model with price discounted fuzzy demand using an interval compared hybrid algorithm

Abstract An economic production quantity (EPQ) model in an imprecise environment is proposed, where the production rate, planning horizon and demand coefficients are fuzzy in nature. At the beginning of each cycle a price discount is offered for a period to boost the demand. During this period, demand increases with time depending upon the amount of discount. Here, demand also depends on the unit selling price. After withdrawal of the price discount, demand depends only on the unit selling price. The governing differential equation for the model is obviously fuzzy in nature as the production rate and demand are fuzzy. For this reason, the model is formulated using a fuzzy differential equation and the α -cut of the total profit from the planning horizon is obtained using fuzzy Riemann integration. To optimize the interval objective function, using a fuzzy preference relation on intervals and a fuzzy possibility/necessity measure, a hybrid algorithm with varying population size is developed by combining the features of particle swarm optimization (PSO) and a genetic algorithm (GA). This algorithm is named Interval Compared Hybrid Particle Swarm-Genetic Algorithm (ICHPSGA) and is used to find an optimal decision for the decision maker (DM) in different cases of the model. To test the efficiency of the algorithm, it is compared with two other established algorithms namely PSO and PSGA. Numerical experiments are performed to illustrate the model and some interesting observations are made.

A multi-balanced hybrid optimization technique to track objects using rough set theory

In this paper, the process of object detection and tracking is performed by means of five stages, namely frame segregation, shot segmentation, shape and texture feature extraction, object detection in frames through rough set theory and soft computing evolutionary programming with hybrid genetic algorithm particle swarm optimization. In the first stage, the input video file is segregated into number of frames and then the image frame from the specific shots is alone separated in the second stage with the help of DCT transformations. The third phase involves extracting shape and texture features from the shot segmented image frames.

Minimization of Grounding System Cost Using PSO, GAO, and HPSGAO Techniques

In this paper, three metaheuristic techniques have been developed to propose a safe and economic grounding system for the future power plant of Labreg situated in Khenchela City (400 km east of Algiers). The corresponding algorithms have been elaborated using particle swarm optimization (PSO), genetic algorithm optimization (GAO) and hybrid particle swarm genetic algorithm optimization (HPSGAO). The aim is to minimize the cost of the considered grounding system basing on the optimal decision of its construction and geometrical parameters in accordance with the security restrictions required by the ANSI/IEEE Standard 80-2000. A new mathematical model has been proposed for the cost function. This later includes the number of conductors, conductor dimension, grid depth, number of rods, length of rods, total area of excavation, and revetment. The results show that the HPSGAO technique presents lower values of the cost than those obtained using GAO and PSO methods. The good accordance between HPSGAO technique safety parameters and those of the CYMGrd code confirms the efficiency of the proposed algorithms.

Spike Detection from Electroencephalogram Signals with Aid of Hybrid Genetic Algorithm-Particle Swarm Optimization

Spike Detection from Electroencephalogram Signals with Aid of Hybrid Genetic Algorithm-Particle Swarm Optimization

Hybrid GA–PSO for optimal placement of static VAR compensators in power system

In recent years, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid genetic algorithm particle swarm optimization (HGAPSO) have attracted considerable attention among various modern heuristic optimization techniques. In this study the HGAPSO, PSO and GA optimization techniques are used for to search the optimal placement and sizing of static VAR compensator (SVC) in power system. The objective function is defined for reducing power loss, voltage deviation and investment costs of SVC. The effectiveness of the proposed hybrid based approach is applied and demonstrated on IEEE 30 Bus network. The results show that the proposed hybrid HGAPSO compared with PSO and GA optimization for performs and giving better solution.