Parallel Genetic Algorithm Research Articles

An asymmetric pinching damaged hysteresis model for glubam members: Parameter identification and model comparison

The performance of glue laminated bamboo (glubam) members is governed by the nonlinear response at their joints, where high deformation levels and stress concentrations are developed. Numerous phenomenological models are presently employed to describe the hysteresis behavior of these joints, while these models always have an excessive number of parameters, and the physical interpretation of these parameters is often challenging. Moreover, some hysteresis models cannot capture all hysteresis features such as asymmetry, pinching, and damage. Consequently, this paper introduces a novel phenomenological-based hysteretic model named Asymmetric Pinching Damaged (APD) model, and implemented it in Abaqus by combining connector and spring elements in series or parallel. This model encompasses asymmetry, pinching, and strength degradation for bamboo joint components, with parameters that possess clear physical meanings and are readily comprehensible. This study also presented a parameter identification framework coupling the Parallel Genetic Algorithm (PGA) and Bayesian Neural Network (BNN). By merging the FE modeling and optimizing algorithms with the interactive application of ABAQUS and Python software platforms, the integrated identification framework is capable of performing multi-threaded parallel computation of finite element models considering the BNN-based uncertainty quantification, thus greatly improving the efficiency of parameter identification.

Heat Pump-Assisted Extractive Pressure-Swing Distillation with Integrated Feed Preconcentration/Solvent Recovery for Isopropanol-Benzene-Water Separation

Recently, heat-integrated pressure-swing distillation (HIPSD) has been explored for recovering isopropanol and benzene from wastewater, but the process remains highly energy-intensive. Given the dilute nature of the feed, intensified extractive distillation with an integrated preconcentration column (IED) is more suitable. However, previous researches have often overlooked the critical role of pressure and lacked rigorous optimization of operating pressure in such systems. In this article, we developed novel extractive pressure-swing distillation with integrated feed preconcentration/solvent recovery column (IEPSD) by introducing a pressure-swing configuration and incorporate energy-saving technologies to achieve more sustainable and cost-effective separation processes. First, thermodynamic analysis is conducted to explore the effect of pressure on extractive pressure-swing distillation. Then, a parallel genetic algorithm is applied for rigorous optimization, followed by the application of heat integration and heat pump. The IEPSD is superior to IED in terms of total annual cost (TAC) and CO2 emission. With the inclusion of energy-saving technologies, IEDHI and IEPSDHI further reduced TAC by 5.29% and 7.40%, and cut CO2 emissions by 25.18% and 24.03%, respectively, compared to their base processes. The use of a heat pump is particularly advantageous for IEPSD due to its lower boiling temperatures under vacuum pressure, leading to the development of IEPSDHIHP, which offered an additional 22.36% CO2 reduction and marginally lower TAC than IEPSDHI. Ultimately, IEPSDHIHP proves to be the best option, offering a 51.15% TAC reduction and 68.42% CO2 emissions reduction compared to HIPSD. In summary, the developed IED and IEPSD processes, especially with heat integration and heat pump technologies, offer significant economic and environmental advantages over conventional HIPSD.

A Parallel Genetic Algorithm With Variable Neighborhood Search for the Vehicle Routing Problem in Forest Fire-Fighting

A Parallel Genetic Algorithm With Variable Neighborhood Search for the Vehicle Routing Problem in Forest Fire-Fighting

Glubam roof truss with riveted glubam connections adopting thin-walled steel tube: Experiment, modeling, and model-updating

Bio-based laminated structures offer a timely solution to meet the pressing demand for resource-efficient and environmentally responsible construction practices in civil engineering. Riveted connections are a kind of commonly used joint and significantly impact the mechanical behaviors of global structure. However, existing studies on the complex behavior of such joints for bamboo-based structures are insufficient. This study investigated the hysteretic behavior of riveted Glued Laminated Bamboo (glubam) connections using thin-walled steel tube and corresponding planar roof truss with thin-walled steel tube connections by experimental and numerical methods. Firstly, cyclic tests were conducted on the riveted glubam connections with thin-walled steel tube to investigate their hysteretic behavior. The test matrix encompasses four distinct connection configurations, carefully selected to assess the connection behavior of glubam. After the investigation of connection behavior, cyclic tests were further conducted on glubam planar roof truss structures to evaluate their global and local hysteretic behavior. Subsequently, the numerical hysteretic models for joints and hybrid roof trusses were developed within the OpenSeesPy framework. To accurately and efficiently calibrate the model parameters, a novel parallel genetic algorithm (PGA) was proposed to carry out an initial calibration on model parameters. Based on PGA, a more comprehensive model updating framework combining the neural network, prior knowledge and PGA was developed to obtain optimal parameters. Finally, the numerical models were successfully validated against the experimental data, demonstrating the effectiveness of the established numerical model and model-updating framework.

Optimal capacity configuration and operation strategy of typical industry load with energy storage in fast frequency regulation

As the potential and competent load-side resources for frequency response and control in modern power grids, typical industrial load can compensate for the deficiency of frequency response capability for new-type power systems. However, the operational flexibility is seriously enforced by the operation conditions uncertainties of industrial load. With “Online Calculation, and Real-time Matching” as the core, based on fuzzy mathematical theory, the coordinated operation strategy of typical industrial loads and energy storage systems (ESS) is proposed to finish fast frequency regulation (FFR) tasks. And an optimal capacity configuration model of industrial loads with ESSs is established to evaluate the whole economic profitability in FFR. Specially, in terms of algorithms, a novel Two-layer Parallel Particle Swarm Optimization and Genetic Algorithm (TPPSGA) is innovatively designed in this manuscript. Comparing with traditional heuristic algorithms, TPPSOGA improves computation speed by 6.84 times. Simulation results show that our proposed strategy can reasonably estimate the frequency capability of industrial load by membership degree function, and prove that the industrial load participating in FFR with the support of ESSs is able to get more revenue and stability than industrial load or ESSs separately is in regulation of FFR. The economic analyze in market price and ESS parameters can help the load agent to screen the suitable-type ESS and its capacity to serve for FFR and enhance the flexibility in load-side frequency regulation.

Hyperparameter optimization of orthogonal functions in the numerical solution of differential equations

Numerical methods for solving differential equations often rely on the expansion of the approximate solution using basis functions. The choice of an appropriate basis function plays a crucial role in enhancing the accuracy of the solution. In this study, our aim is to develop algorithms that can identify an optimal basis function for any given differential equation. To achieve this, we explore fractional rational Jacobi functions as a versatile basis, incorporating hyperparameters related to rational mappings, Jacobi polynomial parameters, and fractional components. Our research develops hyperparameter optimization algorithms, including parallel grid search, parallel random search, Bayesian optimization, and parallel genetic algorithms. To evaluate the impact of each hyperparameter on the accuracy of the solution, we analyze two benchmark problems on a semi‐infinite domain: Volterra's population model and Kidder's equation. We achieve improved convergence and accuracy by judiciously constraining the ranges of the hyperparameters through a combination of random search and genetic algorithms. Notably, our findings demonstrate that the genetic algorithm consistently outperforms other approaches, yielding superior hyperparameter values that significantly enhance the quality of the solution, surpassing state‐of‐the‐art results.

Parallel dual adaptive genetic algorithm: A method for satellite constellation task assignment in time-sensitive target tracking

The evolution of satellite surveillance technology, bolstered by advanced onboard intelligent systems and enhanced attitude maneuver capabilities, has thrust mission scheduling and execution into the spotlight as a prominent and dynamic research field in recent years. As the demand intensifies for mission scheduling and execution to transition from static ground targets to time-sensitive moving targets, conventional scheduling methods often fall short of delivering satisfactory results for continuously tracking these dynamic targets with constellation. The paper introduces a rapid yet efficacious satellite constellation task assignment method, termed the Parallel Dual Adaptive Genetic Algorithm (PDA-GA), for the task assignment of multiple moving target tracking. Specifically, the dual adaptive mechanism isolates the genetic algorithm’s sensitivity to parameters, while the parallel mechanism increases the evolutionary process’s computation speed by deploying complex computations to the GPU. Based on the meticulous analysis of the relevant factors that need to be considered in real tracking scenarios, the proposed PDA-GA can improve the search quality and efficiency of the task assignment solution. We conduct an extensive array of contrast and ablation experiments to showcase the performance and efficiency of PDA-GA in conjunction with autonomous attitude control algorithms across five simulated tracking scenarios. Furthermore, to enable high-fidelity simulation of tracking scenarios, we introduce the Constellation Target Tracking Environment (CTTE), which is equipped with a physics engine and algorithms for multi-satellite task assignment and single-satellite attitude control. This endeavor lays a foundation for future research endeavors focused on autonomous tracking of multiple time-sensitive moving targets within large-scale constellation.

Modeling of glubam roof truss, parameter identification and updating based on parallel genetic algorithm

This research introduces an innovative approach to the design and simulation of bio-based laminated structures, specifically focusing on glue-laminated bamboo (glubam) used in roof trusses. Our study fills a critical gap by investigating the mechanical behaviors of bolted connections in bamboo-based structures, which have not been comprehensively studied before. We employ a dual-phase methodology: initially, cyclic tests on bolted steel to glubam joints assess their hysteretic behavior, followed by tests on glubam planar roof trusses to evaluate structural responses under practical conditions. Our novel contribution is the development of a simplified mechanical-based hysteretic model, incorporating connector and spring elements in series or parallel within the ABAQUS software. This model significantly improves on existing models by allowing for initial calibration through a parallel genetic algorithm (PGA), enhancing both accuracy and efficiency. Subsequent incorporation of this model into the simulation of truss joints enabled the creation of an advanced hybrid roof truss model within ABAQUS. The final stage of our research demonstrates the application of a PGA-based model-updating framework, which substantially increases the model’s predictive accuracy. This work not only advances the understanding of structural behavior in bio-based construction materials but also introduces a robust framework for model updating that can be applied to other engineering simulations, contributing to more sustainable and resource-efficient construction practices.

Bio-based connections and hybrid planar truss: A parallel genetic algorithm approach for model updating

Bolted steel to laminated bio-based material connections experience significant performance challenges due to the nonlinear response and high stress concentrations at their joints. This paper introduces an innovative 3D plasticity-fracture continuum Finite Element (FE) model that significantly advances the simulation of such truss joints by integrating Hill's yielding criteria with an element removal methodology for fracture simulation. This novel approach captures both plastic and fracture behaviors simultaneously, a capability not sufficiently addressed in existing models. We detail the theoretical framework for these models, including the derivation of constitutive equations and the algorithms necessary for their implementation in ABAQUS. Additionally, it is provided a low-fidelity modeling of truss joints, offering a comprehensive analysis of connector elements, joint models, and parametric modeling via Python scripting. The model's efficacy is demonstrated through identification of connection and of hybrid planar trusses under cyclic loading, which validates the practical applicability of the method. To optimize computational efficiency, we developed a Parallel Genetic Algorithm (PGA) that integrates seamlessly with ABAQUS and Python to facilitate parameter calibration. This integration not only enhances the model's accuracy but also reduces computational load, making it feasible for complex engineering applications. Our findings illustrate a significant improvement in modeling accuracy and efficiency, establishing a robust methodology for analyzing truss joints in bio-based construction materials.

Introducing a Parallel Genetic Algorithm for Global Optimization Problems

The topic of efficiently finding the global minimum of multidimensional functions is widely applicable to numerous problems in the modern world. Many algorithms have been proposed to address these problems, among which genetic algorithms and their variants are particularly notable. Their popularity is due to their exceptional performance in solving optimization problems and their adaptability to various types of problems. However, genetic algorithms require significant computational resources and time, prompting the need for parallel techniques. Moving in this research direction, a new global optimization method is presented here that exploits the use of parallel computing techniques in genetic algorithms. This innovative method employs autonomous parallel computing units that periodically share the optimal solutions they discover. Increasing the number of computational threads, coupled with solution exchange techniques, can significantly reduce the number of calls to the objective function, thus saving computational power. Also, a stopping rule is proposed that takes advantage of the parallel computational environment. The proposed method was tested on a broad array of benchmark functions from the relevant literature and compared with other global optimization techniques regarding its efficiency.

An Improved Parallel Biobjective Hybrid Real-Coded Genetic Algorithm with Clustering-Based Selection

Abstract This work presents an improved parallel biobjective hybrid real-coded genetic algorithm (MORCGA-MOPSO-II). The approach is based on the combined use of the parallel Multi-Objective Real-Coded Genetic Algorithm (MORCGA) and the Multi-Objective Particle Swarm Optimization (MOPSO). At the same time, clustering-based selection techniques are used to form subpopulations of parent individuals. Using well-known clustering algorithms (e.g., k-Means, hierarchical clustering, c-means, and DBSCAN) in combination with the proposed clustering-based mutation (the CL-mutation) directed toward the obtained cluster centers allows for improving the quality of the Pareto fronts’ approximations. The results of the MORCGA-MOPSO-II were compared with other well-known multi-objective evolutionary algorithms (e.g., SPEA2, NSGA-II, FCGA, MOSPO, etc.). Moreover, the MORCGA-MOPSO-II was integrated with the previously developed agent-based model of a goods exchange through the objective functions. As a result, the Pareto fronts have been obtained for the agent-based model of a goods exchange in different configurations of the initial distribution of agents.

Parallel Genetic Algorithm Interface II: A novel computational tool for accelerated simulation-based optimization

The ever increasing power of computational tools encouraged by the general need for development of more sustainable technologies fuels the interest in modern optimization approaches. While simulation-based optimization has been receiving considerable attention in the past decades, it still struggles to overcome some challenges, namely excessive computation time. This study proposes a novel optimization interface, the Parallel Genetic Algorithm Interface II (PAGAN-II), which utilizes parallelization of flowsheet simulations to drastically reduce the optimization time without the need to use clustered CPUs and/or modified optimization algorithms. Results of a detailed performance study showed up to 2100% increase in computation rate when optimizing demanding process flowsheets; and approximately 300% increase when optimizing simple ones. Capabilities of the proposed interface were demonstrated by optimization of a 5 MTPA C3MR LNG technology processing 12 different feedstocks, where a 15–30% decrease in the specific energy consumption was achieved. At the same time, the algorithm increased the optimization speed 13-fold compared to the traditional approach. This translates into a reduction of optimization time from 69 days of non-stop computation to approximately 7 days.

Gate Assignment Algorithm for Airport Peak Time Based on Reinforcement Learning

In existing airport gate allocation studies, little consideration has been given to situations where gate resources are limited during peak periods. Under such circumstances, some flights may not be able to make regular stops. In this paper, the airport gate assignment problem under peak time is investigated. We propose a gate pre-assignment model to maximize the gate matching degree and the near gate passenger allocation rate. Besides, to minimize the pre-assignment gate change rate, we propose a dynamic reassignment model based on the pre-assignment model. By considering the non-deterministic polynomial hard (NP-hard) property of this problem, a gate assignment algorithm based on proximal policy optimization (GABPPO) is proposed. The simulation results show that the algorithm can effectively solve the gate shortage problem during the airport peak period. Compared with the adaptive parallel genetic, deep Q-network, and policy gradient algorithms, the target value of solutions obtained by the proposed algorithm in the near gate passenger allocation rate is increased by 5.7%, 3.6%, and 7.9%, respectively, and the target value in the gate matching degree is increased by 10.6%, 4.9%, and 11.5% respectively.

Probabilistic Chain-Enhanced Parallel Genetic Algorithm for UAV Reconnaissance Task Assignment

With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough solutions, they encounter substantial challenges in addressing large-scale task assignments due to their extensive computational demands. Conversely, while heuristic algorithms are capable of delivering satisfactory solutions with limited computational resources, they frequently struggle with converging on locally optimal solutions and are characterized by low iteration rates. In response to these limitations, this paper presents a novel approach: the probabilistic chain-enhanced parallel genetic algorithm (PC-EPGA). The PC-EPGA combines probabilistic chains with genetic algorithms to significantly enhance the quality of solutions. In our approach, each UAV flight is considered a Dubins vehicle, incorporating kinematic constraints. In addition, it integrates parallel genetic algorithms to improve hardware performance and processing speed. In our study, we represent task points as chromosome nodes and construct probabilistic connection chains between these nodes. This structure is specifically designed to influence the genetic algorithm’s crossover and mutation processes by taking into account both the quantity of tasks assigned to UAVs and the associated costs of inter-task flights. In addition, we propose a fitness-based adaptive crossover operator to circumvent local optima more effectively. To optimize the parameters of the PC-EPGA, Bayesian networks are utilized, which improves the efficiency of the whole parameter search process. The experimental results show that compared to the traditional heuristic algorithms, the probabilistic chain algorithm significantly improves the quality of solutions and computational efficiency.

Simultaneous scheduling of jobs and transporters in a hybrid flow shop with collision-free transporter routing: A novel parallel heuristic

A computationally potent two-stage parallel heuristic (TSPH) and a mixed integer linear programming (MILP) are suggested to address the simultaneous scheduling of jobs and transporters in a hybrid flow shop system, wherein multiple transporters, stage omission, transporter eligibility, machine eligibility, and collision-free transporter routing are assumed. To the best of our knowledge, the significance of transporter collision-free routing has not been spotlighted in the literature on flow shop systems. Collision-free routing of transporters is a requisite technical attribute as transport means may collide on routes and break down the whole system. Our MILP and TSPH assume collision-free routing to impede the issue. Being equipped with parallel computing, TSPH can deal with large problems in a considerably short time. To support, TSPH is analogized against the suggested MILP, two-step MILP (TSMILP), and an efficient parallel meta-heuristic (i.e., parallel particle swarm optimization and genetic algorithm (PPSOGA)) that outperformed many literarily prominent meta-heuristics in the literature. The benchmark results uncover that TSPH outclasses both TSMILP and PPSOGA in the quality of solutions. Eventually, utilizing the convergence plot and Nemenyi’s post-hoc procedure for Friedman’s test, it is revealed that the outcomes of TSPH are remarkably more desirable than those of TSMILP and PPSOGA.

A Comprehensive Analysis of Parallel Genetic Algorithms

The paper introduces an optimal approach for optimizing the idle time of processors through the utilization of parallel genetic algorithms. By harnessing the power of parallelism, we propose a method that effectively minimizes idle periods in processor operations. Our approach, outlined in detail within this paper, demonstrates significant improvements in resource utilization and efficiency. Through rigorous experimentation and analysis, we illustrate the efficacy of our parallel genetic algorithm in optimizing processor idle time. The findings presented herein offer promising insights into enhancing computational resource utilization, particularly in multi-processor systems. This research contributes to the ongoing efforts in maximizing computational efficiency and lays a foundation for further advancements in parallel optimization techniques. Keywords: Optimization; Metaheuristic; Parallel Genetic algorithm; Crossover; Mutation; Selection; Evolution;

Open-Pit Pushback Optimization by a Parallel Genetic Algorithm

Determining the design of pushbacks in an open-pit mine is a key part of optimizing the economic value of the mining project and the operational feasibility of the mine. This problem requires balancing pushbacks that have good geometric properties to ensure the smooth operation of the mining equipment and so that the scheduling of extraction maximizes the economic value by providing early access to the rich parts of the deposit. However, because of the challenging nature of the problem, practical approaches for finding the best pushbacks strongly depend on the expert criteria to ensure good operational properties. This paper introduces the Advanced Geometrically Constrained Production Scheduling Problem to account for operational space constraints, modeled as truncated cones of extraction. To find the best solution for this problem, we present a parallel genetic algorithm based on a genotype–phenotype model such that the genotype symbolizes the base block of a truncated cone, and the phenotype represents the cone itself. A central computer node evaluates these solutions, collaborating with various secondary nodes that evolve a population of feasible solutions. The PGA’s efficacy was validated using comprehensive test instances from established research. The PGA solution exhibited a consistent average copper grade across periods, with its incremental phases reflecting real-world mine geometry. Moreover, the benefits of the MeanShift clustering technique were evident, suggesting effective phase-based scheduling. The PGA’s approach ensures optimal resource utilization and offers insights into potential avenues for further model enhancements and fine-tuning.

Optimization of short-term hydropower scheduling with dynamic reservoir capacity based on improved genetic algorithm and parallel computing

The optimal scheduling of hydropower plants is important for the efficient operation and management of hydropower energy systems. In the short-term optimization scheduling of river-type reservoirs, the dynamic reservoir capacity is a key factor. Currently, the short-term power generation scheduling model considering dynamic reservoir capacity was solved by the enumeration method, providing a more accurate simulation of the water flow process compared to the model considering static reservoir capacity. However, the solving process had the disadvantage of a large computational burden and excessive time consumption, which restricted application in the actual scheduling of reservoirs. Therefore, to solve the time-consuming problem of repeated calculation of hydrodynamics model in optimization scheduling, an improved genetic algorithm including adaptive and elitist-selection strategies combined with the parallel computing technique was proposed. The optimal scheduling plan generated by this model were compared with the optimization results of the enumeration method and the genetic algorithm scheduling model. The results showed that the optimal power generation selected by the three models were extremely similar and the optimization model could find the optimal value for the scheduling period. In terms of computing speed, the improved genetic algorithm parallel technique took approximately 0.8 h, around 246 times faster than the enumeration method and 20 times faster than the model without using parallel computing. It demonstrates that the present algorithm is practically usable, significantly enhancing the optimization efficiency of scheduling solutions. Furthermore, it satisfies the engineering application efficiency requirements and can provide technical support for the subsequent power generation scheduling of the Three Gorges Reservoir.

Development of novel hybrid pre-separation/extractive reactive distillation processes for the separation of methanol/methyl acetate/ethyl acetate

The treatment of waste streams is an important research topic for the sustainable development of chemical process. Recently, reactive distillation-based (RD) processes with ethylene oxide hydrolysis have been developed to separate water-containing azeotropic mixtures. To separate Serafimov’s class 2.0–2b mixtures containing methanol/methyl acetate, a methyl acetate transesterification reaction between propylene glycol monomethyl ether is introduced to convert methyl acetate to methanol and coproduce produce propylene glycol monomethyl ether acetate as advanced solvents. Three-column reactive-extractive distillation (TCRED) and extractive-reactive distillation (TCERD), and double-column pre-separation-reactive distillation (DCPSRD) processes, are proposed. Then, we use a parallel genetic algorithm to optimize processes to maximize total net revenue. The case study is methanol/methyl acetate/ethyl acetate. Though TCRED is not suitable due to the occurrence of ethyl acetate transesterification reactions, to compare economic performances of three RD-based processes, TCRED is regarded as a pseudo process. The total net revenue of three RD-based processes is relatively larger than (∼20 % increase) that of the conventional extractive distillation process. Compared with TCRED process, TCERD and DCPSRD can achieve $582336 and $1045995 increase in TNR, 27.43 % and 49.99 % TAC reduction, respectively. In summary, proposed RD-based processes are promising to separate Serafimov’s class 2.0–2b mixtures containing methanol/methyl acetate, especially TCRED and DCPSRD.

Real-time task parameter selection method of accounting system based on multi-objective optimization and genetic algorithm.

The progress of the digital economy has promoted the enterprise accounting system. To accelerate the update and evolution of accounting systems, we propose a parameter selection method based on multi-objective optimization and genetic algorithm. Firstly, this article proposes an accounting feature extraction method based on multimodal information embedding. The dual-branch structure and feature pyramid network are used to realize the feature extraction of the information involved in accounting. Then, this article proposes a multi-objective parameter selection method based on a parallel genetic algorithm. By embedding a genetic algorithm in the process of dual-branch model training, the model's ability to sense accounting information is improved. Finally, using the above two methods, an accounting system evaluation method upon recurrent Transformer is proposed to improve the financial situation of enterprises. Our experiments have proven that our approach attains a remarkable performance with an 87.6% F-value, 83.5% mAP value, and 83.4% accuracy. These results position our method at an advanced level globally, showcasing its capability to enhance accounting systems.