Multi-population Genetic Algorithm Research Articles

Seismic performance of soft soil foundation with a new type of assembled wall tuned mass damper.

The design of tuned mass damper (TMD) parameters is influenced by the soil-structure-TMD coupling system; thus, it is important to consider the soil-structure interaction (SSI) for the vibration control effect of the TMD. Recently, the acquisition of TMD parameters considering soil-structure interactions has only remained at the theoretical stage, lacking relevant experimental verification. Traditional TMD face the problems of occupying a large building space, increasing construction costs, and non-replaceable components. In this study, an assembled wall-type damping TMD was designed. By comparing the dynamic response of the uncontrolled and controlled structures equipped with the newly assembled wall-type damping TMD in the shaking table test on a soft soil foundation, we analyzed whether the SSI effect was considered in the TMD design parameters on the damping effect of the newly assembled wall-type tuned mass damper. The TMD parameters optimized using the artificial intelligence algorithm were verified experimentally. The results indicated that the traditional TMD design parameters were discordant because the SSI effect was not considered. The SSI effect in the soil effectively reduces the dynamic response of the superstructure. By considering the SSI effect and improving the multi-population genetic algorithm, a wall-type damping TMD with optimized parameters can achieve a good damping effect.

Flexible job-shop scheduling problem with parallel batch machines based on an enhanced multi-population genetic algorithm

AbstractThe flexible job-shop scheduling problem (FJSP) with parallel batch processing machine (PBM) is one of those long-standing issues that needs cutting-edge approaches. It is a recent extension of standard flexible job shop scheduling problems. Despite their wide application and prevalence in practical production, it seems that current research on these types of combinatorial optimization problems remains limited and uninvestigated. More specifically, existing research mainly concentrates on the flow shop scenarios in parallel batch machines for job shop scheduling but few literature emphasis on the flexible job shop integration in these contexts. To directly address the above mentioned problems, this paper establishes an optimization model considering parallel batch processing machines, aiming to minimize the maximum completion time in operating and production environments. The proposed solution merges variable neighborhood search with multi-population genetic algorithms, conducting a neighborhood search on the elite population to reduce the likelihood of falling into local optima. Subsequently, its applicability was evaluated in computational experiments using real production scenarios from a partnering enterprise and extended datasets. The findings from the analyses indicate that the enhanced algorithm can decrease the objective value by as much as 15% compared to other standard algorithms. Importantly, the proposed approach effectively resolves flexible job shop scheduling problems involving parallel batch processing machines. The contribution of the research is providing substantial theoretical support for enterprise production scheduling.

Hybrid optimization algorithm for estimating soil parameters of spoil hopper deposition model for trailing suction hopper dredgers

Trailing suction hopper dredger is a kind of hydraulic dredger, it has the characteristics of self-propelled, selfloading, self-dredging, self-unloading, it is the main force in dredging and blowing works, it is widely used in the world, it can be said that where there is a big dredging project where there is a trailing suction hopper dredger’s figure. The loading optimization process of trailing suction hopper dredger contains a lot of dredging parameters related to soil type, and the soil type under different working conditions is not very clear. In this study, we present a hybrid optimization technique based on simulated annealing and multi-population genetic algorithm to enhance the loading efficiency of a trailing suction hopper dredger and to examine the variation of dredged soil parameters. The soil parameters of the spoil hopper deposition model were estimated using this hybrid optimization algorithm. The experimental results show that the soil parameters are successfully estimated and verified by our measured construction data of a trailing suction hopper dredger. In addition, our proposed method has the highest accuracy of soil parameter estimation, the fastest algorithm convergence, and excellent robustness compared to the other three intelligent optimization methods. In addition, our method successfully avoids the phenomenon of premature convergence that usually occurs in traditional genetic algorithms, and the parameters show strong adaptability to different vessels under the same dredging area.

Test data generation for covering mutation-based path using MGA for MPI program

Message Passing Interface (MPI) is a communication protocol used for parallel programming in various languages, valued for its reliability and broad applicability. Mutation testing is a software testing method for systematically simulating software faults. However, the significant number of inserted mutation branches in the program escalates the testing cost. To address this issue, we propose the comprehensive PMTDGM framework. The framework first generates mutation-based paths based on the relevancy of mutant branches and the difficulty of mutant branch coverage, followed by the establishment of a multitask model of path coverage. Finally, we employ a multi-population genetic algorithm (MGA) to generate test data. Our experiments, performed on six MPI programs of varying sizes and structures, demonstrate that the mutation-based paths have the small test sets and are easy to cover. Additionally, the multitask model and MGA can significantly improve the efficiency of generating test data and reduce the cost of mutation testing for MPI programs compared to traditional methods.

Intelligent optimization of axial-flow pump using physics-considering machine learning

Abstract To address the significant energy waste generated by axial flow pumps, this paper proposes an intelligent optimization method based on physics-considering machine learning. First, a highly parameterized geometric design theory is constructed using six featured variables to achieve a complete three-dimensional modeling of the blade geometry. Four hundred preliminary cases are studied using the computational fluid dynamics method with various combinations of these featured variables to obtain a preliminary solution. The best preliminary design has an efficiency of 83.33%, and a head of 5.495 m. To further improve this performance, this paper also presents a high-precision prediction model for the energy performance of axial flow pump based on back-propagation neural network and the encoding layers of random sampling and local feature aggregator network created. Afterwards, a multi-population genetic algorithm is used to quickly find the optimal solution within the prediction mode range. The algorithm achieved a highest efficiency of 86.373% and was validated by numerical simulation with a value of 86.057% and a prediction error of 0.316%. Compared with the preliminary solution, the efficiency of the optimized axial flow pump is increased by 1.615%, with a wider high-efficiency range and an optimal operating point closer to the design conditions. Overall, this intelligent optimization method has the potential to significantly reduce the design time of axial pumps and increase their performance.

A voltage control method for distribution networks based on TCN and MPGA under cloud edge collaborative architecture

A distribution network voltage control method based on TCN and MPGA under cloud edge collaborative architecture is proposed to address the issues of heavy computational burden and inability to balance economic efficiency in traditional decentralized and centralized voltage control methods. Firstly, under the cloud edge collaboration architecture, the power prediction model based on time convolutional networks is trained in the cloud, and the edge predicts the load demand and new energy active output in each region based on the TCN Attention power prediction model issued by the cloud. Then, considering safety and economy comprehensively, with the goal of minimizing the daily network loss and voltage regulation cost in the distribution network, the Pareto solution set for the optimal operation point of each voltage regulation equipment in the entire distribution network was solved based on an improved multi population genetic algorithm at the distribution cloud main station. Finally, the optimal distribution network voltage control scheme is obtained based on the Pareto solution set. Based on the simulation system, experimental testing was conducted on the proposed method, and the results showed that its daily voltage regulation cost and network loss were 863.4 yuan and 43.73 MW h, respectively. The average absolute percentage error of the prediction model was 4.51 %, which was superior to other comparative methods.

Orthotropic Material Parameters Identification Method of Stator Core and Windings in Electric Motors

The accurate equivalent orthotropic material parameters (OMPs) of stator core and windings are the basis of modal analysis in electric motors and the key to precisely predicting electromagnetic vibration and noise. A CNN (convolutional neural network)-MPGA (multi-population genetic algorithm) based OMPs identification method of stator core and windings for electric motors is proposed in this study. Firstly, the surrogate models between the material parameters and eigenmode frequencies are trained with CNN for stator core and windings. Then, MPGA is utilized to identify the equivalent OMPs of stator core and windings combined with tested eigenmode frequencies. Finally, two different classes of electric machines are selected to verify the method. The CNN-MPGA based method in this study can quickly and accurately identify OMPs of stator core and windings. It is significant for the calculation and suppression of electromagnetic noise for electric motors.

Unmanned surface vehicles (USVs) scheduling method by a bi-level mission planning and path control

To enhance the operational efficiency of container terminals, we propose a bi-level scheduling method that employs unmanned surface vehicles (USVs) to transport containers exclusively between different berths on the waterside of terminals. In the upper level, Considering time windows, berth coordination, energy replenishment, etc., we formulate the mission decision model to obtain a cost-effective USVs shipping solution by minimizing unplanned delay and total execution cost. In the lower level, considering path distance, path smoothness, and motion constraints, we develop the USV path control model to achieve the USV path planning and reduce path tracking errors. Finally, we integrate the advantages of the chaotic Electron Search and multi-population Genetic Algorithm (ES-mGA) to solve the bi-level USVs scheduling model. Experimental results demonstrate the effectiveness of our approach and algorithm in guiding the USVs to efficiently accomplish container transshipment and acquire stable USV sailing states.

A novel flow channel design to achieve high temperature homogenization in solid oxide fuel cell

The thermal stress, which has a significant impact on the stable and prolonged operation of Solid Oxide Fuel Cells (SOFCs), can be further aggravated by the presence of local hot spots. Improving the distribution uniformity of physical quantities is of great significance to improving the internal temperature distribution of the SOFC. In this study, to improve the uniformity of temperature distribution, the thermal and flow distribution of three kinds of flow fields are investigated including Z, U, and L flow fields. The results show that the temperature distribution is dominated by the flow rate distribution. To optimize the physical quantity distribution of the designed flow channel, a novel, and easy-to-manufacture thermal management is proposed. This method is to adopt the extended ridge for controlling the flow rate into each flow channel. Moreover, a multi-population genetic algorithm is adopted to optimize the length of each extended ridge. The optimized results show that the temperature deviation in the Z, U, and L flow fields are reduced by about 64.20 %, 26.06 %, and 23.45 % respectively. This novel thermal management method enables more uniform temperature distribution and it is expected to be used in large-scale manufacturing due to its simple but subtle design.

Machine learning-driven optimization design of hydrogel-based negative hydration expansion metamaterials

Hydrogel-based negative hydration expansion (NHE) metamaterials are composite structures composed of responsive hydrogels and polymers, and their properties depend on their unique structures. In this paper, an optimization method based on the combination of the back-propagation neural network (BPNN) and the multi-population genetic algorithm (MPGA) is developed to rapidly design isotropic and anisotropic hydrogel-based metamaterials with specific NHE effects. In this method, several dimensionless design parameters are introduced to describe the structural characteristics of the metamaterial. The initial dataset is constructed based on the finite element method simulation results, and the mapping relationship between the design parameters and the equivalent linear strain is constructed by the BPNN, and the metamaterial with specific effect is efficiently optimized by combining the MPGA. The method is proved to have high accuracy and efficiency, and is applied to design many novel 2D and 3D metamaterials. The 3D metamaterial designed by this method has an ultra-large NHE ratio about 82 %. Compared with the topology optimization method, this method can significantly reduce the amount of computation, and can effectively avoid falling into the local optimum. The results show that the optimization method based on machine learning is an efficient means to design hydrogel-based metamaterials.

Multi-population genetic algorithm with greedy job insertion inter-factory neighbourhoods for multi-objective distributed hybrid flow-shop scheduling with unrelated-parallel machines considering tardiness

Distributed manufacturing is gradually becoming the future trend. The fierce market competition makes manufacturing companies focus on productivity and product delivery. The hybrid flow shop scheduling problem (HFSP) is common in manufacturing. Considering the difference of machines at the same stage, the multi-objective distributed hybrid flow shop scheduling problem with unrelated parallel machines (MODHFSP-UPM) is studied with minimum makespan and total tardiness. An improved multi-population genetic algorithm (IMPGA) is proposed for MODHFSP-UPM. The neighbourhood structure is essential for meta-heuristic-based solving algorithms. The greedy job insertion inter-factory neighbourhoods and corresponding move evaluation method are designed to ensure the efficiency of local search. To enhance the optimisation ability and stability of IMPGA, sub-regional coevolution among multiple populations and re-initialisation procedure based on probability sampling are designed, respectively. In computational experiments, 120 instances (including the same proportion of medium and large-scale problems) are randomly generated. The IMPGA performs best in all indicators (spread, generational distance, and inverted generational distance), significantly outperforming existing efficient algorithms for MODHFSP-UPM. Finally, the proposed method effectively solves a polyester film manufacturing case, reducing the makespan and total tardiness by 40% and 60%, respectively.

General modeling and energy management optimization for the fuel cell electric tractor with mechanical shunt type

The fuel cell electric tractor has emerged as a new development direction in modern agriculture due to its energy-controllable and emission-reducing advantages. This paper focuses on improving the universality of basic research by studying the fuel cell electric tractor with mechanical shunt type (FCETMST). The bond graph model has the benefits of cross-physical system integration. To fill the gap in general modeling of electric tractors, an electromechanical hydraulic integrated machine model by utilizing the bond graph model, which includes energy system, drive system, lifting system and power take-off (PTO) system, is developed. The general power flow model of the electric tractor with mechanical shunt type is also derived. Lastly, the accuracy of the model is validated by analyzing the tractor’s traction characteristics. To further enhance energy efficiency, this paper proposes an energy management strategy (EMS) utilizing a multi-population genetic algorithm (MPGA) and compares it with a power following control strategy (PFCS) for a fuel cell/lithium battery composite energy system. The results base on the co-simulation by using AVL-Cruise and Matlab/Simulink to build a vehicle simulation model and EMS models, respectively, show that under ploughing and transportation conditions, compared with the traditional PFCS, using MPGA leads to reduce state of charge (SOC) fluctuations and the remaining SOC is 11.22 % and 6.1 % higher, respectively. The theoretical hydrogen fuel consumption is decreased by about 26.49 % and 36.21 %, respectively. This study can provide theoretical support for the modeling and energy management optimization of the electric tractor system.

Retracted: Application of Multipopulation Genetic Algorithm in Industrial Special Clothing Design

Retracted: Application of Multipopulation Genetic Algorithm in Industrial Special Clothing Design

Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm

Path planning is crucial in the scheduling and motion planning of multiple robots. However, solving multi-robot path-planning problems efficiently and quickly is challenging due to their high complexity and long computational time, especially when dealing with many robots. This paper presents a unified mathematical model and algorithm for the path planning of multiple robots moving from one formation to another in an area with obstacles. The problem was initially simplified by constructing a cost matrix, and then the route planning was achieved by integrating an elite enhanced multi-population genetic algorithm and an ant colony algorithm. The performance of the proposed planning method was verified through numerical simulations in various scenarios. The findings indicate that this method exhibits high computational efficiency and yields a minimal overall path distance when addressing the path-planning problem of a multi-robot formation reconstruction. As a result, it holds promising potential for the path-planning problem of a multi-robot formation reconstruction.

Research on Multi-AGV Task Allocation in Train Unit Maintenance Workshop

In the context of the continuous development and maturity of intelligent manufacturing and intelligent logistics, it has been observed that the majority of vehicle maintenance in EMU trains still relies on traditional methods, which are characterized by excessive manual intervention and low efficiency. To address these deficiencies, the present study proposes the integration of Automatic Guided Vehicles (AGVs) to improve the traditional maintenance processes, thereby enhancing the efficiency and quality of vehicle maintenance. Specifically, this research focuses on the scenario of the maintenance workshop in EMU trains and investigates the task allocation problem for multiple AGVs. Taking into consideration factors such as the maximum load capacity of AGVs, remaining battery power, and task execution time, a mathematical model is formulated with the objective of minimizing the total distance and time required to complete all tasks. A multi-population genetic algorithm is designed to solve the model. The effectiveness of the proposed model and algorithm is validated through simulation experiments, considering both small-scale and large-scale scenarios. The results indicate that the multi-population genetic algorithm outperforms the particle swarm algorithm and the genetic algorithm in terms of stability, optimization performance, and convergence. This research provides scientific guidance and practical insights for enterprises adopting task allocation strategies using multiple AGVs.

An Evolutionary Framework for Modelling Unknown Behaviours of Other Agents

Multiagent decision making becomes complicated when a subject agent interacts with other agents who could be either collaborative or competitive in their common environment. Generally the subject agent does not know what exact behaviours other agents will execute in the coming interactions. This occurs even when the agents are collaborative and cannot fully disclose their behaviours due to their privacy concerns. With the purpose of improving prediction of the true behaviours of other agents, conventional approaches often employ either an exhaust list or a guided search to expand the set of possible behaviours of other agents. In this article, we investigate an evolutionary approach guided by prior knowledge about other agents and develop a genetic algorithm based framework to modelling unknown behaviours, which may contain the true behaviours of other agents. We develop a new framework with a multi-population genetic algorithm in a general multiagent decision model - interactive dynamic influence diagrams, which represents how multiple agents optimise their interactive decisions from the viewpoint of individual agents. The new framework also provides an alternative way to solving the decision model. We demonstrate the performance of the proposed methods in two problem domains and provide empirical results in support.

Fuzzy Target Job Shop Scheduling Based on Improved Multi-population Genetic Algorithm with Clustering

Owing to the traditional genetic algorithm is difficult to hit the optimal solution in the optimization problem of fuzzy target shop scheduling, this paper converts the weighting of the two goals of processing completion time and customer satisfaction into a single objective task, and adds fuzzy completion time constraints on the basis of traditional shop scheduling constraints A fuzzy objective shop scheduling model is constructed, some genetic operators using different strategies are designed and a multi-population genetic algorithm based on clustering is proposed according to the similarity of populations The algorithm first generates multiple initial populations by extending the integer encoding of the processing order and processing machines, and each population undergoes independent genetic operations and then forms importation, integration, and artistic selection to share the excellent genes of individuals The algorithm is verified through experimental simulations that it has strong stability and global search capability: The probability of finding the global optimal solution has increased to 61%.

Research on vibration suppression of rigid flexible coupling manipulators based on genetic algorithm optimization

Aiming at the problem that the rigid flexible coupling manipulator generates elastic vibration during movement, which reduces the positioning accuracy and work efficiency, we propose a method of end vibration suppression based on cosine function superposition and multi population genetic algorithm. First, we get the relationship between the elastic vibration of the flexible arm and the joint trajectory motion according to the dynamic model of the system. Then, the cosine function superposition is used as the basis function to construct motion parameters of joints and the minimum residual vibration amplitude at the end of the flexible arm is used as the objective function. The undetermined coefficients in the basis function are optimized by multi population genetic algorithm to obtain the optimal trajectory. Finally, the simulation and experiment results show that the residual vibration amplitude is reduced by 60.6%. The proposed method can suppress the residual vibration at the end of the flexible arm and improve the work efficiency.

A new general method for assigning frequencies with low spillover through structural modifications

By modifying the mass and stiffness parameters of a structure, a subset of its natural frequencies can be assigned. However, such an assignment is usually accompanied with changes of the remaining frequencies, known as ‘spillover’. Eigenvalue or frequency spillover often needs to be avoided in engineering. In this paper, a new and general method is proposed for assigning a subset of the natural frequencies of a structure with a low level of spillover through structural modifications. The method combines a multi-population genetic algorithm and a radial basis function neural network and is implemented in a MATLAB-PYTHON-ABAQUS data interaction system. It is suitable for theoretical models (for example, finite element models) of general structures. By means of three numerical examples, the ability of the method to assign subsets of frequencies to discrete and discretised continuous structures while maintaining low spillover is demonstrated.

A self-correction single particle model of lithium-ion battery based on multi-population genetic algorithm

At present, the Single Particle (SP) model and the Extended Single Particle (ESP) model become a serious concern because they have real usage potentials for the battery management system. However, utilization of the models relies too much on accurate model parameters. Measuring accurately the parameters is a tough task, since they change with the battery condition during the actual use. In this study, Multi-Population Genetic Algorithm (MPGA), a certain kind of GA's improved algorithm, has been adopted to propose a Self-Correction Single Particle Model (SC-SPM). The SC-SPM can self-correct the measured values by sensing the terminal voltage change, which gives more accurate results for the solid phase diffusion process. To verify the proposed SC-SPM, four groups of simulations are performed, indicating that the SC-SPM is more accurate at observing the solid phase diffusion process when the battery condition changes beyond a certain range, and helping BMS better estimating other battery states. At the same time, the single step calculation time is <0.5 s which can meet the requirement of on-line application.