Multi-population Genetic Algorithm Research Articles

RETRACTED ARTICLE: Atmospheric environmental changes and a multi-objective optimization model for energy conservation and environmental protection based on improved multi-population genetic algorithm

With the development of computer technology and theory, cloud computing technology suitable for modern computing and large-capacity storage has emerged. Cloud computing technology depends on the Internet. The advancement and development of virtualization technologies such as cloud computing technology is the most cited of current computer technologies. With the development of cloud computing technology, this paper proposes a multi-group genetic algorithm based on interactive multi-functional processing methods, which can achieve the expectation of reducing the energy consumption of the cloud computing center to the greatest extent and at the same time ensure the stability and the running state of the server effectiveness. Through this survey, while reducing the company’s operating costs, it will also contribute to energy conservation and environmental protection. Multi-group genetic algorithms can effectively overcome the shortcomings of standard genetic algorithms, such as optimization range and efficiency, number of iterations, and premature end of iteration. The optimized result can reflect the subjective will of the decision-maker. Experimental examples show the efficiency of the algorithm, and the results show the applicability and feasibility of the algorithm. Based on the changes in the global atmospheric environment in recent years, this article discusses the reasons for the changes and focuses on the impact of human activities. In addition, the environmental issues that have received much attention today are the clues to discuss the climate issues and the harmful effects brought about by changes in the atmospheric environment, which has aroused people’s attention to energy conservation and environmental protection.

Economic scheduling of compressed natural gas main station considering critical peak pricing

Compressed natural gas has been proven to be more advantageous than gasoline and diesel in terms of emission cleanliness and equipment wear. The replacement of gasoline and diesel by CNG can be accelerated through the economic scheduling of CNG fueling stations. Due to the highly electrified equipment in the station, electricity cost is an important part of CNG operation costs. The critical peak pricing mechanism being implemented by the grid company provides opportunities for the economic scheduling of CNG fueling stations. This paper presents an improved operation model for a CNG main station, which considers the pre-system for the first time, including the dehydration device and buffer tank. Then, considering critical peak pricing, an economic scheduling model for the CNG main station is proposed. This optimal scheduling model is searched by an improved multi-population genetic algorithm combined with the elite retention strategy and repair operator. The results show that the electricity operating costs were effectively reduced by 34.62%, and the switching frequency of the compressor was decreased by 62.50%.

Dynamic Response Analysis of a Thin Plate with Partially Constrained Layer Damping Optimization under Moving Loads for Various Boundary Conditions

In this paper, the vibration analysis of a partially constrained layer damping plate subjected to moving loads is investigated. In addition, the first four order damping loss factor of the system is optimized with the location of partially constrained layer damping as a design variable. The equations of motion of a partially constrained layer damping plate are derived through the Lagrange equation based on first order shear deformation theory (FSDT). Next, using an extended Rayleigh–Ritz solution together with the penalty method expresses the unknown displacement terms, and the differential quadrature method is proposed to obtain the dynamic response of the system in the time domain. A multi-population genetic algorithm (MPGA) is employed to deal with the optimization of the damping loss factor of a partially constrained layer damping plate. To ensure the accuracy of the method presented in this study, the numerical results are comprehensively verified by experiments and open literature. The optimization results show that the damping loss factor increases when the position of the patch is close to the constraint boundary, and the best strategy is to optimize the low order damping loss factor of the system under moving loads. It is believed that the research results are of interest to engineering science.

A novel strategy for the crossarm length optimization of PSSCs based on multi-dimensional global optimization algorithms

Prestressed stayed steel columns (PSSCs) offer a lightweight alternative for the design of slender compression columns. The stability of compression columns can be significantly enhanced by introducing crossarms. Previous research on this subject has primarily focused on load-carrying capacity estimation or analyses of the influencing factors of PSSCs. Thus, investigations of the design optimization of PSSCs remain scant. This work proposes a novel strategy for crossarm length optimization of PSSCs based on multi-dimensional global optimization algorithms. A multi-population genetic algorithm is introduced in the optimization process. The proposed optimization algorithm was demonstrated to be efficient and accurate in terms of determining the optimal crossarm length of PSSCs.

Optimum opportunistic maintenance schedule incorporating delay time theory with imperfect maintenance

A proper maintenance schedule is an effective solution to reduce failure and keep systems in good condition. Reliability evaluation and maintenance model are two key issues for schedule optimization. The delay time theory is applied to evaluate reliability for a system by using sufficient inspection data and insufficient failure data. An integrated maintenance optimization model is proposed to arrange multi-level maintenance actions for multi-component series systems. An optimization model for individual components is first developed to minimize the costs of maintenance, failure and downtime respectively over a life cycle of components. The opportunistic maintenance involving reliability thresholds is further imported to reduce the maintenance costs of whole system without any reduction of maintenance performance. An improved multi-population genetic algorithm is then employed to overcome the difficulty from the complexity of large-scale problems in practice. The applicability of model and efficiency of solution algorithm are finally testified through the case studies for a locomotive system where a time-based opportunistic maintenance schedule is carried out for comparisons. The proposed methodology is helpful for engineers and operators to achieve the performance improvement and cost savings of systems over maintenance durations.

Optimizing security and cost of workflow execution using task annotation and genetic-based algorithm

Cloud computing provides an extensible infrastructure for executing workflows that demand high processing and storage capacity. Tasks are distributed and resources selected during scheduling where choices have a significant impact on data protection. Some workflow scheduling algorithms apply security services such as authentication, integrity verification, and encryption for both sensitive and non-sensitive tasks. However, this approach requires long makespan and monetary cost for execution. In this paper, we introduce a scheduling approach that considers the user annotation of workflow tasks according to the sensitiveness. We also optimize the scheduling using a multi-population genetic algorithm for minimizing cost while meeting a deadline. Extensive experiments using three workflow applications with different ratios of sensitive tasks and data size were performed to evaluate in terms of cost, makespan, risk, and wastage. The results showed that our approach can protect sensitive tasks more appropriately while achieving a better cost compared to other approaches in the literature.

Optimization of forced circulation to natural circulation transition characteristics of IPWR

For nuclear power plants, the operating characteristics of the forced circulation to natural circulation are particularly critical. In this paper, a simulation-based optimization method integrating the RELAP5 code and a multi-population genetic algorithm, is applied to investigate the characteristics of transition from forced circulation to natural circulation of the integrated pressurized water reactor. The optimal values of the decision variables: the primary coolant average temperature, the initial power, and main pump shutdown strategy are studied and evaluated by using single objective optimization in MATLAB software. The parameter analysis reveals that the initial plays significance on the transition performance. And shutting down the main pump one by one has a double-edged effect on system stability. Additionally, the optimal operating strategy that enables the integrated pressurized water reactor to transition safely and quickly is also obtained, which could provide an enlightening guidance for the operation of the reactor.

A Heuristic Algorithm for solving a large-scale real-world territory design problem

In this work, we present and evaluate heuristic techniques for a real-world territory design problem of a major dairy company which produces and distributes perishable products. The problem calls for grouping customers into geographic districts, with the objective of minimising the total operational cost, computed as a function of the fixed costs of the districts and the routing costs. Two inter-connected decision levels have to be tackled: partitioning customers into districts and routing vehicles according to complex operational constraints. To solve the problem, a hybrid multi-population genetic algorithm is designed, enhanced with several evolution and search techniques. The proposed design is extensively tested on instances derived from the literature and on real-world large-scale instances, involving more than 1000 customers. The results show the effectiveness of the different components of the algorithm and the feedback from the company’s planners confirms that it produces high-quality, operational solutions. Additionally, we explore some managerial findings with respect to the adoption of alternative objectives and service requirements.

Prediction of resilient modulus for subgrade soils based on ANN approach

The resilient modulus (MR) of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design. In order to determine the resilient modulus of compacted subgrade soils quickly and accurately, an optimized artificial neural network (ANN) approach based on the multi-population genetic algorithm (MPGA) was proposed in this study. The MPGA overcomes the problems of the traditional ANN such as low efficiency, local optimum and over-fitting. The developed optimized ANN method consists of ten input variables, twenty-one hidden neurons, and one output variable. The physical properties (liquid limit, plastic limit, plasticity index, 0.075 mm passing percentage, maximum dry density, optimum moisture content), state variables (degree of compaction, moisture content) and stress variables (confining pressure, deviatoric stress) of subgrade soils were selected as input variables. The MR was directly used as the output variable. Then, adopting a large amount of experimental data from existing literature, the developed optimized ANN method was compared with the existing representative estimation methods. The results show that the developed optimized ANN method has the advantages of fast speed, strong generalization ability and good accuracy in MR estimation.

Research on the Optimal Operation of a Novel Renewable Multi-Energy Complementary System in Rural Areas

Sustainable development is an inevitable choice for the development of human society, and energy is closely related to sustainable development. Improving energy structure, increasing energy efficiency, and vigorously developing renewable energy are of great significance to the sustainable development of rural areas. Moreover, the establishment of a distributed multi-energy complementary system (MECS) using abundant renewable energy such as wind, solar, and biomass energy is an effective way to solve the rapid growth of rural power demand, weak rural power grids, and rural environmental pollution. This paper proposes a new type of Wind–Solar–Biomass–Storage MECS composed of wind power generation (WPG), photovoltaic power generation (PVG), biogas power generation (BPG) and energy storage system (ESS) and establishes a MECS optimization operation model with the goal of maximizing daily operating economic benefits, considering the characteristics of each power generation system and power demand characteristics. By using the multi-population genetic algorithm (MPGA), the simulation experiments of the MECS operation under four typical weather scenarios are carried out. The results show that the MECS can operate stably in different scenarios and achieve the goal of maximizing economic benefits, which verifies the feasibility of the MECS model. In addition, the simulation results are compared with the standard genetic algorithm (SGA), which shows the effectiveness of the optimization method. This paper takes Chinese rural areas as an example for research. The proposed MECS and optimal operation model are also applicable to developing countries with a high proportion of the rural population.

Enhancement of Mutation Testing via Fuzzy Clustering and Multi-Population Genetic Algorithm

Mutation testing, a fundamental software testing technique, which is a typical way to evaluate the adequacy of a test suite. In mutation testing, a set of mutants are generated by seeding the different classes of faults into a program under test. Test data shall be generated in the way that as many mutants can be killed as possible. Thanks to numerous tools to implement mutation testing for different languages, a huge amount of mutants are normally generated even for small-sized programs. However, a large number of mutants not only leads to a high cost of mutation testing, but also make the corresponding test data generation a non-trivial task. In this paper, we make use of intelligent technologies to improve the effectiveness and efficiency of mutation testing from two perspectives. A machine learning technique, namely fuzzy clustering, is applied to categorize mutants into different clusters. Then, a multi-population genetic algorithm via individual sharing is employed to generate test data for killing the mutants in different clusters in parallel when the problem of test data generation as an optimization one. A comprehensive framework, termed as <inline-formula><tex-math notation="LaTeX">$\mathbf {FUZGENMUT}$</tex-math></inline-formula> , is thus developed to implement the proposed techniques. The experiments based on nine programs of various sizes show that fuzzy clustering can help to reduce the cost of mutation testing effectively, and that the multi-population genetic algorithm improves the efficiency of test data generation while delivering the high mutant-killing capability. The results clearly indicate that the huge potential of using intelligent technologies to enhance the efficacy and thus the practicality of mutation testing.

Research on Prediction Model of Mining Subsidence in Thick Unconsolidated Layer Mining Area

Accurately forecasting the scope of coal mining subsidence area is of great significance to the protection of surface structures. Due to the peculiarities of the unconsolidated layers rock formation, the surface movement of the thick unconsolidated layers mining area converges slowly at the boundary of the basin, and the boundary of the subsidence basin is larger than under conventional conditions. It is found that the existing models have a poor prediction effect at the boundary, and the predicted subsidence basin range is smaller than the actual basin range. To solve this problem, a new surface deformation prediction model based on Boltzmann function (IB) is proposed in this paper. Aiming at the problem that the model function is highly nonlinear and difficult to obtain parameters, the multi-population genetic algorithm (MPGA) is introduced into the parameter solution of the prediction model, and the parameter calculation model based on multi-population genetic algorithm (MPGAIB) is constructed. The simulation experiment and engineering example analysis show that both the overall fitting effect and the fitting effect at the boundary of IB model are closer to the actual situation, The MPGAIB model has good ability to anti-random error and gross error, and the result is stable.

A Multi-Population Genetic Algorithm for Adaptive Qos-Aware Service Composition in Fog-Iot Healthcare Environment

The growth of Internet of thing (IoT) implies the availability of a very large number of services which may be similar or the same, managing the Quality of Service (QoS) helps to differentiate one service from another.The service composition provides the ability to perform complex activities by combining the functionality of several services within a single process. Very few works have presented an adaptive service composition solution managing QoS attributes, moreover in the field of healthcare, which is one of the most difficult and delicate as it concerns the precious human life.In this paper, we will present an adaptive QoS-Aware Service Composition Approach (P-MPGA) based on multi-population genetic algorithm in Fog-IoT healthcare environment. To enhance Cloud-IoT architecture, we introduce a Fog-IoT 5-layared architecture. Secondly, we implement a QoS-Aware Multi-Population Genetic Algorithm (P-MPGA), we considered 12 QoS dimensions, i.e., Availability (A), Cost (C), Documentation (D), Location (L), Memory Resources (M), Precision (P), Reliability (R), Response time (Rt), Reputation (Rp), Security (S), Service Classification (Sc), Success rate (Sr), Throughput (T). Our P-MPGA algorithm implements a smart selection method which allows us to select the right service. Also, P-MPGA implements a monitoring system that monitors services to manage dynamic change of IoT environments. Experimental results show the excellent results of P-MPGA in terms of execution time, average fitness values and execution time / best fitness value ratio despite the increase in population. P-MPGA can quickly achieve a composite service satisfying user’s QoS needs, which makes it suitable for a large scale IoT environment.

Composition of Web Services of Multi-Population Adaptive Genetic Algorithm Based on Cosine Improvement

Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition. How to select the web service composition with the highest comprehensive QoS is a NP hard problem. In this paper, an improved multi population genetic algorithm is proposed. Cosine adaptive operator is added to the algorithm to avoid premature algorithm caused by improper genetic operator and the disadvantage of destroying excellent individuals in later period. Experimental results show that compared with the common genetic algorithm and multi population genetic algorithm, this algorithm has the advantages of shorter time consumption and higher accuracy, and effectively avoids the loss of effective genes in the population.

Random Target Localization for an Upper Limb Prosthesis

To achieve the purpose of accurately grasping a random target with the upper limb prosthesis, the acquisition of target localization information is especially important. For this reason, a novel type of random target localization algorithm is proposed. Firstly, an initial localization algorithm (ILA) that uses two 3D attitude sensors and a laser range sensor to detect the target attitude and distance is presented. Secondly, an error correction algorithm where a multipopulation genetic algorithm (MPGA) optimizes backpropagation neural network (BPNN) is utilized to improve the accuracy of ILA. Thirdly, a general regression neural network (GRNN) algorithm is proposed to calculate the joint angles, which are used to control the upper limb prosthetic gripper to move to the target position. Finally, the proposed algorithm is applied to the 5‐DOF upper limb prosthesis, and the simulations and experiments are proved to demonstrate the validity of the proposed localization algorithm and inverse kinematics (IK) algorithm.

Structural dynamic reliability estimation with advanced extremum Kriging method

To enhance the computational performance of the structural dynamic reliability estimation, advanced extremum Kriging method (AEKM) is proposed fusing Kriging surrogate model, multi-population genetic algorithm and response surface method-based extremum thought (ERSM) in this paper. The applicability of the AEKM is verified via the blisk radial deformation dynamic reliability analysis of an aeroengine high-pressure compressor. The results show that the reliability level of the blisk is 0.9956 as the allowable value of radial running deformation 1.75×10−3 m. Moreover, the developed AEKM is superior to other three surrogate models, including ERSM, K-ERSM and IK-ERSM, in terms of computing precision and analytical efficiency. Besides, the analytical precision of the developed method is basically consistent with the direct simulation, and the analytical time is obviously superior to the direct simulation. Therefore, the AEKM is illustrated to be an effective way for structural dynamic reliability analysis, which enriches complex structural reliability theory.

A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm

Owing to map scale reduction and other cartographic generalization operations, spatial conflicts may occur between buildings and other features in automatic cartographic generalization. Displacement is an effective map generalization operation to resolve these spatial conflicts to guarantee map clarity and legibility. In this paper, a novel building displacement method based on multipopulation genetic algorithm (BDMPGA) is proposed to resolve spatial conflicts. This approach introduces multiple populations with different control parameters for simultaneous search optimization and adopts an immigration operation to connect different populations to realize coevolution. The optimal individuals of each population are selected and preserved in the elite population through manual selection operation to prevent the optimal individuals from being destroyed and lost in the evolutionary process. Meanwhile, the least preserving generation of the optimal individuals is used as the termination basis. To validate the proposed method, urban building data with a scale of 1:10,000 from Shenzhen, China are used. The experimental results indicate that the method proposed in this paper can effectively resolve spatial conflicts to obtain better results.

An Automatic Optimized Method for a Digital Optical Phase Conjugation System in Focusing through Scattering Media

In this paper, a reliable automatic optimized method for a digital optical phase conjugation (DOPC) system based on a multipopulation genetic algorithm (MPGA) is proposed for improving the compensation quality of DOPC. The practical implementation and compensation quality of DOPC in focusing through scattering media are greatly limited by imperfect pixel alignment, optical aberration, and mechanical error in the DOPC system. For comprehensively solving the above problems, the concept of global optimization is introduced by Zernike polynomials (Zernike modes) to characterize overall imperfections, and MPGA is used to search for the most optimal Zernike coefficient and compensate for the overall imperfections of the DOPC system. The significant optimization ability of the proposed method is verified in DOPC-related experiments for focusing through scattering media. The peak-to-background ratio (PBR) of the OPC focus increases 174 times that of the initial OPC focus. Furthermore, we evaluated the optimization results of the proposed method with a fitness function of intensity fitness and correlation coefficient fitness in MPGA. The results show that the optimized capability is excellent and more efficiently used than the correlation coefficient fitness function in the Zernike modes.

Enhanced network learning model with intelligent operator for the motion reliability evaluation of flexible mechanism

The evaluation of flexible mechanism involving multi-body dynamics with high nonlinearity and transients urgently requires an efficient evaluation method to enhance its reliability and safety. In this work, an enhanced network learning method (ENLM) is proposed to improve the modeling precision and simulation efficiency in flexible mechanism reliability evaluation, by introducing generalized regression neural network (GRNN) and multi-population genetic algorithm (MPGA) into extremum response surface method (ERSM). In the ENLM modeling, the ERSM is adopted to reasonably handle transients (time-varying) problem in motion reliability analysis by considering one extreme value in whole response process; the GRNN is applied to address high-nonlinearity in surrogate modeling; the MPGA is utilized to find the optimal model parameters in ENLM modeling. In respect of the developed ENLM, the motion reliability of two-link flexible robot manipulator (TFRM) was evaluated, with regard to the related input random parameters to material density, elastic modulus, section sizes, and deformations of components. In term of this study, it is illustrated that (i) the comprehensive reliability of flexible robot manipulator is 0.951 when the allowable deformation is 1.8×10−2 m; (ii) the maximum deformations of member-1 and member-2 obey normal distributions with the means of 1.45×10−2 m and 1.69×10−2 m as well as the standard variances of 6.77×10−4 m and 4.08×10−4 m, respectively. The comparison of methods demonstrates that the ENLM improves the modeling precision by 3.29% and reduces the simulation efficiency by 1.19 s under 10000 simulations, and the strengths of the ENLM with high modeling precision and high simulation efficiency become more obvious with the increase of simulations. The efforts of this study provide a learning-based reliability analysis way (i.e., ENLM) for the motion reliability design optimization of flexible mechanism and enrich mechanical reliability theory.

Study of optimal allocation of water resources in Dujiangyan irrigation district of China based on an improved genetic algorithm

Abstract In order to distribute water resources reasonably, it is advantageous to make full use of resources and produce high economic and social benefits. Taking the Dujiangyan irrigation area of China as an example, we discuss the idea of establishing and solving the optimal allocation model of water resources. With this aim, a two-dimensional constraint model with highest economic value, minimum water shortage, minimum underground water consumption and necessary living water demand was established. In order to solve this model, we improved a multi-population genetic algorithm, extending the genetic optimization of the algorithm into two dimensions, taking population as the vertical dimension and the individual as the horizontal dimension, and transforming the cross genetic operator to copy the genetic (crossover) operator and the mutation operator to only act on the vertical dimension, so as to optimize the allocation of such discrete objectives of water resources in the irrigation area with a particular model suitable for the region. The distribution results successfully control the water shortage rate of each area at a low level, which saves the exploitation of groundwater to the maximum extent and produces high economic benefits. The improved algorithm proposed in this paper has a kind of strong optimization ability and provides a new solution for the optimization problem with multiple constraints.