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Task scheduling is important in cloud manufacturing because of customers’ increasingly individualized demands. However, when various changes occur, a previous optimal schedule may become non-optimal or even infeasible owing to the uncertainty of the real manufacturing environment where dynamic task arrival over time is a vital source. In this paper, we propose a novel collaborative task scheduling (CTS) model dealing with new task arrival which considers multi-supply chain collaboration. We present an improved multi-population biogeography-based optimization (IMPBBO) algorithm that uses a matrix-based solution representation and integrates the multi-population strategy, local search for the best solution, and the collaboration mechanism, for determining the optimal schedule. A series of experiments are conducted for verifying the effectiveness of the IMPBBO algorithm for solving the CTS model by comparing it with five other algorithms. The experimental results concerning average best values obtained by the IMPBBO algorithm are better than that obtained by comparison algorithms for 41 out of 45 cases, showing its superior performance. Wilcoxon-test has been employed to strengthen the fact that IMPBBO algorithm performs better than five comparison algorithms.

The Gravitational Field Algorithm, a modern optimization algorithm, mainly simulates celestial mechanics and is derived from the Solar Nebular Disk Model (SNDM). It simulates the process of planetary formation to search for the optimal solution. Although this optimization algorithm has more advantages than other optimization algorithms in multi-peak optimization problems, it still has the shortcoming of long computation time when dealing with large-scale datasets or solving complex problems. Therefore, it is necessary to improve the efficiency of the Gravitational Field Algorithm (GFA). In this paper, an optimization method based on multi-population parallel is proposed to accelerate the Gravitational Field Algorithm. With the help of the parallel mechanism in MATLAB, the algorithm execution speed will be improved by using the parallel computing mode of multi-core CPU. In addition, this paper also improves the absorption operation strategy. By comparing the experimental results of eight classical unconstrained optimization problems, it is shown that the computational efficiency of this method is improved compared with the original Gravitational Field Algorithm, and the algorithm accuracy has also been slightly improved.

The success in reducing global HIV prevalence rates is attributed to control measures like information and education campaigns (IECs), antiretroviral therapy (ART), and national, multinational and ...

This paper proposes a multi-level hierarchical credibility regression approach to model multi-population mortality data. Future mortality rates are derived using extrapolation techniques, while the forecasting performances between the proposed model, the original Lee–Carter model and two Lee–Carter extensions for multiple populations are compared for both genders of three northern European countries with small populations (Ireland, Norway, Finland). Empirical illustrations show that the proposed method produces more accurate forecasts than the Lee–Carter model and its multi-population extensions.

An optimization model to minimize the cost of designing water distribution network is presented in this study. The model was formulated to reduce the cost coefficient in a plumbing system. A new hybrid method of optimization was constructed by combining the search abilities of Jaya-based algorithm and pollination intelligence algorithm, and was used to solve the designed model. The model was implemented by obtaining geometrical information of a water distribution network layout stationed at Gaa Odota, Ilorin, Kwara State, Nigeria. Result obtained from the model showed a significant reduction in the cost coefficient compared to that of the study area.

We compare results for 12 multi-population mortality models fitted to 10 distinct socio-economic groups in England, subdivided using the Index of Multiple Deprivation. Using the Bayes Information Criterion to compare models, we find that a special case of the common age effect (CAE) model fits best in a variety of situations, achieving the best balance between goodness of fit and parsimony. We provide a detailed discussion of key models to highlight which features are important. Group-specific period effects are found to be more important than group-specific age effects, and non-parametric age effects deliver significantly better results than parametric (e.g. linear) age effects. We also find that the addition of cohort effects is beneficial in some cases but not all. The preferred CAE model has the additional benefit of being coherent in the sense of Hyndman et al. ((2013) Demography50(1), 261–283); some of the other models considered are not.

Recent developments in actuarial literature have shown that credibility theory can serve as an effective tool in mortality modelling, leading to accurate forecasts when applied to single or multi-population datasets. This paper presents a crossed classification credibility formulation of the Lee–Carter method particularly designed for multi-population mortality modelling. Differently from the standard Lee–Carter methodology, where the time index is assumed to follow an appropriate time series process, herein, future mortality dynamics are estimated under a crossed classification credibility framework, which models the interactions between various risk factors (e.g. genders, countries). The forecasting performances between the proposed model, the original Lee–Carter model and two multi-population Lee–Carter extensions are compared for both genders of multiple countries. Numerical results indicate that the proposed model produces more accurate forecasts than the Lee–Carter type models, as evaluated by the mean absolute percentage forecast error measure. Applications with life insurance and annuity products are also provided and a stochastic version of the proposed model is presented.

A common problem that the world faces is the waste of energy. In water pump stations, the situation is not different. Employees still use the traditional, manual, and empirical operation of the water pumps. This process gradually generates unwanted losses of energy and money. To avoid such profligacy, this paper presents two Adaptive and one Improved Multi-population based nature-inspired optimization algorithms for water pump station scheduling. The main goal here is to obtain the optimal operational scheduling of each group of pumps, wasting the minimum amount of energy. Therefore, since the objective function relies on the shaft power consumption of all the pumps running together, our aim becomes feasible. We implemented and tested the algorithms in the main water pump station of Shanghai, in China. Based on traditional multi-population based nature-inspired optimization algorithms, such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), this work adapts and improves the models to fit the complex constraints and characteristics of the system. It also compares and analyses the performance of each method used in this case study, considering the obtained results. The method which demonstrated outperformance was chosen as the best solution for the present problem.

In this paper, we formulate the multi-population mortality forecasting problem based on 3-way (age, year, and country/gender) decompositions. By applying the canonical polyadic decomposition (CPD) and the different forms of the Tucker decomposition to multi-population mortality data (10 European countries and 2 genders), we find that the out-of-sample forecasting performance is significantly improved both for individual populations and the aggregate population compared with using the single-population mortality model based on rank-1 singular value decomposition (SVD), or the Lee–Carter model. The results also shed lights on the similarity and difference of mortality among different countries. Additionally, we compare the variance-explained method and the out-of-sample validation method for rank (hyper-parameter) selection. Results show that the out-of-sample validation method is preferred for forecasting purposes.

Porous material (PM) shows good sound absorption performance, however, the sound absorbing property of PM with different parameters are greatly different. In order to match the most suitable absorbing materials with the most satisfactory sound-absorbing performance according to the noise spectrum in different practical applications, multi-population genetic algorithm is used in this paper to optimize the parameters of porous sound absorbing structures that are commonly used according to the actual demand of noise reduction and experimental verification. The results shows that the optimization results of multi-population genetic algorithm are obviously better than the standard genetic algorithm in terms of sound absorption performance and sound absorption bandwidth. The average acoustic absorption coefficient of PM can reach above 0.6 in the range of medium frequency, and over 0.8 in the range of high frequency through optimization design. At a mid-to-high frequency environment, the PM has a better sound absorption effect and a wider frequency band than that of micro-perforated plate. However, it has a poor sound absorption effect at low frequency. So it is necessary to select suitable sound absorption material according to the actual noise spectrum.

Differential evolution (DE) is an efficient population-based search algorithm with good robustness, but it faces challenges in dealing with Large-Scale Global Optimization (LSGO). In this paper, we proposed an improved multi-population differential evolution with best-random mutation strategy (called mDE-brM). The population is divided into three sub-populations based on the fitness values, each sub-population uses different mutation strategies and control parameters, individuals share different mutation strategies and control parameters by migrating among sub-populations. A novel mutation strategy is proposed, which uses the best individual and a randomly selected individual to generate base vector. The performance of mDE-brM is evaluated on the CEC 2013 LSGO benchmark suite and compared with 5 state-of-the-art optimization techniques. The results show that, compared with other contestant algorithms, mDE-brM has a competitive performance and better efficiency in LSGO.

The performance of various population-based advanced optimization algorithms has been significantly improved by using the multi-population search scheme. The multi-population search process...

The successful reduction in prevalence rates of HIV in many countries is attributed to control measures such as information and education campaigns (IEC), antiretroviral therapy (ART), and national, multinational and multilateral support providing official developmental assistance (ODAs) to combat HIV. However, control of HIV epidemics can be interrupted by limited random supply of ODAs, high poverty rates and low living standards. This study presents a stochastic HIV/AIDS model with treatment assessing the roles of IEC, the supply of ODAs and early treatment in HIV epidemics. The supply of ODAs is assessed via the availability of medical and financial resources leading more people to get tested and begin early ART. The basic reproduction number ($\mathfrak{R}_{0}$) for the dynamics is obtained, and other results for HIV control are obtained by conducting stability analysis for the stochastic SITRZ disease dynamics. Moreover, the model is applied to Uganda HIV/AIDS data, wherein linear regression is applied to predict the $\mathfrak{R}_{0}$ over time, and to determine the importance of ART treatment in the dynamics.

We present a novel distributed heterogeneous multi-population evolutionary dynamics approach, which can be used in diverse engineering applications as a distributed optimization-based algorithm. We also provide stability certificates of Nash equilibria under the proposed approach. Finally, over the end of this paper, an example illustrates the performance of the aforementioned multi-population evolutionary dynamics.

Various intelligent algorithms are applied in optimization design, and the differential evolution (DE) algorithm is widely applied with its excellent convergence speed and convergence precision. This study analyzed the advantages and disadvantages of the existing multi-objective differential evolution (MODE) algorithm, and developed a MODE algorithm based on the adaptive weight and the multi-population strategy (MODE/AWMS). The proposed algorithm was verified using test functions. MODE/AWMS exhibited certain advantages compared with several other multi-objective optimization algorithms. Taking a polarized magnetic relay as an example, MODE/AWMS was used to optimize its key parameters by establishing a rapid calculation model of its electromagnetic mechanism. The electromagnetic force (EMF) of the release position was improved, which verified the validity of MODE/AWMS.

In this paper, we present and calibrate a multi-population stochastic mortality model based on latent square-root affine factors of the Cox-Ingersoll and Ross type. The model considers a generalization of the traditional actuarial mortality laws to a stochastic, multi-population and time-varying setting. We calibrate the model to fit the mortality dynamics of UK males and females over the last 50 years. We estimate the optimal states and model parameters using quasi-maximum likelihood techniques.

For solving the problems of low accuracy, low convergence speed and accidental premature of canonical whale optimization algorithm (WOA), a modified multi-population WOA based on orthogonal learning (MOL-WOA) is proposed. Firstly, the multi-population strategy is adopted. The diversity of the sub-populations can be improved by the generalized opposition-based learning (GOBL) strategy. Secondly, the nonlinear convergence factor and adaptive weight strategy are utilized to take account of the global search and local development. Finally, the orthogonal learning method is adopted to realize the communication among the sub-populations. At the same time, a generation jump operation is brought in for avoiding premature. Simulation experiments with 20 benchmark test functions, contrasted with the other two modified algorithms, MOL-WOA owns better performance on accuracy, convergence speed and ability to miss the local optimal.

Genetic algorithm (GA) is a metaheuristic method that has been widely adopted for solving the Rich Vehicle Routing Problems (RVRP) due to its ability to find quality approximate solutions, even for large-scale instances of the problem, in a reasonable time. However, GA is stochastic in nature and does not guarantee a good solution all the time, a problem primarily due to premature convergence. In this paper we present Multi-population Genetic Algorithm for Rich Vehicle Routing Problems (MPGA-RVRP) to provide diversity and delay premature convergence in GA by making use of multiple populations that each evolves independently optimizing a single objective while sharing potential solutions. MPGA-RVRP is applied in RVRP with three objectives:- total route distance, total route duration and total route cost. Results from the experiments show that MPGA-RVRP performs considerably better compared to benchmark, multi-objective Genetic Algorithm (MOGA).

Increasing population around the world, especially in India and China, has resulted in a drastic increase in water intake in both domestic and agricultural sectors. This, therefore, requires that water resources be planned and controlled wisely and effectively. With this consideration, the aim of the study is to achieve an optimal cropping pattern under a constrained environment. The objective is to maximize the net benefits with an optimum use of water. For optimization, a self-adaptive multi-population Jaya algorithm (SAMP-JA) has been used. For the Karjan reservoir in Gujarat State, India, two different models, i.e. maximum and average cropping patterns, were formulated based on the 75 per cent dependable inflow criteria. These two model scenarios are developed in such a way that either model can be selected by the farmer based on the crop area and its respective net benefits. Invasive weed optimization (IWO), particle swarm optimization (PSO), differential evolution (DE) and the firefly algorithm (FA) were compared to the results. The results show that the SAMP-JA obtained the maximum net benefit for both the models. The findings of the research are also compared with the actual cropping pattern. A significant increase has been noted in the cultivation of sugarcane, groundnut, wheat, millet, banana and castor. SAMP-JA has been noted to converge faster and outperforms PSO, DE, IWO, FA, teaching–learning-based optimization (TLBO), the Jaya algorithm (JA), elitist-JA and elitist-TLBO.

The present study investigates the diversification and dynamic behavior of a multi-population microfluidic microbial fuel cell (MFC) as a biosensor. The cost effective microfluidic MFC coupled to a comprehensive model, presents a novel platform for monitoring chemical and biological phenomena. The importance of competition among different microbial groups, hierarchical biochemical processes, bacterial chemotaxis and different mechanisms of electron transfer were significant considerations in the present model. The validation of the model using experimental data from a microfluidic MFC shows an appropriate match with the hierarchal biodegradation processes of a complex substrate as well as development of bacterial chemotaxis during multi-population biofilm formation under real conditions. Microfluidic MFC performance, including temporal and spatial distribution of different microbial group concentrations in the biofilm and anolyte bulk, the competitive behavior of different species, bacterial transport parameters and bioelectrochemical characteristics are also assessed.

Wireless sensor networks (WSNs) have been the popular targets for cyberattacks these days. One type of network topology for WSNs, the scale-free topology, can effectively withstand random attacks in which the nodes in the topology are randomly selected as targets. However, it is fragile to malicious attacks in which the nodes with high node degrees are selected as targets. Thus, how to improve the robustness of the scale-free topology against malicious attacks becomes a critical issue. To tackle this problem, this paper proposes a Robustness Optimization scheme with multi-population Co-evolution for scale-free wireless sensor networKS (ROCKS) to improve the robustness of the scale-free topology. We build initial scale-free topologies according to the characteristics of WSNs in the real-world environment. Then, we apply our ROCKS with novel crossover operator and mutation operator to optimize the robustness of the scale-free topologies constructed for WSNs. For a scale-free WSNs topology, our proposed algorithm keeps the initial degree of each node unchanged such that the optimized topology remains scale-free. Based on a well-known metric for the robustness against malicious attacks, our experiment results show that ROCKS roughly doubles the robustness of initial scale-free WSNs, and outperforms two existing algorithms by about 16% when the network size is large.

Fuzzy rule-based classification systems (FRBCSs) are appropriate tools for dealing with classification problems because of their interpretable models based on linguistic variables. Intelligent optimization techniques are widely used in the rule mining of FRBCSs due to their parallel processing capability for solving the combination optimization of fuzzy antecedent parameters and “don’t care” variables, however, FRBCSs suffer from misclassification because of the chain coding scheme and non-guidance of updating strategy in rule mining process. This study presents an FRBCS that uses a multi-population quantum evolutionary algorithm with contradictory rule reconstruction. The developed method utilizes fuzzy C-means clustering to heuristically generate representative initial rules, and performs multi-population quantum coding and guided updating to optimize fuzzy rules. Furthermore, contradictory rule reconstruction is introduced to adjust misclassification rules. Numerical experiment results show that the classification accuracy and noise tolerance of the proposed method are better than those of compared FRBCSs. The proposed method is verified by applying it to fault identification in a wind turbine.

This paper presents a novel framework for improving the performance of multi-population algorithms in solving dynamic optimization problems (DOPs). The fundamental idea of the proposed framework is to incorporate the concept of scheduling into multi-population methods with the aim to allocate more function evaluations to the best performing sub-populations. Two methods are developed based on the proposed framework, each of which uses a different approach for scheduling the sub-populations. The first method combines the quality of sub-populations and the degree of diversity among them into a single feedback parameter for detecting the best performing sub-population. The second method uses the learning automata as the central unit for performing the scheduling operation. In order to validate the applicability of the proposed methods, they are incorporated into three well-known algorithms for DOPs. The experimental results show the efficiency of the scheduling approach for improving the multi-population methods on the moving peaks benchmark (MPB) and generalized dynamic benchmark generator.

Multi-population based nature-inspired optimization algorithms have attracted wide research interests in the last decade, and become one of the frequently used methods to handle real-world optimization problems. Considering the importance and value of multi-population methods and its applications, we believe it is the right time to provide a comprehensive survey of the published work, and also to discuss several aspects for the future research. The purpose of this paper is to summarize the published techniques related to the multi-population methods in nature-inspired optimization algorithms. Beginning with the concept of multi-population optimization, we review basic and important issues in the multi-population methods and discuss their applications in science and engineering. Finally, this paper presents several interesting open problems with future research directions for multi-population optimization methods.

In this paper, we first propose a multi-dimensional Buhlmann credibility approach to forecasting mortality rates for multiple populations, and then compare forecasting performances among the propos...

To solve large-scale traveling salesmen problem (TSP) with better performance, we propose a multi-population ant colony optimization algorithm based on congestion factor and co-evolution mechanism (CCMACO). First, the congestion factor is introduced to control the number of ants on the path and can improve the adaptability of CCMACO. Then, the sub-populations restructuring strategy is proposed to balance the convergence speed and the diversity of solutions. Besides, the inter-specific competition mechanism can be used to strengthen the optimal solution and to accelerate convergence speed. Finally, the co-evolutionary strategy is used to interchange information among different sub-populations so as to maintain the diversity of populations. For the purpose of verifying the optimization performance of the CCMACO algorithm, CCMACO is compared with several improved multi-population ant colony optimization algorithms in TSP. The experiment results show that the proposed CCMACO algorithm can effectively obtain the best optimization value in solving TSP and it achieves better optimization ability and stability.

In this paper, a recently proposed Jaya algorithm is implemented on the economic load dispatch problems (ELDPs). Different from most of the other meta-heuristics, Jaya algorithm needs no algorithm-specific parameters, and only two common parameters are required for effective execution, which makes the implementation simple and effective. Simultaneously, considering the non-convex, non-linear, and non-smooth characteristics of the ELDPs, the multi-population (MP) method is introduced to improve the population diversity. However, the introduction of the MP method adds extra parameters to the Jaya algorithm, hence a self-adaptive strategy is used to cope with the tuning problem for extra parameters. Moreover, to avoid being trapped by local optima, Levy flights distribution is incorporated into the population iteration phase. Finally, Jaya algorithm with self-adaptive multi-population and Levy flights (Jaya-SML) is proposed, it is evaluated by ELDPs with different constraints including power balance constraints, generating capacity limits, ramp rate limits, prohibited operating zones, valve-point effects, and multi-fuel options. The comparisons with state-of-the-art methods indicate that Jaya-SML can generate more competitive results for solving the ELDPs.

Background: Common genetic variation in the arsenic methyltransferase (AS3MT) gene region is known to be associated with arsenic metabolism efficiency (AME), measured as the percentage of dimethylarsinic acid (DMA%) in the urine. Rare, protein-altering variants in AS3MT could have even larger effects on AME, but their contribution to AME has not been investigated. Objectives: We estimated the impact of rare, protein-coding variation in AS3MT on AME using a multi-population approach to facilitate the discovery of population-specific and shared causal rare variants. Methods: We generated targeted DNA sequencing data for the coding regions of AS3MT for three arsenic-exposed cohorts with existing data on arsenic species measured in urine: Health Effects of Arsenic Longitudinal Study (HEALS, n=2,434), Strong Heart Study (SHS, n=868), and New Hampshire Skin Cancer Study (NHSCS, n=666). We assessed the collective effects of rare (allele frequency

Background: Common genetic variation in the arsenic methyltransferase (AS3MT) gene region is known to be associated with arsenic metabolism efficiency (AME), measured as the percentage of dimethyla...

Mortality prediction is a crucial aspect for insurance and pension fund companies in deciding a suitable premium. The aim of this research is to discuss a cross-country (multi-population) mortality modeling in order to obtain a better mortality prediction. This modeling is based on the multi-dimensional Buhlmann credibility approach. The expansion in this research refers to mortality rate data taken from several countries. The Buhlmann credibility theory is generally used to predict the value of a random variable in a given period in the future. In this research, prediction for years to come was done using two strategies: Expanding Window and Moving Window. For every prediction in the upcoming period, both Expanding Window and Moving Window use prediction result values as additional data to build upon the prediction model for the next year; however, Moving Window also dismisses the oldest data. The model parameter is estimated with non-parametric approach. This model is then applied to the mortality data from Japan, Sweden, and the Czech Republic. Finally, each model’s performance is analyzed using Mean Absolute Percentage Error (MAPE) and Average Mean Absolute Percentage Error (AMAPE). The result shows that the performance of the multi-dimensional Buhlmann credibility approach is satisfactory in modeling cross-country mortality rates.

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.

An application of own algorithm for reduction optimization calculation time is presented in the paper. The algorithm is called Distributed Multi-Population Evolutionary Algorithm and uses a genetic algorithm with real-coded genes in the chromosome. The client-server architecture was used for processing lots of populations with a simultaneous data exchange between the populations. Each generation consists of standard genetic operators: a natural selection, one-point crossing, uniform mutation and a data exchange within the computational units. This method was used for selecting values of damping coefficients of a driver’s seat sub-assembly to improve driving comfort. Numerical results of a vehicle driving with different variants of velocity and obstacles are presented in the paper.

All-cause mortality is driven by various types of cause-specific mortality. Projecting all-cause mortality based on cause-of-death mortality allows one to understand the drivers of the recent changes in all-cause mortality. However, the existing literature has argued that all-cause mortality projections based on cause-specific mortality experience have a number of serious drawbacks, including the inferior cause-of-death mortality data and the complex dependence structure between causes of death. In this article, we use the recent World Health Organization causes-of-death data to address this issue in a multi-population context. We construct a new model in the spirit of N. Li and Lee (2005) but in terms of cause-specific mortality. A new two-step beta convergence test is used to capture the cause-specific mortality dynamics between different countries and between different causes. We show that the all-cause mortality estimations produced by the new model perform in the sample similarly to the estimations by the Lee-Carter and Li-Lee all-cause mortality models. However, in contrast to results from earlier studies, we find that the all-cause mortality projections of the new model have better out-of-sample performance in a long forecast horizon. Moreover, for the case of The Netherlands, an approximately 1-year higher remaining life expectancy projection for a 67-year-old Dutch male in a 30-year forecast horizon is obtained by this new model, compared to the all-cause Li-Lee mortality model.

Berths, quay cranes and container trucks are three kinds of facilities that determine the operation efficiency of container terminals. This paper aims at the working relationship and characteristics of these three facilities, and carries out systematic optimization. First, by considering the coefficient of berth migration, a cooperative nonlinear mixed integer programming model is built. Then, a two-phase algorithm based on multiple population genetic algorithm is designed to solve the model. Finally, through the specific example analysis and solution, an optimal cooperative scheduling scheme is obtained, which verifies the feasibility of the model in this paper and could save the whole operating time.

Multi-population evolutionary models are effective methods for solving many difficult optimisation problems due to their ability to preserve population diversity via isolated evolution mechanisms. However, applications to multi-objective combinatorial problems containing time-varying characteristics is limited. In this paper, we propose a multi-population model to improve optimisation performance on the recent dynamic formulations of the Travelling Thief Problem. A key feature of our model relates to how the currency of exploitable problem information, with regard to problem dynamics, can be used for population seeding in response to dynamic events. We contrast performance of a multi-population model with differing optimisation goals in each isolated sub-population with a similar model featuring migration between populations and a standard single population method. The sharing of information between differently directed subpopulations provides significant improvements in terms of hypervolume measurements throughout the optimisation procedure. A performance analysis method is applied here to capture the significant improvements of the different models throughout the varying dynamic intervals of the problem instances. Significant performance improvements are achieved with a relatively simple multi-population topology and migration patterns that ensure currency of exploited problem information. The application of multi-population techniques to further examples of complex dynamic multi-objective optimisation problems remains an ongoing research pursuit.

This paper investigates parallelization method for meta-heuristic algorithms such as Particle Swarm optimization (PSO) and Differential Evolution (DE) on multicore processor to reach eventually fast execution and stable result. In PSO or DE algorithm, all of member in initial population are created to search the best place in which the value of member in that place is satisfied the output criteria. As the parallelization method, the searching region is separated into many sub-regions which are executed with optimized algorithm on multi-core processor. The structure of meta-heuristic algorithms is rebuilt to execute in parallel multi population mode. The benchmark functions such as Rosenbrock, Griewank, Ackley and Michalewicz are used to test those proposed algorithms. The results show that the proposed parallel multi-population technique applied on PSO and DE algorithm has a competitive performance compared to the standard ones. The parallel multi-population technique shows better result which proves more precise and stable. Especially, meta-heuristic algorithms running in parallel multipopulation mode execute quite convincingly faster than standard ones.

This paper presents a hybrid multi-population evolution algorithm based on genetic algorithms and regularized evolution for neural architecture search. The proposed algorithm improves the search quality by maintaining population diversity. The total population is divided into multiple tribes. In each tribe, there are multiple subpopulations with different crossover and mutation strategies. The best individuals in subpopulations are migrated between subpopulations and tribes to guide search direction toward optimum solutions. The experimental results show that the proposed algorithm performs better than traditional multi-population genetic algorithms and comparably compete with regularized evolution and other state of the art NAS algorithm on CIFAR-10 dataset.

EFSM is widely used in the modeling of control systems and protocols. Genetic algorithm is often used in EFSM test data generation due to its good performance. However, genetic algorithm tends to converge prematurely in complex search space and fall into local optimum, which leads to poor search efficiency. At the same time, test cases generated by genetic algorithm for EFSM have the problem of poor defect detection ability. Aiming at the above two problems, we improve genetic algorithm based on fault propagation assumption and multi-population strategy, design and implement the multi-population genetic algorithm based on fault propagation. In order to verify the performance of the improved algorithm, we design and conduct a control experiment, the results of the experiment show that the algorithm we proposed can effectively solve the problem of falling into local optimum and has advantages in defect detection.

Clinical outcomes of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) showed enormous inter-individual and inter-population differences, possibly due to host genetics differences. Earlier studies identified single nucleotide polymorphisms (SNPs) associated with SARS-CoV-1 in Eastern Asian (EAS) populations. In this report, we aimed at exploring the frequency of a set of genetic polymorphisms that could affect SARS-CoV-2 susceptibility or severity, including those that were previously associated with SARS-CoV-1. We extracted the list of SNPs that could potentially modulate SARS-CoV-2 from the genome wide association studies (GWAS) on SARS-CoV-1 and other viruses. We also collected the expression data of these SNPs from the expression quantitative trait loci (eQTLs) databases. Sequences from Qatar Genome Programme (QGP, = 6,054) and 1000Genome project were used to calculate and compare allelic frequencies (AF). A total of 74 SNPs, located in 10 genes: , -γ, , , , , , , and promoter, were identified. Analysis of Qatari genomes revealed significantly lower AF of risk variants linked to SARS-CoV-1 severity (, , , , and ) compared to that of 1000Genome and/or the EAS population (up to 25-fold change). Conversely, SNPs in , -γ, , and were more common among Qataris (average 2-fold change). Inter-population analysis showed that the distribution of risk alleles among Europeans differs substantially from Africans and EASs. Remarkably, Africans seem to carry extremely lower frequencies of SARS-CoV-1 susceptibility alleles, reaching to 32-fold decrease compared to other populations. Multiple genetic variants, which could potentially modulate SARS-CoV-2 infection, are significantly variable between populations, with the lowest frequency observed among Africans. Our results highlight the importance of exploring population genetics to understand and predict COVID-19 outcomes. Indeed, further studies are needed to validate these findings as well as to identify new genetic determinants linked to SARS-CoV-2. This work was supported by the Qatar University High Impact Grant (Grant Number: QUHI-BRC-20_21-1). OA was supported by a startup grant from the College of Health and Life Sciences, Hamad Bin Khalifa University. This work makes use of data generated by the Qatar Genome Programme (QGP) and Qatar Biobank (QBB), which are funded by Qatar Foundation for Education, Science and Community.