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Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

For an entrant platform in the on-demand service market, choosing an appropriate employment model is critical. This study explores how the entrant optimally chooses the employment model to achieve better performance and investigates the optimal pricing strategies and wage schemes for both incumbent and entrant platforms.

Based on the Hotelling model, the authors develop a game-theoretic framework to study the incumbent's and entrant's optimal service prices and wage schemes. Moreover, the authors determine the entrant's optimal employment model by comparing the entrant's optimal profits under different market configurations and analytically analyze the impacts of some critical factors on the platforms' decision-making.

This study reveals that the impacts of the unit misfit cost of suppliers or consumers on the pricing strategies and wage schemes vary with different operational efficiencies of platforms. Only when both the service efficiency of contractors and the basic employee benefits are low, entrants should adopt the employee model. Moreover, a lower unit misfit cost of suppliers or consumers makes entrants more likely to choose the contractor model. However, the service efficiency of contractors has nonmonotonic effects on the entrant's decision.

This study focuses on an entrant's decision on the optimal employment model in an on-demand service market, considering the competition between entrants and incumbents on both the supplier and consumer sides, which has not been investigated in the prior literature.

This paper aims to conduct a fuzzy discrete time cost quality risk in the ambiguous mode CO_{2 tradeoff problem} (FDTCQRP*TP) in a megaproject based on fuzzy ground.

A combinatorial evolutionary algorithm using Fuzzy Invasive Weed Optimization (FIWO) is used in the discrete form of the problem where the parameters are fully fuzzy multi-objective and provide a space incorporating all dimensions of the problem. Also, the fuzzy data and computations are used with the Chanas method selected for the computational analysis. Moreover, uncertainty is defined in FIWO. The presented FIWO simulation, its utility and superiority are tested on sample problems.

The reproduction, rearrangement and maintaining elite invasive weeds in FIWO can lead to a higher level of accuracy, convergence and strength for solving FDTCQRP*TP fuzzy rules and a risk ground in the ambiguous mode with the emphasis on the necessity of CO₂ pollution reduction. The results reveal the effectiveness of the algorithm and its flexibility in the megaproject managers' decision making, convergence and accuracy regarding CO₂ pollution reduction.

This paper offers a multi-objective fully fuzzy tradeoff in the ambiguous mode with the approach of CO₂ pollution reduction.

Although green logistics has become a new focus of cooperation between government and enterprises under environmental constraints, how local governments formulate subsidy policies to effectively guide the green transformation of regional logistics and how to facilitate the reasonable cost-sharing are rather critical. This paper will deeply explore the dynamic process of the tripartite participation (government, platform, and logistics enterprises) in the selection of regional green logistics strategy, and reveal the evolutionary game relationship of the three parties.

To explore the dynamics involving the government, platform and logistics enterprises for the green logistic transformation, and reveal the evolutionary gaming among the three parties, based on the bounded rationality premise, this study constructs the tripartite asymmetric evolutionary game models, uses the stability theorem of differential equation to explore the evolution and stability strategy of the system in different cases and explicates the paths of influence on the tripartite behaviors via simulations.

Results of this study indicate that there exist stable equilibrium strategies among the three parties regarding the regional green logistics, and they are affected by different factors. The government's subsidy, subsidy intensity and the platform's cost-sharing proportion can generate positive effects, but the latter two can also impact negatively beyond the effective ranges. The findings provide a theoretical basis for local governments, platforms and logistics enterprises to formulate justifiable subsidy intensity and determine reasonable sharing proportion.

Firstly, considering the significant relevance of local government, it is included in the evolution model, and the tripartite game (among government, platform and enterprises) is explored; Secondly, by comparing the equilibrium results under different game conditions, this paper analyzes the evolution of each party's game strategy to achieve the optimal return under bounded rationality and the important factors determining the strategic selection; Finally, the key factor of platform cost sharing is involved, and to what extent the change of platform cost sharing ratio will influence the systematic stability is explored.

There is a growing importance of business intelligence systems (BIS) adoption in today’s digital economy age which is characterized by uncertainty and ambiguity considering the magnitude and influence of data-related issues to be solved in contemporary businesses. This study aims to investigate critical success factors that affect business intelligence efficiency based on the DeLone and McLean model in Jordanian banking industry.

A quantitative research method through a questionnaire was used to collect data from actual users who depend on business intelligence tools to make operational and strategic decisions in Jordanian banks. The data obtained were tested using the partial least squares–structural equation modeling approach.

The survey findings attest that system quality, information quality, user quality, user satisfaction and user performance are important factors and contribute to business intelligence efficiency in the Jordanian banking industry.

The findings gained from this work can help policymakers in Jordanian banks to improve the business intelligence success and organizational performance.

To the best of the authors’ knowledge, this study is the first of its kind to propose a theoretical model to assess drivers of BIS efficiency from the Jordanian banks’ perspective.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

Bayesian cubature (BC) has emerged to be one of most competitive approach for estimating the multi-dimensional integral especially when the integrand is expensive to evaluate, and alternative acquisition functions, such as the Posterior Variance Contribution (PVC) function, have been developed for adaptive experiment design of the integration points. However, those sequential design strategies also prevent BC from being implemented in a parallel scheme. Therefore, this paper aims at developing a parallelized adaptive BC method to further improve the computational efficiency.

By theoretically examining the multimodal behavior of the PVC function, it is concluded that the multiple local maxima all have important contribution to the integration accuracy as can be selected as design points, providing a practical way for parallelization of the adaptive BC. Inspired by the above finding, four multimodal optimization algorithms, including one newly developed in this work, are then introduced for finding multiple local maxima of the PVC function in one run, and further for parallel implementation of the adaptive BC.

The superiority of the parallel schemes and the performance of the four multimodal optimization algorithms are then demonstrated and compared with the k-means clustering method by using two numerical benchmarks and two engineering examples.

Multimodal behavior of acquisition function for BC is comprehensively investigated. All the local maxima of the acquisition function contribute to adaptive BC accuracy. Parallelization of adaptive BC is realized with four multimodal optimization methods.

With the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent, and organized and purposeful cyberattacks have increased, posing more challenges to cybersecurity protection. Therefore, reliable network risk assessment methods and effective network security protection schemes are urgently needed.

Based on the dynamic behavior patterns of attackers and defenders, a Bayesian network attack graph is constructed, and a multitarget risk dynamic assessment model is proposed based on network availability, network utilization impact and vulnerability attack possibility. Then, the self-organizing multiobjective evolutionary algorithm based on grey wolf optimization is proposed. And the authors use this algorithm to solve the multiobjective risk assessment model, and a variety of different attack strategies are obtained.

The experimental results demonstrate that the method yields 29 distinct attack strategies, and then attacker's preferences can be obtained according to these attack strategies. Furthermore, the method efficiently addresses the security assessment problem involving multiple decision variables, thereby providing constructive guidance for the construction of security network, security reinforcement and active defense.

A method for network risk assessment methods is given. And this study proposed a multiobjective risk dynamic assessment model based on network availability, network utilization impact and the possibility of vulnerability attacks. The example demonstrates the effectiveness of the method in addressing network security risks.