Using Ant Colony Optimization to Identify Optimal Sample Allocations in Cluster-Randomized Trials

Mediation analyses in randomized controlled trials (RCTs) can unpack potential causal pathways between interventions and outcomes and help the iterative improvement of interventions. When designing RCTs investigating these mechanisms, two key considerations are (a) the sample size needed to achieve adequate statistical power and (b) the efficient use of resources. The current study has developed closed-form statistical power formulas for RCTs investigating mediation effects with and without covariates under the Sobel and joint significance tests. The power formulas are functions of sample size, sample allocation between treatment conditions, effect sizes in the treatment-mediator and mediator-outcome paths, and other common parameters (e.g., significance level, one- or two-tailed test). The power formulas allow us to assess how covariates impact the magnitude of mediation effects and statistical power. Accounting for the potential unequal sampling costs between treatment conditions, we have further developed an optimal design framework to identify optimal sample allocations that provide the maximum statistical power under a fixed budget or use the minimum resources to achieve a target power. Illustrations show that the proposed method can identify more efficient and powerful sample allocations than conventional designs with an equal number of individuals in each treatment condition. We have implemented the methods in the R package odr to improve the accessibility of the work. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Cutter head is one of the most important components for the full-face rock tunnel boring machine (TBM), in which the layout design of the disc cutters is a key issue. Considering the complex engineering technical requirements, this paper studied an ant colony optimization (ACO) algorithm to solve the disc cutters plane layout problem. The searching space of the design variables (disc cutters’ position) were dispersed by setting several different search steps, then an ACO algorithm was adopted to search the best searching step of each design variable dynamically during the whole optimization process. Finally, the disc cutters layout design instance was solved by the ACO method to demonstrate its feasibility and effectiveness. The computational results showed that that ACO method can provide various disc cutters layout schemes for engineers choosing from.KeywordsFull-face tunnel boring machineDisc cutterslayoutAnt Colony Optimization

In genome-wide epigenetic studies, it is of great scientific interest to assess whether the effect of an exposure on a clinical outcome is mediated through DNA methylations. However, statistical inference for causal mediation effects is challenged by the fact that one needs to test a large number of composite null hypotheses across the whole epigenome. Two popular tests, the Wald-type Sobel’s test and the joint significant test using the traditional null distribution are underpowered and thus can miss important scientific discoveries. In this article, we show that the null distribution of Sobel’s test is not the standard normal distribution and the null distribution of the joint significant test is not uniform under the composite null of no mediation effect, especially in finite samples and under the singular point null case that the exposure has no effect on the mediator and the mediator has no effect on the outcome. Our results explain why these two tests are underpowered, and more importantly motivate us to develop a more powerful divide-aggregate composite-null test (DACT) for the composite null hypothesis of no mediation effect by leveraging epigenome-wide data. We adopted Efron’s empirical null framework for assessing statistical significance of the DACT test. We showed analytically that the proposed DACT method had improved power, and could well control Type I error rate. Our extensive simulation studies showed that, in finite samples, the DACT method properly controlled the Type I error rate and outperformed Sobel’s test and the joint significance test for detecting mediation effects. We applied the DACT method to the U.S. Department of Veterans Affairs Normative Aging Study, an ongoing prospective cohort study which included men who were aged 21 to 80 years at entry. We identified multiple DNA methylation CpG sites that might mediate the effect of smoking on lung function with effect sizes ranging from –0.18 to –0.79 and false discovery rate controlled at the level 0.05, including the CpG sites in the genes AHRR and F2RL3. Our sensitivity analysis found small residual correlations (less than 0.01) of the error terms between the outcome and mediator regressions, suggesting that our results are robust to unmeasured confounding factors. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

The crowd-creation space is a manifestation of the development of innovation theory to a certain stage. With the creation of the crowd-creation space, the problem of optimizing the resource allocation of the crowd-creation space has become a research hotspot. The emergence of cloud computing provides a new idea for solving the problem of resource allocation. Common cloud computing resource allocation algorithms include genetic algorithms, simulated annealing algorithms, and ant colony algorithms. These algorithms have their obvious shortcomings, which are not conducive to solving the problem of optimal resource allocation for crowd-creation space computing. Based on this, this paper proposes an In the cloud computing environment, the algorithm for optimizing resource allocation for crowd-creation space computing adopts a combination of genetic algorithm and ant colony algorithm and optimizes it by citing some mechanisms of simulated annealing algorithm. The algorithm in this paper is an improved genetic ant colony algorithm (HGAACO). In this paper, the feasibility of the algorithm is verified through experiments. The experimental results show that with 20 tasks, the ant colony algorithm task allocation time is 93ms, the genetic ant colony algorithm time is 90ms, and the improved algorithm task allocation time proposed in this paper is 74ms, obviously superior. The algorithm proposed in this paper has a certain reference value for solving the creative space computing optimization resource allocation.

Framework for computationally efficient optimal crop and water allocation using ant colony optimization

With the continuous acceleration of the building process of urban modernization in China, the scale of buildings continues to expand, and more and more information equipment and electrical equipment are applied to high-rise buildings, which brings new challenges to the design of electricity for high-rise buildings. In order to ensure the normal operation of electric equipment and choose the reasonable electricity consumption and reduce the cost of investment and maintenance, it is necessary to discuss the wiring optimization problems of high-rise buildings. Therefore, this paper chooses the ant colony optimization to optimize the wiring design of the electrical equipment of the building intelligently. Based on the biological model of ant colony optimization, the traditional ant colony optimization is improved, and the ant colony optimization is proposed in the continuous space optimization problem. It optimizes the routing path of high-rise buildings by simulating the shortest path of the ant colony to find food. The computing results of power parameters show that, the improved ant colony optimization model proposed in this paper can shorten the length of high-rise building wiring, the control of voltage drop, line loss and other power parameters can improve the economic benefit of the algorithm, and it is feasible in the optimization of high-rise building wiring.

We present a novel multiuser detection (MUD) technique, based on ant colony optimization (ACO), for synchronous direct sequence code division multiple access (DS-CDMA) systems. ACO algorithms are based on the cooperative foraging strategy of real ants. While an optimal MUD design using an exhaustive search method is prohibitively complex, we show that the ACO based MUD converges to the optimal BER performance in relatively few iterations, providing 9.5% saving in computational complexity.

The wind turbine (WT) layout and electrical system layout plays a vital role in offshore wind farm (OWF) design. The wake effect has a significant impact on the power production of the OWF. WTs in wake region will not experience healthy wind; hence it affects the power production. The proper placement of WTs can reduce the wake effect in OWF. The optimal design of large OWF is based on combined approach of Ant colony optimization (ACO) with multiple travelling salesmen problem (MTSP) is presented. The objectives of the approach are to improve power production, minimize the length of cable and cable cost. By considering (a) placement of WT with consideration of wake effect, (b) placement of substation, (c) selection of submarine cables with higher reliability and minimal power loss, and (d) minimum length of WT cable routing with zero cross connection. ACO-MTSP approach is applied on large OWF connected with 280 WTs and results are compared with the outcome of reference OWF.

The assembly of a machine tool modular fixture with a complex structure, flexible layout and poor regularity characteristics is used as an example in this paper to establish an IOMT system for optimizing the assembly sequence of a machine tool modular fixture based on IOT technology driven by modern intelligent identification and acquisition technology, with the aim of addressing the shortcomings of existing assembly technology in the traditional serial design method. From the perspective of an inverse sequence of an assembly sequence, and combined with the disassembly sequence data extracted by the perceptual assembly layer control unit under the environment of the IOT, the disassembly sequence data can be mathematically expressed by using three mainstream heuristic algorithms (i.e. the artificial fish swarm algorithm, the genetic algorithm, and the ant colony algorithm), while considering the disassembly quality, disassembly cycle and cost. Then, the three algorithm models are used to analyse the example of a machine tool modular fixture, and the calculation efficiency and solution accuracy are comprehensively evaluated; Simulation results show that the ant colony algorithm has the highest computational efficiency and the highest accuracy in a complex data environment. Therefore, the construction of an IOMT system for optimizing the assembly sequence of a machine tool modular fixture can be realized by using the ant colony algorithm.

In order to solve the problems of poor balance and low efficiency of traditional dynamic allocation method of human resources, this paper proposes a new dynamic allocation method of human resources based on improved ant colony optimization. In order to improve the convergence of the traditional ant colony optimization algorithm and avoid falling into the local optimal solution, the ant colony optimization algorithm is improved from three aspects: heuristic function, state selection strategy and pheromone allocation mechanism. Based on the improved ant colony optimization algorithm, the objective function of human resource dynamic allocation is constructed on the basis of human resource workload, work efficiency and work ability index, so as to obtain the optimal allocation results and complete the dynamic allocation of human resources. The experimental results show that, compared with the traditional human resource allocation method, the distribution equilibrium and efficiency of the proposed method are higher, and the distribution equilibrium is basically consistent with the standard value, indicating that the practical application performance of the proposed method is stronger.

A variety of clusters are grouped together to form a multi-cluster environment which can tackle the computational needs of a system which cannot be addressed by a single cluster. Studying multi-cluster frameworks is turning challenging day by day as it requires contemporary tools to move alongside with rapidly development and enhanced complexity of one system. Job scheduling in considered as NP hard problem in parallel and distributed computing environments such as cluster, grid and clouds. The way jobs are scheduled by the scheduler is dependent on various factors like number of jobs, processor availability, arrival time etc. Metaheuristics techniques like Genetic Algorithms, Ant Colony Optimization, Artificial Bee Colony, Cuckoo Search, Firefly Algorithm, Bat Algorithm etc. are used by researchers to get near optimal solutions to job scheduling problems. This work addresses a scheduling problem with multiple objectives. The makespan and flowtime are minimized simultaneously solving the issue of optimal job allocation. This work also includes the detailed description of parallel computing and various types scheduling as well as scheduling environments. The performance of the multi-cluster environment is optimized by applying a novel meta-heuristic technique named Bacterial Foraging Optimization Algorithm. This algorithm has better convergence and is not affected by the size of problem. The proposed algorithm was evaluated for different job sets on 3 types of processor configurations. And the final values were compared to those of the existing algorithm. The results show that the proposed algorithm has performed better than the existing one and it can be concluded that the proposed algorithm is feasible and effective for optimal allocation of jobs.

Due to the individualized consumer needs, cloud manufacturing (CMfg) has been widely used in the optimization of available manufacturing resource allocation to enhance resource utilization and reduce energy consumption. However, efficient scheduling of tasks and subtasks under dynamic CMfg environments to these re-sources are challenging problems. This paper proposes a game theory based on task scheduling and model selection for effectively exploiting distributed manufacturing resources in CMfg, and the Nash equilibrium (NE) in this game theory is implemented by a double ant colony optimization (DACO) algorithm. Through this model, services provided by different providers can handle a batch of tasks in real-time. Besides, to satisfy different service providers and demanders, the proposed approach considers multiple task attributes simultaneously, including completion time, cost, service quality, service composition capability, service availability, energy consumption, service sustainability, service maintainability, and service trust. Simulation results demonstrate that the proposed method is not only effective for the relevant optimization objective but also can achieve great performance under real-time CMfg environments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —To provide the best production guides, the efficiency of configuration optimization of manufacturing resources is critical to the control and management of smart manufacturing systems. This paper investigates the dynamic scheduling problem for manufacturing services in CMfg. Previous task scheduling approaches fail to evaluate multiple factors together, like completion time, cost, and energy consumption. Also, the traditional scheduling method cannot respond to requests caused by service state changes in an efficient way. Therefore, in this paper, a game theory model that consists of a static scheduling sub-game and a dynamic selection sub-game is presented. This model is achieved by adopting a proposed double ant colony optimization algorithm that solves constrained non-linear programming. Simulation experiments shown in this paper prove that the proposed method outperforms existing scheduling methods in multiple aspects, including completion time and energy consumption. Also, this method can be readily implemented and incorporated into real production environments. Future work can improve the proposed method by analyzing the uncertainty during scheduling tasks and sharing the logistics resources on the same routes.

This paper presents an optimal kinematic controller design based on ant colony optimization (ACO) computing method for omnidirectional mobile robots with three independent driving wheels equally spaced at 120 degrees from one another. The optimal control parameters are obtained by minimizing the performance index using the proposed ACO computing method. These optimal parameters are used in the ACO-based kinematic controller to obtain better performance for omnidirectional mobile robots to achieve both trajectory tracking and stabilization. Simulation results are conducted to show the effectiveness and merit of the proposed ACO-based kinematic controller for omnidirectional mobile robots.

Currently, competitive markets place a great emphasis on various quality characteristics to customers. In the past, competition on the manufacturing of the bearing outer rings has primarily focused on the product hardness. Other quality characteristics of stain yield and roundness performance have recently become equally critical to fulfill customer satisfaction. To simultaneously meet all requirement criteria, the multiple response surface optimisation has focused on the use of desirability function approach to combine multiple response criteria into a single performance measure and to determine the optimal process design parameters of a pre-heat and heat chamber heaters, number of vacuum, gas fan speed in quenching chamber and partial gas pressure. This study proposed integrated algorithms with Taguchi orthogonal design and analysis and desirability function approach in forms of the path of composite ascent. There are three phases which use the conventional aspect of the steepest ascent via influential parameters, designed for moving toward the optiumum, use the ant colony optimisation (ACO) and hunting search (HuS) mechanism to present getting stuck at the local optimum. Results demonstrate that the integrated algorithms with the ACO and HuS elements are superior to the use of conventional composite ascent algorithm on average of 15.11 and 16.43 % improvement, respectively, for the overall responses. The confirmation tests on the actual manufacturing process based on the predicted operating conditions were verified and carried out to show statistically significant improvement at 95 % confidence interval.

During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the optimal design and operation of water distribution systems. More recently, ant colony optimization algorithms (ACOAs), which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to a number of benchmark combinatorial optimization problems. In this paper, a formulation is developed which enables ACOAs to be used for the optimal design of water distribution systems. This formulation is applied to two benchmark water distribution system optimization problems and the results are compared with those obtained using genetic algorithms. The findings of this study indicate that ACOAs are an attractive alternative to GAs for the optimal design of water distribution systems, as they outperformed GAs for the two case studies considered both in terms of computational efficiency and their ability to find near global optimal solutions.