Multilayer Genetic Algorithm Research Articles

Towards time-constrained task allocation in semi-opportunistic mobile crowdsensing

Mobile crowdsensing (MCS) is an emerging sensing paradigm that leverages mobile users carrying smart devices to perform sensing tasks. On the one hand, as the sensing scenarios become more realistic and complex, the impact of time attributes on task allocation is significantly increased. However, most of the existing works only treat time attributes as supplementary constraints and ignore the existence of multiple time constraints. On the other hand, considering the exploration and application of emerging models, making MCS more adaptive and diversified also becomes a research focus. In this paper, we propose the Multiple Time constrained Task Allocation problem in Semi-Opportunistic mobile crowdsensing (SO-MTTA), with the goal of maximizing the sensing value obtained by the platform. We prove that the SO-MTTA problem is NP-hard and design a Multi-Layer Genetic Algorithm (MLGA), which uses a low space complexity encoding method for three layers: User-path-task, and adds a conflict elimination operation to correct possible conflicts of constraints. Finally, we conduct experiments on both synthetic and real-world datasets to successfully verify the effectiveness of our proposed algorithm.

An effective self-adaptive bi-directional multi-layer genetic algorithm for resource and user allocations in networks

Transportation, telecommunication, electricity supply, and production–distribution systems all involve the construction and management of highly sophisticated networks. Without properly designing such networks, none of these systems work effectively. Network design problems, as the core of network-based decision making systems, can be used to model the technological design of networks. They are widespread and have many industrial applications. In telecommunication, usually an infrastructure is sought, as a combination of possible links and switches, which can best maintain a given level of data traffic between a number of servers and devices. This paper presents a solution to a problem in which in every period of time, the specification of some of the nodes in network may need to be altered. In the current procedure, all arcs and nodes are fixed and only the node parameters need to change. However, the presented solution strategy, because of its multi-layered characteristic, can be easily extended to the cases where arcs and nodes are also subject to change.The solution procedure has been tested on the cloud-based Internet Café problem. In this problem, different types of video games can use different amount of RAM, CPU, GPU, and video-RAM resources, with demand for each type of game varying dynamically in a specified period, e.g., over a day. Different servers provide these resources to dumb-terminals which act essentially as input–output devices, including display monitors, for games running on the servers. Each server needs to provide the necessary resources for the games currently played on all terminals served. In this way, depending on the demand of each terminal, at each period, the terminal’s capability can change dynamically, and it will act as a different virtual machine in different periods. Hence, the requirements of the Internet Café infrastructure change dynamically over time. The solution procedure has been tested on a series of generated large instances, and the results are very promising. For a total of 9 cases tested, the optimal solution is obtained, in a matter of minutes, for 3 cases. For an additional 2 cases, the solution obtained is within less than 2% of the optimal solution. The results are also reported for 3 additional test cases for a problem with over 13,000 users and 4,000 seats, demonstrating the procedure capability in handling extra-large problems.

Application of Feature Selection Based on Multilayer GA in Stock Prediction

This paper proposes a feature selection model based on a multilayer genetic algorithm (GA) to select the features of a high stock dividend (HSD) and eliminate the relatively redundant features in the optimal solution by using layer-by-layer information transfer and two-dimensionality reduction methods. Combining the ensemble model and time-series split cross-validation (TSCV) indicator as the fitness function solves the problem of selecting the fitness function for each layer. The symmetry character of the model is fully utilized in the two-dimensionality reduction processes, according to the change in data dimensions and the unbalanced characteristics of the HSD, setting the corresponding TSCV indicators. We built seven ensemble prediction models for actual stock trading data for comparison experiments. The results show that the feature selection model based on multilayer GA can effectively eliminate the relatively redundant features after dimensionality reduction and significantly improve the balancing accuracy, precision and AUC performance of the seven ensemble learning models. Finally, adversarial validation is used to analyze the differences in the balanced accuracy of the training and test sets caused by the inconsistent distribution of the data sets.

A Multilayer Genetic Algorithm for Automated Guided Vehicles and Dual Automated Yard Cranes Coordinated Scheduling

At present, a lot of studies on automatic terminal scheduling are aimed at the shortest operating time. An effective way to reduce the operating time is to increase the amount of operating equipment. However, people often ignore the additional costs and energy consumption caused by increasing the amount of equipment. This paper comprehensively considers the two aspects of the equipment operation time and equipment quantity matching. With the minimum total energy consumption of the operating equipment as the objective function, a cooperative scheduling model of Automated Guided Vehicles (AGVs) and dual Automated Yard Cranes (AYCs) is established. In the modelling process, we also considered the interference problem between dual Automated Yard Cranes (AYCs). In order to solve this complex model, this paper designs an improved multilayer genetic algorithm. Finally, the calculation results from CPLEX and a multilayer genetic algorithm are compared, and the effectiveness of the model and algorithm is proved by experiments. In addition, at the same time, it is proved that it is necessary to consider the interference problem of dual Automated Yard Cranes (AYCs), and the optimal quantity matching scheme for the equipment and the optimal temporary storage location is given.

Genetic algorithm for the cargo shunting cooperation between two hub-and-spoke logistics networks

Purpose: The overstocked goods flow in the hub of hub-and-spoke logistics network should be disposed of in time, to reduce delay loss and improve the utilization rate of logistics network resources. The problem we need to solve is to let logistics network cooperate by sharing network resources to shunt goods from one hub-and-spoke network to another hub-and-spoke network.Design/methodology/approach: This paper proposes the hub shunting cooperation between two hub-and-spoke networks. Firstly, a hybrid integer programming model was established to describe the problem, and then a multi-layer genetic algorithm was designed to solve it and two hub-and-spoke networks are expressed by different gene segments encoded by genes. The network data of two third-party logistics companies in southern and northern China are used for example analysis at the last step. Findings: The hub-and-spoke networks of the two companies were constructed simultaneously. The transfer cost coefficient between two networks and the volume of cargo flow in the network have an impact on the computation of hubs that needed to be shunt and the corresponding cooperation hubs in the other network.Originality/value: Previous researches on hub-and-spoke logistics network focus on one logistics network, while we study the cooperation and interaction between two hub-and-spoke networks. It shows that two hub-and-spoke network can cooperate across the network to shunt the goods in the hub and improve the operation efficiency of the logistics network.

Energy-efficient optimal task offloading in cloud networked multi-robot systems

Task offloading plays a critical role in cloud networked multi-robot systems for leveraging computation support from cloud infrastructure and benefiting greatly from the well-developed cloud network facilities. However, considering the delay constraint, the extra costs of data transmission and remote computation, it is not trivial to make optimized offloading decisions. In particular, task offloading for robots is more complex due to their on-demand mobility and network connectivity that significantly influence the robot–cloud communication links. Moreover, for multi-robot systems, a suitable balance of workload between local network (robot–robot) and global cloud (robot–cloud) is also required, so as to attain proper utilization of resources. Therefore, it is essential to establish more comprehensive offloading schemes for modeling systems that can handle these higher level of complications. With that view, this paper aims to develop a novel multi-layer decision-making scheme for task offloading which jointly considers the following four aspects: (i) selection of task for offloading, (ii) selection of robot to offload a task, (iii) selection of location to offload/perform task, (iv) selection of access point for offloaded task. An integrated framework for cloud networked multi-robot systems is presented to enable our task offloading scheme where the primary robot can aid from additional local robots to improve the offloading process. In particular, we consider a warehouse scenario with 36 cell workspace where a 40 node taskflow is motivated from a “parcel sorting and distribution” application, to be completed by the primary robot. The offloading decision for each task is formulated as part of a joint optimization problem and it is solved by developing a multi-layer genetic algorithm scheme that takes into account motion, network connectivity and local sharing for its offloading decisions. We evaluate the results of the scheme via comparison with two validated benchmarks. The outcome highlights a significant improvement in overall system performance due to joint involvement of motion (path planning), connectivity (bandwidth estimation) and robot–robot communication (local offloading) that facilitates energy-efficient offloading to cloud, faster completion of tasks and better utilization of available resources.

Research on Location and Transportation Route Optimization for Hazardous Chemical Waste Based on Multi-objective Constraints

Traditional literature has only considered the dual constraints of waste stacking and storage and vehicle transportation, and given little thought on environmental risk control. In light of this problem, this paper proposes a multi-objective optimization model considering cost, environmental risk and social risk and verifies the feasibility of the proposed model through an instance. The proposed cost-environment risk-social risk multi-objective optimization model is a multi-layer network structure. It considers the environmental capacity constraint and the environmental and social risks for recycling hazardous chemicals and performs clustering analysis based on the multi-layer genetic algorithm. The results show that compared with the optimization solution considering social risk only, the one considering environmental risk only reduces the total cost by about 66.98% and that the multi-objective optimization solution considering construction cost, environmental risk and social risk reduces the total cost by about 71.39%, indicating that environmental risk is the most important factor for the location-transportation route optimization scheme. In summary, the multi-objective optimization solution considering construction cost, environmental risk and social risk established in this paper can achieve the best overall optimization.

Integrated scheduling of handling operations in railway container terminals

The railway handling area of the railway container terminal is the main place for container sea–rail transportation. To enhance the efficiency of the handling systems and improve the coordination of different types of equipment in railway handling area, an integrated scheduling model is developed, which integrates the scheduling of rail-mounted gantry cranes (GCs), inner trucks (ITs), and yard cranes (YCs). This model considers not only train loading and unloading simultaneously, but GC interference and safety margin, GC and YC travel time as well as buffer area. A multi-layer genetic algorithm (MLGA) is proposed to solve the problem. Eventually, comparison experiments are conducted, including the experiments on handling process between non-optimization and optimization, MLGA with and without the balanced operator and the performance of different numbers of ITs. These experiments validate that the model and algorithm is feasible and efficient in both equipment scheduling and allocation problem.

Solving cross-matching puzzles using intelligent genetic algorithms

Cross-matching puzzles are logic based games being played with numbers, letters or symbols that present combinational problems. A cross-matching puzzle consists of three tables: solution table, detection table, and control table. The puzzle can be solved by superposing the detection and control tables. For the solution of the cross-matching puzzle, a depth first search method can be used, but by expanding the size of the puzzle, computing time can be increased. Hence, the genetic algorithm, which is one of the most common optimization algorithms, was used to solve cross-matching puzzles. The multi-layer genetic algorithm was improved for the solution of cross-matching puzzles, but the results of the multi-layer genetic algorithm were not good enough because of the expanding size of the puzzle. Therefore, in this study, the genetic algorithm was improved in an intelligent way due to the structure of the puzzle. The obtained results showed that an intelligent genetic algorithm can be used to solve cross-matching puzzles.

Multi-layer Genetic Algorithm for Maximum Disjoint Reliable Set Covers Problem in Wireless Sensor Networks

Densely deployment of sensors is generally employed in wireless sensor networks to ensure complete target coverage for a long period of time. Many sensors scheduling techniques have been recently proposed for prolonging the network lifetime. Scheduling sensors into a maximum number of disjoint sets has been modeled, in the literature, as disjoint set covers (DSC) problem which is a well-known NP-hard optimization problem. Unlike other attempts which considers only a simple disk sensing model, this paper addresses the problem of finding the maximum number of set covers while considering a more realistic sensing model to handle uncertainty into the sensors' target-coverage reliability. The paper investigates the development of a simple multi-layer genetic algorithm (GA) whose main ingredient is to support selection of a minimum number of sensors to be assigned to maximum number of set covers. With the aid of the remaining unassigned sensors, the reliability of DSCs provided by the GA, can further be enhanced by a post-heuristic step. By identifying the upper bound of number of disjoint set covers, the GA can successively construct covers, each of which is of minimal sensor cost. Performance evaluations on solution quality in terms of both number of set covers and coverage reliability are measured and compared through extensive simulations, showing the effectiveness of the proposed GA.

Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

A considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures such as beams or plates. In the application on a complex structure, such as steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates and then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple-damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.

Using a Multilayer Perceptrons Neural Network and Genetic Algorithm to Predict the NTD/USD Exchange Rate

In recent years, the global community has experienced economic difficulties, such as the 2008 financial crisis and the ongoing European debt crisis. Consequently, currency values have fluctuated significantly over short periods of time, which increases the difficulty of survival and fear of businesses that rely on import and export trade. Failure to properly and appropriately address the operational risks of exchange rate fluctuations can reduce corporate profit and even lead to operational losses. However, financial markets provide numerous methods for corporations to hedge the risks of exchange rate fluctuations. Nevertheless, a model for predicting exchange rate fluctuations can enable business owners to make more appropriate judgments. This study employs a multilayer perceptions (MLP) neural network with genetic algorithm (GA) to predict the New Taiwan dollar (NTD)/U.S. dollar (USD) exchange rate. The GA is used to determine the optimum number of input and hidden nodes for a feedforward neural network, the optimum slope of the activation function, and the optimum learning rates and momentum coefficients. The empirical results show that the ability of the proposed model to predict the NTD/USD exchange rate is excellent. The absolute relative error between the predicted value and the actual value was 0.2948%, and the correlation coefficient was 0.994802.

Correlation-Based Damage Detection for Complicated Truss Bridges Using Multi-Layer Genetic Algorithm

The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure's damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach's effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account.

Integrated scheduling of handling equipment at automated container terminals

To improve the productivities of an automated container terminal, it is important to schedule different types of handling equipment in an integrated way. A mixed-integer programming model, which considers various constraints related to the integrated operations between different types of handling equipment, is formulated. A heuristic algorithm, called multi-layer genetic algorithm (MLGA) is developed with a view to overcome the computation difficulty for solving the mathematical model. A numerical experimentation is carried out in order to evaluate the performance of the algorithm.

Integrated scheduling of handling equipment at automated container terminals

To improve the productivities of an automated container terminal, it is important to schedule different types of handling equipment in an integrated way. A mixed-integer programming model, which considers various constraints related to the integrated operations between different types of handling equipment, is formulated. This study proposes a heuristic method, called multi-layer genetic algorithm (MLGA) to obtain the near-optimal solution of the integrated scheduling problem and an improved heuristic algorithm, called genetic algorithm plus maximum matching (GAPM), to reduce the computation complexity of the MLGA method. The performance of GAPM is also compared with that of the MLGA method.