Distributed Genetic Algorithm Research Articles

Privacy-preserving data publishing: an information-driven distributed genetic algorithm

The privacy-preserving data publishing (PPDP) problem has gained substantial attention from research communities, industries, and governments due to the increasing requirements for data publishing and concerns about data privacy. However, achieving a balance between preserving privacy and maintaining data quality remains a challenging task in PPDP. This paper presents an information-driven distributed genetic algorithm (ID-DGA) that aims to achieve optimal anonymization through attribute generalization and record suppression. The proposed algorithm incorporates various components, including an information-driven crossover operator, an information-driven mutation operator, an information-driven improvement operator, and a two-dimensional selection operator. Furthermore, a distributed population model is utilized to improve population diversity while reducing the running time. Experimental results confirm the superiority of ID-DGA in terms of solution accuracy, convergence speed, and the effectiveness of all the proposed components.

Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm

Integrating Internet technologies with traditional healthcare systems has enabled the emergence of cloud healthcare systems. These systems aim to optimize the balance between online diagnosis and offline treatment to effectively reduce patients’ waiting times and improve the utilization of idle medical resources. In this paper, a distributed genetic algorithm (DGA) is proposed as a means to optimize the balance of patient assignment (PA) in cloud healthcare systems. The proposed DGA utilizes individuals as solutions for the PA optimization problem and generates better solutions through the execution of crossover, mutation, and selection operators. Besides, the distributed framework in the DGA is proposed to improve its population diversity and scalability. Experimental results demonstrate the effectiveness of the proposed DGA in optimizing the PA problem within the cloud healthcare systems.

Computational Methods for Two-Level 0-1 Programming Problems through Distributed Genetic Algorithms

In this paper, we consider a two-level 0-1 programming problem in which there is not coordination between the decision maker (DM) at the upper level and the decision maker at the lower level. We propose a revised computational method that solves problems related to computational methods for obtaining the Stackelberg solution. Specifically, in order to improve the computational accuracy of approximate Stakelberg solutions and shorten the computational time of a computational method implementing a genetic algorithm (GA) proposed by the authors, a distributed genetic algorithm is introduced with respect to the upper level GA, which handles decision variables for the upper level DM. Parallelization of the lower level GA is also performed along with parallelization of the upper level GA. The proposed algorithm is also improved in order to eliminate unnecessary computation during operation of the lower level GA, which handles decision variables for the lower level DM. In order to verify the effectiveness of the proposed method, we propose comparisons with existing methods by performing numerical experiments to verify both the accuracy of the solution and the time required for the computation.

Computational Methods for Two-Level 0-1 Programming Problems through Distributed Genetic Algorithms

In this paper, we consider a two-level 0-1 programming problem in which there is not coordination between the decision maker (DM) at the upper level and the decision maker at the lower level. We propose a revised computational method that solves problems related to computational methods for obtaining the Stackelberg solution. Specifically, in order to improve the computational accuracy of approximate Stakelberg solutions and shorten the computational time of a computational method implementing a genetic algorithm (GA) proposed by the authors, a distributed genetic algorithm is introduced with respect to the upper level GA, which handles decision variables for the upper level DM. Parallelization of the lower level GA is also performed along with parallelization of the upper level GA. The proposed algorithm is also improved in order to eliminate unnecessary computation during operation of the lower level GA, which handles decision variables for the lower level DM. In order to verify the effectiveness of the proposed method, we propose comparisons with existing methods by performing numerical experiments to verify both the accuracy of the solution and the time required for the computation.

A Parallel distributed genetic algorithm using Apache Spark for flexible scheduling of multitasks in a cloud manufacturing environment

ABSTRACT Cloud manufacturing is one of the modern manufacturing systems that aim to control and manage production processes through a cloud platform to optimize the usage of resources and achieve customer objectives. Scheduling large-scale problems with diversity in the required tasks and the available services is a significant challenge for any researcher, especially with the possibility of recurring dynamic events, such as the arrival of new tasks. Thus, our first objective is to present a mixed-integer mathematical model that demonstrates the complexity of scheduling problems in a dynamic CMfg environment. We developed a parallel distributed genetic algorithm (PDGA) for optimizing large-scale scheduling problems on Apache Spark. The PDGA adopts a resilient distributed dataset (RDD) to improve the decomposition of the population and also enhance both the performance and the speed of the evolution process. The PDGA then updates the schedule by using a greedy strategy to improve the overall performance. To verify the effectiveness of the PDGA, we evaluated the algorithm on a benchmark and generated 8 large-scale problems based on this benchmark. The experiments show that the PDGA provides better performance and lower computational time compared to the traditional genetic algorithm and particle swarm optimization algorithm.

Novel Robust Dynamic Distributed Drone-Deployment Strategy for Channel-Capacity Optimization for 3-D UAV-Aided Ad Hoc Networks

Unmanned aerial vehicles (UAVs) are considered to be excellent candidates of airborne relays or base stations for the three-dimensional ad hoc networks. They can be promptly deployed to serve large bursts of communication traffic in cellular networks or provide timely network support in wireless sensor networks (WSN). It is desirable but challenging to dynamically find the optimal deployment strategy of UAVs in the air to provide a better quality of service (QoS) for a UAV-assisted wireless network. In this paper, we propose a novel Gibbs-sampling distributed algorithm (GSDA) to dynamically optimize the UAVs’ locations when they serve as airborne base stations for ground users. In our proposed GSDA, channel capacity is adopted as the objective function and a distributed approach is employed such that each UAV is able to optimize its location independently and asynchronously. Furthermore, we propose a polynomial-regression-based predictor to make use of users’ moving trajectories and take advantage of the predicted users’ future locations to expedite the convergence of the GSDA. Meanwhile, we also compare our proposed GSDA with the existing distributed genetic algorithm. The asynchronization of UAV location updates and the location errors are also investigated to evaluate the robustness of the GSDA. Simulation results demonstrate that our proposed novel GSDA is quite robust and superior to the existing distributed genetic algorithm.

Implementation of roulette wheel and random selection methods in a hybrid intelligent system: A comparison study for two Islands and Subway distributions considering different router replacement methods

The Internet of Things (IoT) and 5G wireless networks have made it possible to connect intelligent devices with each other and to the Internet. Different kind of networks and applications are appearing and they support us in everyday life. The Wireless Mesh Networks (WMNs) have emerged as a good approach for edge computing and last miles networks. They can be applied in different scenarios and areas with difficult terrains. In order to deploy reliable and highly connected WMNs at low cost, we optimize the connectivity of mesh routers and the coverage mesh clients. In our previous work, we designed and implemented a hybrid intelligent system called WMN-PSODGA, which integrates two intelligent algorithms: Particle Swarm Optimization (PSO) and Distributed Genetic Algorithm (DGA). In this work, we implement in our system Two Islands and Subway distributions of mesh clients and evaluate the performance of different router replacement methods considering two selection methods: roulette wheel and random. We compare the simulation results for each selection and router replacement method and find the best scenarios in terms of coverage, connectivity, and load balancing. For each combination, the achieved network connectivity and client coverage are 100%, but the best scenarios in terms of load balancing are when we combine the random selection method and Constriction Method (CM) for the Two Islands distribution, and the random selection method and Random Inertia Weight Method (RIWM) for the Subway distribution.

A Hybrid Intelligent Simulation System for Building IoT Networks: Performance Comparison of Different Router Replacement Methods for WMNs Considering Stadium Distribution of IoT Devices.

As the Internet of Things (IoT) devices and applications proliferate, it becomes increasingly important to design robust networks that can continue to meet user demands at a high level. Wireless local area networks (WLANs) can be a good choice as IoT infrastructure when high throughput is required. On the other hand, wireless mesh networks (WMNs), which are WLANs with mesh topology following the IEEE802.11s standard, have many advantages compared to conventional WLANs. Nevertheless, there are some problems that need solutions. One of them is the node placement problem. In this work, we propose and implement a hybrid intelligent system that solves this problem by determining the position of mesh nodes by maximizing the mesh connectivity and the coverage of IoT devices. The system is based on particle swarm optimization (PSO), simulated annealing (SA), and distributed genetic algorithm (DGA). We compare the performance of three router replacement methods: constriction method (CM), random inertia weight method (RIWM), and rational decrement of Vmax method (RDVM). The simulation results show that RIWM achieves better performance compared to CM and RDVM because it achieves the highest connectivity while covering more clients than the other two methods.

Preventing Premature Convergence in Evolutionary Structure Determination of Complex Molecular Systems: Demonstration in Few-Nanometer-Sized TiCl4-Capped MgCl2 Nanoplates.

The combination of genetic algorithm-based global search and local geometry optimization enables nonempirical structure determination for complex materials such as practical solid catalysts. However, premature convergence in the genetic algorithm hinders the determination of the global minimum for complicated molecular systems. Here, we implemented a distributed genetic algorithm based on the migration from a structure database for avoiding the premature convergence, and thus we realized the structure determination for TiCl4-capped MgCl2 nanoplates with experimentally consistent sizes. The obtained molecular models are featured with a realistic size and nonideal surfaces, representing actual primary particles of catalysts.

Performance Evaluation of Chi-Square and Normal Distributions of Mesh Clients for WMNs Considering Five Router Replacement Methods

In our previous work, we implemented a simulation system to solve the node placement problem in WMNs considering Particle Swarm Optimization (PSO) and Distributed Genetic Algorithm (DGA), called WMN-PSODGA. In this paper, we compare Chi-square and Normal distributions of mesh clients for different router replacement methods. The router replacement methods considered are Constriction Method (CM), Random Inertia Weight Method (RIWM), Linearly Decreasing Inertia Weight Method (LDIWM), Linearly Decreasing Vmax Method (LDVM) and Rational Decrement of Vmax Method (RDVM). The simulation results show that for both distributions, the mesh routers cover all mesh clients for all router replacement methods. In terms of load balancing, Normal distribution shows better results than Chi-square. The best router replacement method for this distribution is LDIWM. Thus, the best scenario is the Normal distribution of mesh clients with LDIWM as a router replacement method.

A web interface for WMN-PSODGA hybrid intelligent simulation system: performance evaluation for different mesh client distributions

PurposeThe purpose of this paper is to implement a web interface for a hybrid intelligent system. By using the implemented web interface, one can find optimal assignments of mesh routers in wireless mesh networks (WMNs). This study evaluates the implemented system considering three distributions of mesh clients to solve the node placement problem in WMNs.Design/methodology/approachThe node placement problem in WMNs is well known to be a computationally hard problem. Therefore, intelligent algorithms are used for solving this problem. The implemented system is a hybrid intelligent system based on meta-heuristics algorithms: particle swarm optimization (PSO) and distributed genetic algorithm (DGA). The proposed system is called WMN-PSODGA.FindingsThis study carried out simulations using the implemented simulation system. From the simulations results, it was found that the WMN-PSODGA system performs better for chi-square distribution of mesh clients compared with Weibull and exponential distributions.Research limitations/implicationsFor simulations, three different distributions of mesh clients were considered. In the future, other mesh client distributions, number of mesh nodes and communication distance need to be considered.Originality/valueThis research work, different from other research works, implemented a hybrid intelligent simulation system for WMNs. This study also implemented a web interface for the proposed system, which make the simulation system user-friendly.

A comparison study of chi-square and uniform distributions of mesh clients for different router replacement methods using WMN-PSODGA hybrid intelligent simulation system

Wireless Mesh Networks (WMNs) are gaining a lot of attention from researchers due to their advantages such as easy maintenance, low upfront cost and high robustness. Connectivity and stability directly affect the performance of WMNs. However, WMNs have some problems such as node placement problem, hidden terminal problem and so on. In our previous work, we implemented a simulation system to solve the node placement problem in WMNs considering Particle Swarm Optimization (PSO) and Distributed Genetic Algorithm (DGA), called WMN-PSODGA. In this paper, we compare chi-square and uniform distributions of mesh clients for different router replacement methods. The router replacement methods considered are Constriction Method (CM), Random Inertia Weight Method (RIWM), Linearly Decreasing Inertia Weight Method (LDIWM), Linearly Decreasing Vmax Method (LDVM) and Rational Decrement of Vmax Method (RDVM). The simulation results show that for chi-square distribution the mesh routers cover all mesh clients for all router replacement methods. In terms of load balancing, the method that achieves the best performance is RDVM. When using the uniform distribution, the mesh routers do not cover all mesh clients, but this distribution shows good load balancing for four router replacement methods, with RIWM showing the best performance. The only method that shows poor performance for this distribution is LDIWM. However, since not all mesh clients are covered when using uniform distribution, the best scenario is chi-square distribution of mesh clients with RDVM as a router replacement method.

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

Wind Technologies for Wake Effect Performance in Windfarm Layout Based on Population-Based Optimization Algorithm

The focus of this study is under the auspices of China Steel Corporation, Taiwan, in carrying out the national energy policy of 2025 Non-Nuclear Home. Under this policy, an estimated 600 offshore wind turbines will be installed by 2025. In order to carry out the wind energy project effectively, a preliminary study must be conducted. In this article, we investigated the influence of the wake effect on the efficiency of the turbines’ layout in a windfarm. A distributed genetic algorithm is deployed to study the wind turbines’ layout in order to alleviate the detrimental wake effect. In the current stage of this research, the historical weather data of weather stations near the site of the 29th windfarm, Taiwan, were collected by Academia Sinica. Our wake effect resilient optimized windfarm showed superior performance over that of the conventional windfarm. Additionally, an operation cost minimization process is also demonstrated and implemented using an ant colony optimization algorithm to optimize the total length of the power-carrying interconnecting cables for the turbines inside the optimized windfarm.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

Purpose: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, antisaturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional-integral-derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

Distributed genetic algorithm for lifetime coverage optimisation in wireless sensor networks

In this paper, a protocol called distributed genetic algorithm for lifetime coverage optimisation (DiGALCO) is suggested to preserve the coverage and enhance the lifetime of a wireless sensor network (WSN). DiGALCO combines three energy-efficient schemes: virtual network subdivision into subfields, distributed cluster head selection in each subfield, followed by sensor activity scheduling-based genetic algorithm (GA) optimisation performed by each cluster head. DiGALCO works into rounds. Each round consists of three phases: discovery, cluster head selection and GA decision and sensing. The decision process, which results in an activity scheduling vector, is achieved by a cluster head node through executing the GA to pick out a set of sensors staying active for monitoring through the current sensing round. In comparison with other protocols, several experimental results were done by using OMNeT++ network simulator show that DiGALCO is capable of prolonging the lifetime of WSN and gives enhanced coverage performance.

WMN-PSODGA - An Intelligent Hybrid Simulation System for WMNs Considering Load Balancing

Wireless mesh networks (WMNs) are becoming an important networking infrastructure because they have many advantages, such as low cost and increased high-speed wireless Internet connectivity. In the authors' previous work, they implemented a hybrid simulation system based on particle swarm optimization (PSO) and distributed genetic algorithm (DGA), called WMN-PSODGA. Moreover, they added to the fitness function a new parameter for mesh router load balancing a number of covered mesh clients per router (NCMCpR). In this article, the authors consider Exponential, Weibull, and Normal distributions of mesh clients and carry out a comparison study. The simulation results show that the performance of the Exponential, Weibull and Normal distributions was improved by considering load balancing when using WMN-PSODGA. For the same number of mesh clients, the Normal distribution behaves better than the other distributions. This is because all mesh clients are covered by a smaller number of mesh routers and the standard deviation is improved by effectively using NCMCpR.

A Hybrid Intelligent Simulation System for Constructing IoT Networks: Performance Evaluation of WMN-PSODGA Simulation System Considering Different Router Replacement Methods

Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure because they have many advantages such as low cost and increased high-speed wireless Internet connectivity. In our previous work, we implemented a Particle Swarm Optimization (PSO) based simulation system, called WMN-PSO, and a simulation system based on Genetic Algorithm (GA), called WMN-GA, for solving node placement problem in WMNs. Then, we implemented a hybrid intelligent simulation system based on PSO and distributed GA (DGA), called WMN-PSODGA. Moreover, we added in the fitness function a new parameter for the load balancing of the mesh routers called NCMCpR (Number of Covered Mesh Clients per Router). In this paper, we consider five router replacement methods for WMN-PSODGA. The simulation results show that CM and LDVM router replacement methods have better performance than other methods. Comparing LDVM and CM, we see that LDVM has better behavior.

Distributed Genetic Algorithm for Lifetime Coverage Optimization in Wireless Sensor Networks

In this paper, a protocol called distributed genetic algorithm for lifetime coverage optimisation (DiGALCO) is suggested to preserve the coverage and enhance the lifetime of a wireless sensor network (WSN). DiGALCO combines three energy-efficient schemes: virtual network subdivision into subfields, distributed cluster head selection in each subfield, followed by sensor activity scheduling-based genetic algorithm (GA) optimisation performed by each cluster head. DiGALCO works into rounds. Each round consists of three phases: discovery, cluster head selection and GA decision and sensing. The decision process, which results in an activity scheduling vector, is achieved by a cluster head node through executing the GA to pick out a set of sensors staying active for monitoring through the current sensing round. In comparison with other protocols, several experimental results were done by using OMNeT++ network simulator show that DiGALCO is capable of prolonging the lifetime of WSN and gives enhanced coverage performance.

QoS-Aware Cloudlet Load Balancing in Wireless Metropolitan Area Networks

With advances in wireless communication technology, more and more people depend heavily on portable mobile devices for business, entertainments and social interactions. This poses a great challenge of building a seamless application experience across different computing platforms. A key issue is the resource limitations of mobile devices due to their portable size, however this can be overcome by offloading computation-intensive tasks from the mobile devices to clusters of nearby computers called cloudlets through wireless access points. As increasing numbers of people access the Internet via mobile devices, it is reasonable to envision in the near future that cloudlet services will be available for the public through easily accessible public wireless metropolitan area networks (WMANs). However, the outdated notion of treating cloudlets as isolated data-centers-in-boxes must be discarded as there are clear benefits to connecting multiple cloudlets together to form a network. In this paper we investigate how to balance the workload among cloudlets in an WMAN to optimize mobile application performance. We first introduce a novel system model to capture the response time delays of offloaded tasks and formulate an optimization problem with the aim to minimize the maximum response time of all offloaded tasks. We then propose two algorithms for the problem: one is a fast heuristic, and another is a distributed genetic algorithm that is capable of delivering a more accurate solution compared with the first algorithm, but at the expense of a much longer running time. We finally evaluate the performance of the proposed algorithms in realistic simulation environments. The experimental results demonstrate the significant potential of the proposed algorithms in reducing the user task response time, maximizing user experience.