Genetic Algorithm-based Solution Research Articles

Fuzzy multi-objective solid transportation problem: genetic algorithm-based solution approach

Fuzzy multi-objective solid transportation problem: genetic algorithm-based solution approach

Proof of Evaluation-based energy and delay aware computation offloading for Digital Twin Edge Network

The increasing availability of Internet of Things (IoT) applications has led to the development of new technologies. Specifically, the deployment of edge servers close to IoT devices has strengthened the edge computing paradigm. With the collaboration of Mobile Edge Computing (MEC) and cloud computing, delay-sensitive and computation-intensive tasks can be offloaded to the edge/cloud servers to improve system performance in terms of the delay and energy consumption of IoT devices. However, there is a need to schedule the computation tasks for an efficient management. More importantly, the task scheduling strategy can face data tampering attacks to deliberately modify, destroy or manipulate the decisions. To solve the above problems, in this paper, we newly propose to integrate digital twin and blockchain into the edge networks. However, it is unclear (i) how energy and delay-aware computation should be combined, and (ii) which mining computations should be executed for a secure task scheduling. The state-of-the-art focuses on task scheduling and blockchain mining, separately. Therefore, we propose a novel blockchain-based digital twin-edge network architecture where our proposed algorithm solves these two challenges at the same execution. We design a three-layer system architecture, composed of physical entity layer, digital twin edge layer and blockchain layer. In the physical entity layer, we formulate an energy and delay-aware task scheduling problem. In the digital twin edge layer, we propose a novel Proof of Evaluation (PoE)-based secure energy and delay-aware task scheduling algorithm where optimization is executed by the genetic algorithm implementation of Warehouse Location Problem (WLP). In the blockchain layer, the best-found solutions are shared with the topology in a blockchain. Here, each block includes the hash of the previous block, a genetic algorithm-based solution, nonce value, and a hash of whole blocks in the blockchain. Thus, we aim to execute the computation tasks with an acceptable delay in an energy-efficient manner and prevent data tampering attacks against the optimal computation decisions. We validate the outcomes of our PoE-based secure digital twin-edge network model with extensive evaluations. Since the proposed model distributes the task not only to the local device but also to the MEC and cloud server for delay awareness, it reduces the delay but consumes more energy. Nevertheless, the additional energy consumption can be neglected against the delay reduction. The proposed scheme is also more scalable to compare with the conventional solution. The numerical results clearly show that the proposed model provides energy and delay awareness, maintaining both data integrity and trustworthiness at the same execution of algorithm.

Bayesian optimization for demographic inference.

Inference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a model's parameters that maximize a certain log-likelihood. This log-likelihood is often expensive to evaluate in terms of time and hardware resources, critically more so for larger population counts. Although genetic algorithm-based solution has proven efficient for demographic inference in the past, it struggles to deal with log-likelihoods in the setting of more than three populations. Different tools are therefore needed to handle such scenarios. We introduce a new optimization pipeline for demographic inference with time consuming log-likelihood evaluations. It is based on Bayesian optimization, a prominent technique for optimizing expensive black box functions. Comparing to the existing widely used genetic algorithm solution, we demonstrate new pipeline's superiority in the limited time budget setting with four and five populations, when using the log-likelihoods provided by the moments tool.

A novel adaptive restarting genetic algorithm based congestion management

AbstractThis manuscript proposes a novel adaptive restarting genetic algorithm-based solution approach for rescheduling generation-based congestion control. The generator sensitivity values are considered to select generators to participate in the congestion management. The efficacy of the suggested technique is demonstrated on a 39-bus New England system and a modified IEEE 30 bus system, and a comparative study with other optimization strategies are established. The findings produced with the suggested technique for congestion management better the outcomes obtained with different methods. The presented approach ensures a superior convergence profile by eliminating local minima traps. This method also assists the independent system operator in managing congestion more efficiently.

Scheduling mobile dental clinics: A heuristic approach considering fairness among school districts.

Mobile dental clinics (MDCs) are suitable solutions for servicing people living in rural and urban areas that require dental healthcare. MDCs can provide dental care to the most vulnerable high-school students. However, scheduling MDCs to visit patients is critical to developing efficient dental programs. Here, we study a mobile dental clinic scheduling problem that arises from the real-life logistics management challenge faced by a school-based mobile dental care program in Southern Chile. This problem involves scheduling MDCs to treat high-school students at public schools while considering a fairness constraint among districts. Schools are circumscribed into districts, and by program regulations, at least 50% of the students in each district must receive dental care during the first semester. Fairness prevents some districts from waiting more time to receive dental care than others. We model the problem as a parallel machine scheduling problem with sequence-dependent setup costs and batch due dates and propose a mathematical model and a genetic algorithm-based solution to solve the problem. Our computational results demonstrate the effectiveness of our approaches in obtaining near-optimal solutions. Finally, dental program managers can use the methodologies presented in this work to schedule mobile dental clinics and improve their operations.

GA-IRACE: Genetic Algorithm-Based Improved Resource Aware Cost-Efficient Scheduler for Cloud Fog Computing Environment

The ever-growing number of Internet of Things (IoT) devices increases the amount of data produced on daily basis. To handle such a massive amount of data, cloud computing provides storage, processing, and analytical services. Besides this, real-time applications, i.e., online gaming, smart traffic management, and smart healthcare, cannot tolerate the high latency and bandwidth consumption. The fog computing paradigm brings the cloud services closer to the network edge to provide quality of service (QoS) to such applications. However, efficient task scheduling becomes critical for improving the performance due to the heterogeneous nature, resource-constrained, and distributed environment of fog resources. With an efficient task scheduling algorithm, the response time to application requests can be reduced along with bandwidth and cloud resource costs. This paper presents a genetic algorithm-based solution to find an efficient scheduling approach for mapping application modules in a cloud fog computing environment. Our proposed solution is based on the execution time as a fitness function to determine an efficient module scheduling on the available fog devices. The proposed approach has been evaluated and compared against baseline algorithms in terms of execution time, monetary cost, and bandwidth. Comprehensive simulation results show that the proposed approach offers a better scheduling strategy than the existing scheduler.

Time-cost trade-off model in GERT-type network with characteristic function for project management

Activity crashing is an efficient way for project acceleration but always leads to time–cost trade-off issues. The current study proposes an optimal time–cost trade-off model in GERT (Graphical Evaluation and Review Technique) with consideration to both the probability of on-time delivery and under-budget. Firstly, the hypothesis in the previous literature that the minimization of expected completion time is equivalent to the maximization of the on-time completion probability is proven to be invalid. Secondly, to ensure successful project completion, the constraints on the probability of on-time delivery and under-budget are added in the trade-off model. Furthermore, the characteristic function-enhanced GERT is designed to derive the probability density function of project time and cost, thereby yielding the on-time probability and under-budget probability. Consequently, the stochastic programming model for time–cost trade-offs is presented to minimize the mean duration while ensuring a desired on-time completion probability and under-budget probability. To solve the proposed stochastic programming, a genetic algorithm-based solution procedure is designed. Finally, a numerical example is employed to demonstrate the effectiveness of the proposed model. The study contributes to our understanding that minimizing mean duration cannot necessarily maximize on-time project completion. It also helps us reduce the mean project duration and guarantee high on-time delivery and under-budget performance simultaneously, which are two critical success factors in project management.

Heuristic Strategies for NFV-Enabled Renewable and Non-Renewable Energy Management in the Future IoT World

The ever-growing energy demand and the CO2 emissions caused by energy production and consumption have become critical concerns worldwide and drive new energy management and consumption schemes. In this regard, energy systems that promote green energy, customer-side participation enabled by the Internet of Things (IoT) technologies, and adaptive consumption mechanisms implemented on advanced communications technologies such as the Network Function Virtualization (NFV) emerge as sustainable and de-carbonized alternatives. On these modern schemes, diverse management algorithmic solutions can be deployed to promote the interaction between generation and consumption sides and optimize the use of available energy either from renewable or non-renewable sources. However, existing literature shows that management solutions considering features such as the dynamic nature of renewable energy generation, prioritization in energy provisioning if needed, and time-shifting capabilities to adapt the workloads to energy availability present a complexity NP-Hard. This condition imposes limits on applicability to a small number of energy demands or time-shifting values. Therefore, faster and less complex adaptive energy management approaches are needed. To meet these requirements, this paper proposes three heuristic strategies: a greedy strategy (GreedyTs), a genetic-algorithm-based solution (GATs), and a dynamic programming approach (DPTs) that, when deployed at the NFV domain, seeks the best possible scheduling of demands that lead to efficient energy utilization. The performance of the algorithmic strategies is validated through extensive simulations in several scenarios, demonstrating improvements in energy consumption and processing of demands. Additionally, simulation results reveal that the heuristic approaches produce high-quality solutions close to the optimal while executing among two and seven orders of magnitude faster and with applicability to scenarios with thousands and hundreds of thousands of energy demands.

Genetic algorithm-based solution of multi-objective stochastic transportation problem

In transportation problem (TP), a decision-maker (DM) always wishes to optimise the given objectives by effectively transporting a given item from several sources to several destinations. The prese...

Genetic algorithm-based solution of multi-objective stochastic transportation problem

Genetic algorithm-based solution of multi-objective stochastic transportation problem

Optimized Reactive Power Supports Using Transformer Tap Stagger in Distribution Networks

In distribution networks, the operation of two parallel transformers in small different tap positions, i.e., known as tap stagger, can provide a means of absorbing reactive power. The aggregated VAr absorption from many pairs of parallel transformers could help mitigate the high voltages in the upstream transmission system during periods of low demand. Considering distribution networks as reactive power support providers, this paper presents an optimal control strategy for the tap stagger to minimize the introduced network losses, as well as the tap switching operations. A genetic algorithm-based solution procedure is developed to find the optimal dispatch for the transformer tap positions in order to achieve the VAr absorption required by the transmission system. The proposed method is compared with two alternative control approaches based on the rule-based method and the branch-and-bound algorithm. Two practical U.K. distribution networks are modeled from real data and used to demonstrate the effectiveness of the proposed control strategy under different operating conditions.

A Note on Transit Coordination Using Integer-Ratio Headways

Recently, in a paper published in IEEE Transactions on Intelligent Transportation Systems , the authors proposed a genetic algorithm-based solution approach to the transit coordination problem using integer-ratio headways. This note provides more accurate mathematical formulations of the cost components of the objective functions considered in the transit coordination optimization, to improve precision in evaluating the cost effectiveness of the coordinated transit timetable design. In addition, this note discusses a new cost component and some feasible, real-time, operational control strategies and tactics that can be used to achieve better transit transfer synchronization.

Interference coordination strategy based on Nash bargaining for small‐cell networks

In this study, a distributed scheme based on the Nash bargaining model is designed to coordinate co-channel interference for small-cell networks. The authors consider a scenario that resource blocks can be reused among different small cells. Different to existing works where resource allocation is conducted at the base stations, they propose the scheme where user initialises resource bargaining request to the serving base station once its quality-of-service cannot be satisfied because of the severe co-channel interference from other users. Since the general bargaining problem is a non-linear integer optimisation, the genetic algorithm is utilised to solve it. They also develop a low-complexity bargaining model which only takes into account the strongest co-channel interference. Simulation results show that the proposed distributed scheme can effectively reduce the outage probability of users and improve the system throughput. In addition, the proposed low-complexity bargaining solution can achieve a close performance to the genetic algorithm-based solution.

Large-scale evacuation network optimization: a bi-level control method with uncertain arterial demand

To improve the efficiency of large-scale evacuations, a network aggregation method and a bi-level optimization control method are proposed in this paper. The network aggregation method indicates the uncertain evacuation demand on the arterial sub-network and balances accuracy and efficiency by refining local road sub-networks. The bi-level optimization control method is developed to reconfigure the aggregated network from both supply and demand sides with contraflow and conflict elimination. The main purpose of this control method is to make the arterial sub-network to be served without congestion and interruption. Then, a corresponding bi-objective network flow model is presented in a static manner for an oversaturated network, and a Genetic Algorithm-based solution method is used to solve the evacuation problem. The numerical results from optimizing a city-scale evacuation network for a super typhoon justify the validity and usefulness of the network aggregation and optimization control methods.

Redesigning a Supply Chain Distribution Network: Formulation and Genetic Algorithm-Based Solution Procedure

Distribution affects company cost and customer satisfaction. Distribution network design is a strategic concern for nearly all companies, and must be periodically reviewed to ensure fitness to today's rapidly changing business environment. Unlike most of previous research, this paper investigates a highly complicated and realistic distribution network, and develops a multi-objective model to achieve an effective redesign solution. Reconfiguring cost, service differentiation, multiple products, and optional capacity levels are simultaneously considered. A determinant code-based encoding structure is designed to represent the complicated multi-objective optimization problem, and a genetic algorithm (GA)-based procedure is developed as a solution. The paper provides an application example using actual data. Finally, a comparison between the empirical results and those of the Prüfer-based GA demonstrate the efficiency of the proposed GA.

A Comparative Study of Proposed Genetic Algorithm-Based Solution with Other Algorithms for Time-Dependent Vehicle Routing Problem with Time Windows for E-Commerce Supply Chain

The vehicle routing problem (VRP) is classified as an NP-hard problem. Hence exact optimization methods may be difficult to solve these problems in acceptable CPU times, when the problem involves real-world data sets that are very large. To get solutions in determining routes which are realistic and very close to the optimal solution, one has to use heuristics and meta-heuristics. In this paper, an attempt has been made to develop a GA based meta-heuristic to solve the time dependent vehicle route problem with time windows (TDVRPTW). This algorithm is compared with five other existing algorithms in terms of minimizing the number of vehicles used as well as the total distance travelled. The algorithms are implemented using Matlab and HeuristicLab optimization software. A plugin was developed using Visual C# and NET Framework 4.5. Results were tested using Solomon’s 56 benchmark instances (of which 24 instances are used with 4 in each of the 6 problem classes) classified into groups such as C1, C2, R1, R2, RC1, and RC2. For each of the performance measures, through a complete factorial experiment with two factors, it is proved that the proposed algorithm is the best among all the six algorithms compared in this paper.

Research on Genetic Algorithm-Based Solution Method for Variable Cycle Engine Model

Variable cycle engine is a complex system, which is usually mathematically modeled as a series of multi-dimensional nonlinear implicit equations. Processes for solution of these equations are often complicated; therefore, a genetic algorithm-based method was presented in this paper for the solution of the mathematical model. The method was also evaluated by such parameters as initial value sensitivity, computation efficiency, convergence and stability; and compared with Newton-Raphson method. It shows that genetic algorithm-based method is less sensitive to initial values, more capable in convergent and computing stability than Newton-Raphson method, however more time consuming.

A priority rule-based constructive heuristic and an improvement method for balancing assembly lines with parallel multi-manned workstations

Assembly lines of big-size products such as buses, trucks and helicopters are very different from the lines studied in the literature. These products’ manufacturing processes have a lot of tasks most of which have long task times. Since traditional assembly line models including only one worker in each station (i.e. simple assembly lines) or at most two workers (two-sided assembly lines) may not be suitable for manufacturing these type of products, they need much larger shop floor for a number of stations and long product flow times. In this study, an assembly line balancing problem (ALBP) with parallel multi-manned stations is considered. Following the problem definition, a mixed integer programming formulation is developed. A detailed study of priority rules for simple ALBPs is also presented, and a new efficient constructive heuristic algorithm based on priority rules is proposed. In order to improve solutions found by the constructive heuristic, a genetic algorithm-based solution procedure is also presented. Benchmark instances in the literature are solved by using the proposed mathematical programming formulation. It has been seen that only some of the small-size instances can be solved optimally by this way. So the efficiency of the proposed heuristic method is verified in small-size instances whose optimal solutions are found. For medium- and big-size instances, heuristics’ results and CPU times are demonstrated. A comparative evaluation with a branch and bound algorithm that can be found in the literature is also carried out, and results are presented.

Integrating renewables economic dispatch with demand side management in micro-grids: a genetic algorithm-based approach

Economic dispatch and demand side management are two of the most important tools for efficient energy management in the grid. It is a casual observation that both these processes are intertwined and thus complement each other. Strategies aiming to optimize economic dispatch have implications for demand side management techniques and vice versa. In this paper, we present a genetic algorithm-based solution which combines economic dispatch and demand side management for residential loads in a micro-grid. Our system collects preferences of demand data from consumers and costs of energy of various sources. It then finds the optimal demand scheduling and energy generation mix for the given time window. Our evaluations show that the given approach can effectively reduce operating costs in a single- and multiple-facility micro-grids for both suppliers and consumers alike.

Placement of Roadside Equipment in Connected Vehicle Environment for Travel Time Estimation

Vehicle-to-infrastructure (VTI) communication technology offers great potential to improve safety and mobility in transportation systems. One application of VTI communication to mobility is the measurement of travel time in an urban road network. The presence of traffic control at intersections in an urban network makes it challenging for traditional traffic-monitoring methods to provide accurate travel times. The use of several new technologies (e.g., Bluetooth) has been proposed to alleviate this problem. One of the features of the VTI communication technology is probe vehicle data collection, in which vehicles collect information on, for example, their location and speed. The speed information can be used for travel time estimation. This paper proposes a methodology for determining the optimal placement of roadside equipment (RSE) for travel time estimation in a VTI communication environment. A connected vehicle simulation test bed of Boise, Idaho, was developed in VISSIM traffic simulation software according to the SAE J2735 standard. A hybrid performance measure, the network coverage index, which combines travel time error and the number of links for which travel times are available, is proposed. A genetic algorithm-based solution method was implemented in conjunction with the simulation test bed to determine the optimal placement of different RSE deployments. The results indicate that the proposed methodology is capable of optimization of RSE locations in a VTI communication environment. Sensitivity analysis was also conducted by the use of various market penetration rates and travel time estimation intervals. The results indicate that higher penetration rates and larger estimation intervals produce better coverage index values.