Efficient Heuristic Algorithm Research Articles

DEEP-FEL: Decentralized, Efficient and Privacy-Enhanced Federated Edge Learning for Healthcare Cyber Physical Systems

The rapid development of Internet of Things (IoT) stimulates the innovation for the health-related devices such as remote patient monitoring, connected inhalers and ingestible sensors. Simultaneously, with the aid of numerous equipments, a great number of collected data can be used for disease prediction or diagnosis model establishment. However, the potential patient data leak will also bring privacy and security issues in the interaction period. To deal with these existing issues, we propose a decentralized, efficient, and privacy-enhanced federated edge learning system called DEEP-FEL, which enables medical devices in different institutions to collaboratively train a global model without raw data mutual exchange. Firstly, we design a hierarchical ring topology to alleviate centralization of the conventional training framework, and formulate the ring construction as an optimization problem, which can be solved by an efficient heuristic algorithm. Subsequently, we design an efficient parameter aggregation algorithm for distributed medical institutions to generate a new global model, and the total amount of data transmitted by N nodes is only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2/N$</tex-math></inline-formula> times that of traditional algorithm. In addition, data security among different medical institutions is enhanced by adding artificial noise to the edge model. Finally, experimental results on three medical datasets demonstrate the superiority of our system.

Optimal Choice and Allocation of Shunt FACTS Devices Using Differential Search Algorithm

In this paper, new and efficient heuristic algorithm, Differential Search Algorithm (DSA), is used for optimal choice and allocation of shunt Flexible AC transmission systems (FACTS) devices, Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM). In the proposed optimal location method, SVC and STATCOM devices are located randomly and dynamically with objective of minimizing the real power loss, improving the voltage profile and enhancing the voltage stability. The best solution sets for objectives aforementioned are achieved by minimizing each objective functions by the proposed approach. In order to evaluate the proposed methodology, it has been tested on IEEE 30 bus system and compared to another widely used optimization algorithm, Artificial Bee Colony (ABC) algorithm. The validity of the proposed optimization algorithm is proven by the results and comparative analysis with other method.

LUEM : Local User Engagement Maximization in Networks

Understanding a social network is a fundamental problem in social network analysis because of its numerous applications. Recently, user engagement in networks has received extensive attention from many research groups. However, most user engagement models focus on global user engagement to maximize (or minimize) the number of engaged users. In this study, we formulate the so-called Local User Engagement Maximization (LUEM) problem. We prove that the LUEM problem is NP-hard. To obtain high-quality results, we propose an approximation algorithm that incorporates a traditional hill-climbing method. To improve efficiency, we propose an efficient pruning strategy while maintaining effectiveness. In addition, by observing the relationship between the degree and user engagement, we propose an efficient heuristic algorithm that preserves effectiveness. Finally, we conducted extensive experiments on ten real-world networks to demonstrate the superiority of the proposed algorithms. We observed that the proposed algorithm achieved up to 605% more engaged users compared to the best baseline algorithms.

An efficient framework for brownout based appliance scheduling in microgrids

Future generation Smart Grids are transforming into networks of small scale microgrids. Further, in developing nations where severe power deficits are set to remain a harsh reality, such microgrids are expected to be equipped with brownout based power management in order to avoid rolling blackouts, which affect complete denial of power to a service area during power shortage. Brownouts allow power provisioning to selective loads while curtailing power supply to others. Given the electricity demands of appliances associated with a set of establishments in a microgrid, this work presents a novel centralized, price-induced brownout based appliance scheduling mechanism. The consumers express their appliance’s priority/urgency towards uninterrupted power supply by subscribing to appropriate price tariffs and notify their preferred operation intervals. Two different types of appliances have been considered, i.e., rigid (non-deferrable but curtailable) and elastic (deferrable). We first formulate the scheduling problem as an Integer Linear Programming (ILP) problem with the objective of maximizing total revenue and show that such an optimal strategy incurs high overheads in terms of solution generation times. Therefore, we have proposed a fast yet efficient heuristic algorithm namely, Revenue-aware Appliance Scheduler (RaAS), which is able to produce appreciably good solutions which are only about 2% lower on average, than the optimal ones. However, the solution generation times taken by RaAS are about 2 8 times faster than the optimal ILP based strategy. The proposed algorithm is extensively evaluated by conducting experiments on various empirically generated microgrid scenarios. RaAS is found to be scalable to microgrids with significantly large number of establishments. • A novel price based demand response scheme has been proposed. • A comprehensive system model has been presented, which takes into consideration both rigid (curtailable) and elastic (shiftable) type of appliance demands. • The appliance scheduling problem has been formulated as an Integer Linear Programming (ILP). • The problem is NP Hard thus, we propose an efficient heuristic algorithm.

Joint optimization combining the capacity of subway on‐board energy storage device and timetable

On-board energy storage devices (OESD) and energy-efficient train timetabling (EETT) are considered two effective ways to improve the usage rate of regenerative braking energy (RBE) of subway trains. EETT is less costly but has lower ceilings, whereas OESD, although expensive, maximizes the reuse of RBE. To make the RBE usage rate of the train group reach the required target, a typical problem is how to use the OESD with the minimum capacity to reduce the cost. With this consideration, this paper proposes an optimization method that combines EETT and OESD. In this method, the joint optimization of timetable and OESD capacity allocation in case OESDs of different capacities can be carried between trains is considered to further improve the optimization. A non-linear programming model is built to minimize the total OESD capacity of the train group under the constraint of reaching target RBE usage. An efficient heuristic algorithm is designed to simultaneously obtain the optimal timetable and the matching capacity allocation scheme of OESDs. Finally, some simulation cases based on real-line data are designed to verify the effectiveness of the method.

Unrelated Parallel Machine Scheduling with Job Splitting, Setup Time, Learning Effect, Processing Cost and Machine Eligibility

This work investigates an unrelated parallel machine scheduling problem in the shared manufacturing environment. Based on practical production complexity, five job and machine-related factors, including job splitting, setup time, learning effect, processing cost and machine eligibility constraint, are integrated into the considered problem. Parallel machines with uniform speed but non-identical processing capabilities are shared on a sharing service platform, and jobs with different types can only be processed by the machines with matching eligibilities. The platform pays an amount of processing cost for using any machine to process the jobs. To balance the processing cost paid and the satisfaction of customers, we aim to minimize the weighted sum of total processing cost and total completion time of jobs in the considered problem. We establish a mixed integer linear programming model, and provide a lower bound by relaxing the machine eligibility constraint. The CPLEX solver is employed to generate optimal solutions for small-scale instances. For large-scale instances, we propose an efficient heuristic algorithm. Experimental results demonstrate that for various instance settings, the proposed algorithm can always produce near optimal solutions. We further present several managerial insights for the shared manufacturing platform.

Reconnaissance and Confirmation Task Planning of Multiple Fixed-Wing UAVs with Specific Payloads: A Comparison Study

In this study, the reconnaissance and confirmation task planning of multiple fixed-wing unmanned aerial vehicles (UAV) with specific payloads, which is an NP-hard problem with strong constraints and mixed variables, is decomposed into two subproblems, task allocation with “payload-target” matching constraints, and fast path planning of the UAV group, for which two mathematical models are respectively established. A bi-layer collaborative solution framework is also proposed. The outer layer optimizes the allocation scheme between the UAVs and targets, whereas the inner layer generates the UAV path and evaluates the outer scheme. In the outer layer, a unified encoding based on the grouping and pairing relationship between UAVs and targets is proposed. The corresponding combinatorial mutation operators are then designed for the representative NSGA-II, MOEA/D-AWA, and DMOEA-ϵC algorithms. In the inner layer, an efficient heuristic algorithm is used to solve the path planning of each UAV group. The simulation results verify the effectiveness of the cooperative bi-layer solution scheme and the combined mutation operators. At the same time, compared with the NSGA-II and MOEA/D-AWA, DMOEA-ϵC can obtain a significantly better Pareto front and can weigh the assigned number of UAVs and the total task completion time to generate more diversified reconnaissance confirmation execution schemes.

Effects of dispatch strategies on optimum sizing of solar-diesel-battery energy storage-RO desalination hybrid scheme by efficient heuristic algorithm

Dispatch strategies on optimum sizing of hybrid renewable energy systems are crucial for attaining a cost-effective and reliable power supply. In this paper, the effects of dispatch strategies on optimum sizing of photovoltaic/energy storage/diesel/ reverse osmosis desalination hybrid scheme for supplying the electrical loads and freshwater demand of a remote region are presented. The size of the components of the hybrid scheme are optimized to minimize the total net annual cost and minimizing the system's Levelized cost of energy and to minimize the cost of the desalinated water produced, and the reliability index. For this aim, two optimal dispatch strategies, load following, and cycle charging, are designed for integrated energy systems. So an efficient heuristic algorithm, tabu search, is used for optimum sizing of the hybrid energy scheme. The results are compared with the results obtained by well-known optimization method, harmony search. In each of the strategies, the effects of reliability index and economic factors, including diesel generator initial cost, fuel cost, the O&M cost of diesel generator, initial battery cost, photovoltaic initial cost, and the interest rate, on the optimization of the hybrid energy scheme have been investigated. It showed that the tabu search algorithm has potential benefit to reduce total cost by nearly 1.8% and average simulation time by nearly 1.65% as compared to the harmony search algorithm. Also, in a specific reliability index, the cycle charging strategy has potential benefit to reduce greenhouse gas emissions by nearly 11.5% per year and the cost criteria by nearly 7.5% as compared to the load following strategy.

Compartmental observability approach for the optimal transparency problem in multi-agent systems

This paper considers a network of agents, where each agent is assumed to take actions optimally with respect to a predefined payoff function involving the latest actions of the agent’s neighbors. Neighborhood relationships stem from payoff functions rather than actual communication channels between the agents. A principal is tasked to optimize the network’s performance by controlling the information available to each agent with regard to other agents’ latest actions. The information control by the principal is done via a compartmental observability approach, which comprises a static partitioning of agents into blocks and making the mean of agents’ latest actions within each block publicly available. While the problem setup is very general in terms of the payoff functions and the network’s performance metric, the paper has a narrower, yet still fairly general, focus to display essential characteristics of the problem and to show how it can be addressed in practice. In particular, the performance metric is assumed to be a function of the steady-state behavior of the agents. After conducting a comprehensive steady-state analysis of the network, two efficient heuristic algorithms finding nearly-optimal partitions with respect to various performance metrics are presented and validated via numerical studies.

Maximizing influence in social networks by distinguishing the roles of seeds

The problem of influence maximization in social networks has attracted much attention, and some algorithms have been proposed. However, the existing methods may be not suitable for large-scale networks because of high time complexity or failing to achieve the great performance. Given that the impacts of seeds in the loose neighbors (i.e., only including one-hop path with the candidate node) and the close neighbors (i.e., including one-hop and two-hop paths with the candidate node, and they form a closed triad) on the degree discount of candidate node are different. Moreover, when selecting multiple nodes as the seeds, we should not only consider the importance of the seeds themselves, but also ensure that the seeds are sufficiently dispersed to avoid the redundancy of propagation. To the end, we propose an efficient heuristic algorithm for influence maximization in social networks by considering redundancy weakening and two types of seeds into degree discount (named RWTDD). Based on the independent cascade model, the proposed RWTDD is compared with some well-known heuristic algorithms and greedy algorithms in six real social networks, experimental results indicate that the proposed RWTDD has a better influence coverage and low time complexity.

Line-haul feeder location-routeing problem: mathematical model and heuristic algorithm

This study proposes a mathematical formulation and solution approach for a novel extension of the location-routeing problem (LRP), namely line-haul feeder LRP (LFLRP), where large vehicles (trucks) are synchronised with small vehicles (motorcycles) throughout delivery process. Customers are visited by site-dependent vehicles such that those not accessible by trucks must be served by motorcycles. The LFLRP is formulated as a mixed-integer linear programming model, and two efficient heuristic algorithms called EHA and Enhanced-EHA are developed to solve the problem. Experimental results show that the proposed algorithms can provide near-optimal solutions for 18 randomly generated small-scale LFLRP test instances and best-known solutions for 12 out of 19 large-scale standard LRP test instances in reasonable computation time. A cost-benefit analysis also indicates that the LFLRP model can considerably reduce total costs compared to equivalent standard LRP formulations. To provide managerial insights, a case study and sensitivity analysis of key parameters are conducted.

Parallel batch processing machines scheduling in cloud manufacturing for minimizing total service completion time

This paper considers the integrated scheduling problem of production and delivery on parallel batch processing machines with non-identical capacities in different locations in cloud manufacturing. It is assumed that the jobs have arbitrary release times, different job sizes, unequal processing times, and unique information of customers submitting jobs. The service completion time of a job is the sum of the production completion time and the delivery duration. The objective of the studied problem is to minimize the total service completion time. A mixed-integer programming (MIP) model is presented to solve this problem. Since the problem is NP-hard, an efficient heuristic algorithm and an improved particle swarm optimization algorithm are proposed. The two proposed algorithms are compared with several state-of-the-art algorithms and the commercial optimization solver (Gurobi) through extensive experiments, verifying the effectiveness of the proposed algorithms.

Optimization models for fair horizontal collaboration in demand-responsive transportation

The advances in information and communication technology are changing the way people move. Companies that offer demand-responsive transportation services have the opportunity to reduce their costs and increase their revenues through collaboration, while at the same time reducing the environmental impact of their operations. In this paper, we consider the case of companies, offering a shared taxi service, that are involved in horizontal collaboration, and present mixed integer programming models for the optimization of their routes that embed constraints aimed at controlling the workload exchange of each company with others. These constraints bound the workload exchange in terms of traveled time and/or served customers to be less than thresholds agreed in advance by the companies. We also present an efficient heuristic algorithm and show the effectiveness of the heuristic and the benefits of the collaboration.

Maximizing User Service Satisfaction for Delay-Sensitive IoT Applications in Edge Computing

The Internet of Things (IoT) technology provisions unprecedented opportunities to evolve the interconnection among human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated Mobile Edge Computing (MEC) with remote clouds is a promising platform to enable delay-sensitive service provisioning for IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices. Thus, computation-intensive and sensing data from IoT devices can be offloaded to the MEC network immediately for processing, and the service response latency can be significantly reduced. In this paper, we first formulate two novel optimization problems for delay-sensitive IoT applications, i.e., the total utility maximization problems under both static and dynamic offloading task request settings, with the aim to maximize the accumulative user satisfaction on the use of the services provided by the MEC, and show the NP-hardness of the defined problems. We then devise efficient approximation and online algorithms with provable performance guarantees for the problems in a special case where the bandwidth capacity constraint is negligible. We also develop efficient heuristic algorithms for the problems with the bandwidth capacity constraint. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising in reducing service delays and enhancing user satisfaction, and the proposed algorithms outperform their counterparts by at least 10.8 percent.

Joint optimization of collaborative interactive charging and charging lane placement for cyclic electric vehicles

Electric vehicle (EV) usually has limited battery capacity, which has to be charged frequently during service time. Although lots of work on deployment of charging lanes can support charging in-motion EVs, it cannot guarantee that every EV can be operational in its entire route. We are motivated by the following two observations. First, with the development on the circuit design of energy transmit antennas, it is possible to transfer energy between EVs bi-directionally and efficiently. Second, EVs usually have repetitive motions and may cyclically encounter with each other, so that in-motion EVs can have chance to charge each other. So we considered distribute energy among EVs in a collaborative and interactive manner. We proposed a joint optimization of collaborative interactive charging and charging lane placement to minimize the wireless energy loss and the cost of placing charging lanes. We then proved the joint optimization problem is NP-complete and proposed an efficient heuristic algorithm for it. Also extensive simulation results were conducted which showed our proposed algorithm can reduce the overall cost by 20.0% on average and 43.3% at most.

Minimizing Stack Memory for Partitioned Mixed-criticality Scheduling on Multiprocessor Platforms

A Mixed-Criticality System (MCS) features the integration of multiple subsystems that are subject to different levels of safety certification on a shared hardware platform. In cost-sensitive application domains such as automotive E/E systems, it is important to reduce application memory footprint, since such a reduction may enable the adoption of a cheaper microprocessor in the family. Preemption Threshold Scheduling (PTS) is a well-known technique for reducing system stack usage. We consider partitioned multiprocessor scheduling, with Preemption Threshold Adaptive Mixed-Criticality (PT-AMC) as the task scheduling algorithm on each processor and address the optimization problem of finding a feasible task-to-processor mapping with minimum total system stack usage on a resource-constrained multi-processor. We present the Extended Maximal Preemption Threshold Assignment Algorithm (EMPTAA), with dual purposes of improving the taskset’s schedulability if it is not already schedulable, and minimizing system stack usage of the schedulable taskset. We present efficient heuristic algorithms for finding sub-optimal yet high-quality solutions, including Maximum Utilization Difference based Partitioning (MUDP) and MUDP with Backtrack Mapping (MUDP-BM), as well as a Branch-and-Bound (BnB) algorithm for finding the optimal solution. Performance evaluation with synthetic task sets demonstrates the effectiveness and efficiency of the proposed algorithms.

Communication-Aware Secret Share Placement in Hierarchical Edge Computing

Secret sharing (SS) and secure multiparty computation (MPC) are now widely considered for privacy-preserving data processing. However, related applications can suffer from large transmission overhead. In this article, we propose a communication-aware secret share placement strategy to optimize communication overhead by minimizing transmission hop counts in a hierarchical edge computing architecture. Meanwhile, relevant privacy constraints in SS can still be guaranteed. We show that the constructed optimization problem is NP-hard, and efficient heuristic algorithms can be applied to find suboptimal solutions. With this consideration, we first evaluate two traditional heuristics, i.e., the genetic algorithm (GA) and particle swarm optimization (PSO). Besides, we introduce two basic heuristics, i.e., top-down and bottom-up heuristic, which can outperform GA and PSO in certain cases. Finally, we propose an algorithm, called bottom-up top-down (BUTD) heuristic, which can outperform all of the above four heuristics when communication among different shares of the same secret is comparable to that among different secrets. Comprehensive experimental results demonstrate the advantage of the proposed algorithms.

Frequency-Adaptive VDC Embedding to Minimize Energy Consumption of Data Centers

The increasing popularity of cloud computing would require Data Centers (DCs) to be scaled up rapidly, as needed, to provide adequate computing and storage infrastructure with rapidly growing demands. Since energy costs would be crucially important for these DCs, various approaches have been proposed to operate them efficiently. Virtual data centers (VDCs) are a promising approach for this as they can efficiently provide computing and storage resources to users over a shared physical infrastructure. In the context of VDC services, this paper focuses on improving the energy efficiency of DCs by applying a Dynamic Frequency Scaling (DFS) mechanism to provision VDC services. Specifically, the frequencies applied in each hardware are adaptively adjusted as per the given service requirements. This is done to minimize the overall energy consumption in the DC hardware when embedding a specific VDC. To the best of our knowledge, this is the first work that incorporates this DFS mechanism in the VDC provisioning problem. To minimize the overall energy consumption, we develop both an integer linear programming (ILP) optimization model and efficient heuristic algorithms. Extensive simulations are conducted to show that incorporating the DFS mechanism in VDC service provisioning significantly improves the energy efficiency of a DC when compared with a scheme where this mechanism is not applied. The proposed heuristic algorithms are also efficient and perform almost as well as the optimum ILP model.

Optimal Sizing and Efficient Routing of Electric Vehicles for a Vehicle-on-Demand System

Due to the steep rise in global population, urbanization, and industrialization, most of the cities in the world today are witnessing increased carbon footprints and reduced per capita space. In such a scenario, vehicle sharing and carpooling systems, specifically with electric vehicles (EV), can significantly help due to the reduced cost of ownership, maintenance, and parking space. In this article, we study the challenging problem of optimal sizing and efficient routing for an electric vehicle-on-demand system. Users demand EVs at the pooling stations at different time instances with individual deadlines to reach the destinations. The objective is to fulfill all the demands respecting the deadlines with minimum investment, which essentially translates to minimizing the total number of EVs. We define the problem formally using mixed-integer linear programming formulation and propose a set of intelligent and efficient heuristic algorithms to solve it efficiently. The proposed algorithms’ performances are tested and validated in a simulated environment on a reasonable size city network with many EV demands. The results obtained show that the proposed heuristic algorithms are competent by reducing 200–360 EVs per day on a network of 282 charging ports, indicating their scalability to be implemented in real-world scenarios.

Path Planning for Multi-Vehicle-Assisted Multi-UAVs in Mobile Crowdsensing

Due to the capability of fast deployment and controllable mobility, unmanned aerial vehicles (UAVs) play an important role in mobile crowdsensing (MCS). However, constrained by limited battery capacity, UAVs cannot serve a wide area. In response to this problem, the ground vehicle is introduced and used to transport, release, and recycle UAVs. However, existing works only consider a special scenario: one ground vehicle with multiple UAVs. In this paper, we consider a more general scenario: multiple ground vehicles with multiple UAVs. We formalize the multi-vehicle-assisted multi-UAV path planning problem, which is a joint route planning and task assignment problem (RPTSP). To solve RPTSP, an efficient multi-vehicle-assisted multi-UAV path planning algorithm (MVP) is proposed. In MVP, we first allocate the detecting points to proper parking spots and then propose an efficient heuristic allocation algorithm EHA to plan the paths of ground vehicles. Besides, a genetic algorithm and reinforcement learning are utilized to plan the paths of UAVs. MVP maximizes the profits of an MCS carrier with a response time constraint and minimizes the number of employed vehicles. Finally, performance evaluation demonstrates that MVP outperforms the baseline algorithm.