The prediction strategy is a key method for solving dynamic multi-objective optimization problems (DMOPs), particularly the commonly used linear prediction strategy, which has an advantage in solving problems with regular changes. However, using ...

• Using the linear prediction strategy only may result in the loss of population diversity.
• A dynamic particle swarm prediction strategy is used to increase the population diversity.
• An improved linear prediction strategy is used to ...

The application of Artificial Neural Networks (ANNs) has been extensively investigated for fluid dynamic problems. A specific form of ANNs are Physics-Informed Neural Networks (PINNs). They incorporate physical laws in the training and have ...

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• PINNs improved the ANN prediction accuracy for the potential flow cases in this work.
• Random distribution of training data lead to a higher prediction accuracy of ANNs.
• A Sequence-to-sequence method enabled temporal interpolation ...

Through user participation, mobile crowdsensing (MCS) services overcome the problem of the excessive costs of relying solely on the active deployment of sensors and of achieving large-scale and low-cost applications of the Internet of Things, ...

• This paper addresses the MCS problem based on a POI task model as a two-stage game problem. The subjects of the first and second stages are users and service providers, respectively, aiming to maximize their own interests.
• In the first ...

The convergence of transformative technologies, including the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI), has driven private edge cloud systems to the forefront of research efforts. The access to massive terminals and ...

• The resource allocation is elucidated as a MINLP problem
• The MPAA is used to address the resource allocation between MNO and MVNO.
• The TRAS is developed to address the spectrum allocation between MVNO and terminal.
• The SA is ...

Batched Sparse Linear Algebra has become an emergent processing mode on modern hardware accelerators based on Graphics Processing Units (GPUs) developed over the years to serve as the main compute devices in the largest computing clusters and ...

• We show performance accelerator portable solve interface for sparse matrix batches.
• We show tmBLAS for mixed-precision computing scenarios with floating-point formants.
• We use C and C++ programming languages to target a range of ...

The increasing complexity of Compute Continuum environments calls for efficient resource optimization techniques. In this paper, we propose and evaluate three distributed designs of a genetic algorithm (GA) for resource optimization, within an ...

• Three distributed genetic algorithm designs for the Compute Continuum.
• Resource optimization execution distributed across the infrastructure’s devices.
• Lightweight implementation using the MQTT protocol.
• Experiments based on a ...

Quantum computing has emerged in recent years as an alternative to classical computing, which could improve the latter in solving some types of problems. One of the quantum programming models, Adiabatic Quantum Computing, has been successfully ...

• Solving the scheduling of unrelated parallel machines problem using quantum annealer.
• The scheduling considers the priority of kinds of tasks.
• The scheduling includes delays due to the switches between kinds of tasks on machines.

Mobile edge computing (MEC) is expected to support the computation-intensive and delay-sensitive applications of mobile internet users. In this paper, we investigate the resource allocation of MEC with the effect of I/O interference among ...

• Energy minimization for MEC with the effect of I/O interference among VMs.
• A flexible task scheduling approach combining parallel and sequential computing.
• Task scheduling problem is formulated as a mixed-integer nonlinear ...

Cloud providers handle substantial number of requests to create and delete virtual machines (VMs) on a daily basis, where the unknown sequence of requests eventually leads to resource fragmentation. To mitigate this issue, periodic consolidation ...

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• We introduce the BalCon algorithm for solving the migration-aware consolidation
• The balance factor is proposed to measure free space distribution in a datacenter
• Force Fit is a key part of BalCon that allows for solving imbalanced ...

With the advent of the Internet of things (IoT) era, federated learning plays an important role in breaking through traditional data barriers and effectively realizing data privacy and security in the process of sharing. However, the demand of ...

Multi-task optimization utilizes knowledge transfer to optimize multiple tasks simultaneously. When the number of tasks is increased to many-task optimization, the computational burden of the algorithm increases and the positive knowledge ...

• A new many-objective many-task optimization algorithm is proposed in this paper.
• This algorithm proposes a new many-objective many-task framework.
• This algorithm uses a knowledge transfer method based on many-task.
• The superior ...

With the tremendous developments of Internet of Things (IoT), IoT devices and applications are imposing progressively higher requirements on computing, communication, and power services. Edge computing in combination with wireless ...

• We propose to leverage the IRS technology to assist WPT and data transmission.
• ...

To solve the problem of high time consumption and energy loss of edge computing perceptual task offloading in the mobile IoT (Internet of things), it is necessary to increase the particle population diversity and the performance of ...

• New Offloading Method of IoT User Perceptual Task Based on QBPPO was proposed.
• ...

To support distributed upper-level services such as those in edge or fog computing, a hierarchical and distributed multicloud architecture is introduced to replace the traditional centralized cloud architecture. The intra-time slot demand ...

• The demand fluctuation is a new challenge for hierarchical edge clouds.
• It is the first work about inner-time slot stochasticity with 2D resource sharing.
• The general problem is formulated for hierarchical and distributed edge ...

In the environment of peer competition and energy conservation, optimizing the deployment of application server clusters in real time according to actual workload conditions to reduce operating costs and energy consumption is an ...

• We prove the feasibility of defining programming variables for each server type.

This study proposes a new metaheuristic algorithm, called “Geometric Octal Zones Distance Estimation” (GOZDE) algorithm to solve global optimization problems. The presented GOZDE employs a search scheme with the information sharing ...

• A novel metaheuristic algorithm is proposed.
• Eight different search strategies ...

The paper presents multi-objective optimization technique for the exploration of unknown space. Exploration refers to the building of an estimated finite map of the environment using sensor data. Conventionally, in robotics, the optimization is ...

• Developed a multi-objective optimization technique.
• Tested the proposed method against exploration of unknown space problems.
• Compared the proposal to other similar optimization algorithms.
• Demonstrated effectiveness and ...

Cloud computing and Network Function Virtualization (NFV) are two complementary technologies. Virtual network functions (VNFs) provided by NFV are connected in the form of service function chains (SFCs) and typically hosted on the ...

• Meta-heuristic algorithms are proposed to help in adapting/allocating resources for both linear and non-linear SFC requests.

Fog computing emerged to ease the load of resource-constrained devices on the Internet. It provides services and computational devices closer to the users to reduce latency and improve quality of service. For this paradigm to be used ...

• This work advances the State-of-the-Art on resource allocation in a fog environment.