Recently, many studies have used evolutionary algorithms (EAs) to optimize complex problems across various fields, including mechanical structure design, robotics, and cloud computing. EAs simulate the process of evolution to improve solutions to a given problem iteratively. However, EAs encounter significant challenges when dealing with high-dimensional expensive problems (HEPs). The large solution space and high computing cost of fitness evaluations (FEs) make optimization with limited FEs particularly difficult. To tackle this problem, an Adaptive Multi-stage Evolution Algorithm named AMEA is proposed. In AMEA, an adaptively enhanced teaching-learning-based optimization algorithm is adopted to explore the search space and find potential areas quickly. Then, in the next stage, the Gaussian process surrogate model and a genetic learning particle swarm optimization algorithm are adopted for further exploitation. Besides, this work proposes an adaptive stage switching criterion and an individual screening mechanism to enhance the optimization ability. AMEA demonstrates strong optimization performance when applied to HEPs. We compare AMEA with several state-of-the-art HEP optimization algorithms through seven benchmark functions, and the results show that it performs competitively with other algorithms. Finally, we also validate AMEA's effectiveness with a real-world computation offloading problem.

In emerging applications of the Internet of Things, wireless sensor networks (WSNs) are often utilized to gather, track, and monitor data in remote areas with limited communication infrastructure. Since the majority of WSNs employ sensors powered by batteries, maintaining energy efficiency and conservation is crucial for ensuring their sustained operations over time. This work designs an Energy-efficient Unmanned aerial vehicle (UAV)-assisted hierarchical architecture of WSNs (EUW). EUW supports fast transmission of data collected from WSNs to a cloud server. Based on this architecture, this work first formulates a joint optimization problem for cluster head selection, time slot allocation, and UAV path planning to minimize the weighted sum of energy consumption of WSNs and that of a UAV. Then, a hybrid meta-heuristic algorithm named knowledge transfer-based particle swarm optimization (KTPSO) is designed, which utilizes previous optimization results to increase the convergence speed and find better results. Finally, numerical analysis and evaluation results are shown to demonstrate the efficiency of KTPSO and the proposed UAV-assisted architecture of hierarchical WSNs.

Current Industrial Internet supports the sharing of information on heterogeneous resources and elements in a process of industrial production. It enables intelligent production processes and supports cost-effective scheduling. However, collaborative manufacturing and scheduling planning for enterprises with multiple plants cause several major challenges because of a large number of decision variables and constraints of manufacturing abilities of plants, resources of production, etc. Existing methods cannot comprehensively optimize the cost of multiple products in different plants, and fail to consider machine-level optimization of tasks of manufacturing. We propose a comprehensive machine-level architecture for enterprises with multiple plants. Based on this architecture, we formulate a limited non-linear integer optimization problem to decrease the total cost of transportation, production, and sales. In it, several real-life complicated nonlinear constraints are jointly considered, and they include constraints of storage space, replacement times, pairing production, substitution, and order fulfillment rates. To solve this optimization problem, we design a hybrid meta-heuristic optimization algorithm named Genetic Simulated annealing-based Particle Swarm Optimizer with Auto-Encoders (GSPAE). Extensive experiments with real-life data show that GSPAE decreases the total cost by 25% than other state-of-the-art methods.

Because a non-distributed system has to be partitioned into different parts for distributed deployment, delays in the data exchanged between computer nodes are incurred and state errors are produced. As a result, the transformed distributed system may undergo performance deterioration or even become unstable. The errors are mainly affected by the distributed advancing step. In this paper, a novel approach is introduced to calculate the maximum advancing step. Experimental results verify the effectiveness of our approach.

Abstract We propose an octree‐based presentation of vortex particles to simulate smoke and gaseous phenomena in a physical way. Vortex particle method prevails over grid‐based method in terms of less numerical dissipation and more detail features, but it suffers from heavy computational overhead due to per‐particle Biot–Savart integration over the entire simulation space. To alleviate this problem, we employ an octree background grid to separate the vortex particles into individual groups. Particles in groups are aggregated as a single super vortex particle to reduce computational cost. The proposed method produces comparable visual result as previous methods with much less computational overhead. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract We propose an adaptive Catmull–Rom interpolation to improve accuracy of semi‐Lagrangian advection for smoke simulation. Original Catmull–Rom improves numerical accuracy but overshoot violates global stability. Monotonic Catmull–Rom is unconditionally stable, whereas it sweeps out detail features due to overly suppression operations. Our method modifies original Catmull–Rom to obtain second‐order accuracy and unconditional stability. It flattens locations where interpolations might break through global bounds but maintains local overshoots to conserve diversity of fluid flow. The scheme is easy to collaborate with existing fluid simulators to improve small features. Copyright © 2013 John Wiley & Sons, Ltd.

Path planning is a significant problem in mission planning. For Miniature Air Vehicles (MAVs) exposed to strong winds, their flight paths are dynamically influenced by wind. Moreover, the small payload of MAVs severely restricts on-board computational resources. To alleviate the computational burden, a multi-resolution path planning approach for MAVs is presented, which is considering the knowledge of obstacles and wind. The method is based on multi-resolution cell decomposition of the environment, and two key factors, which are distribution of wind and the distance between MAV and obstacles, affect the resolution. High resolution is constructed in the region close to the MAV and of wind varied severely. This multi-resolution representation of the environment increases numerical efficiency and robustness. Simulations are provided under two scenarios of flight environment, without wind and with the wind incorporated. The results show the proposed solution is effective in solving path planning problems for MAVs with wind effect and obstacles avoidance.

In computer graphics, 3D model is often represented by triangle meshes. A complicated scene sometimes consists of considerable meshes, which is very difficult to store, transmit and render. This paper introduces an improved mesh simplification algorithm based on edge-collapse. This algorithm utilizes quadric error metric to compute the vertex significance and control the sequence of edge collapse. The experimental results show that the new algorithm can reserve original shape features at low levels of detail.

In this paper, the key techniques of multi-projector display wall are discussed. First we present an approach for geometric registration based on bicubic Bezier curved surface; Second, Focus on optimizing edge blending and achieving perfect visual effect, we propose a new edge blending paradigm, which based on spline interpolation. Comparing to the previous paradigm, our new paradigm is much more universal and flexible. To the best of our knowledge, this is the first method using spline interpolation for edge blending which can achieve perfect visual seamless.

High performance and economic gains are treated as the main target for future aerospace product design. An inward turning inlet equipped hypersonic vehicle parameterization procedure was devised for collaboration of wingbody and scramjet propulsion system modeling. Top design parameters (free-stream Mach number, flight altitude, mission requests, etc.) drive the overall collaborative system construction and both the scramjet engine geometry and wingbody profiles are conveniently generated using the NURBS toolbox and streamline tracing method within the programs developed in this paper. An accelerated digital real time hypersonic vehicle-propulsion integrated controlled plant was prepared for advanced controlling algorithm tests and further researches.

Combining virtual reality (VR) technology with Automated Highway System (AHS) simulation could not only show visually comprehensible results but also reduce the cost when it is no longer necessary to test with real vehicles. This paper presents an intelligent vehicles virtual reality (IVVR) platform, which is an interdisciplinary software application of VR & AHS. It aims at the control algorithm design and verification for intelligent vehicles. Firstly, the design of IVVR platform is implemented by several UML functional diagrams; then, software technology as well as its realization methods are presented; finally, some intelligent vehicles behaviors simulations like maintenance of a secure inter-vehicular distance or lane-change are experimented in IVVR platform and real-time animation could be obtained with good performance besides the figures and data.

This paper is devoted to efficient algorithms for real-time rendering of seashore using programmable Graphics Processing Unit (GPU). The scene of seashore is a usual component of virtual environment in simulators or games and should be realistic and real-time. We realized the realtime seashore simulation in three steps: first the ocean wave generation, using a simple but high-efficiency model which can describe both shallow and deep ocean; second the optical effects imitation and third the interaction of ocean waves with the coast, including the coastline simulation designed mathematically and the breaking waves simulation designed by 3D Bézier curved surface via metamorphosing and key-frame animation. Scenes under different atmospheric conditions are also presented in this paper.