High-dimensional expensive problems are often encountered in the design and optimization of complex robotic and automated systems and distributed computing systems, and they suffer from a time-consuming fitness evaluation process. It is extremely challenging and difficult to produce promising solutions in a high-dimensional search space. This work proposes an evolutionary optimization framework with embedded autoencoders that effectively solve optimization problems with high-dimensional search space. Autoencoders provide strong dimension reduction and feature extraction abilities that compress a high-dimensional space to an informative low-dimensional one. Search operations are performed in a low-dimensional space, thereby guiding whole population to converge to the optimal solution more efficiently. Multiple subpopulations coevolve iteratively in a distributed manner. One subpopulation is embedded by an autoencoder, and the other one is guided by a newly proposed Multi-swarm Gray-wolf-optimizer based on Genetic-learning (MGG). Thus, the proposed multi-swarm framework is named Autoencoder-based MGG (AMGG). AMGG consists of three proposed strategies that balance exploration and exploitation abilities, i.e., a dynamic subgroup number strategy for reducing the number of subpopulations, a subpopulation reorganization strategy for sharing useful information about each subpopulation, and a purposeful detection strategy for escaping from local optima and improving exploration ability. AMGG is compared with several widely used algorithms by solving benchmark problems and a real-life optimization one. The results well verify that AMGG outperforms its peers in terms of search accuracy and convergence efficiency.
Swarm intelligence in a bat algorithm (BA) provides social learning. Genetic operations for reproducing individuals in a genetic algorithm (GA) offer global search ability in solving complex optimization problems. Their integration provides an opportunity for improved search performance. However, existing studies adopt only one genetic operation of GA, or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only. Differing from them, this work proposes an improved self-adaptive bat algorithm with genetic operations (SBAGO) where GA and BA are combined in a highly integrated way. Specifically, SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality. Guided by these exemplars, SBAGO improves both BA's efficiency and global search capability. We evaluate this approach by using 29 widely-adopted problems from four test suites. SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems. Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness, search accuracy, local optima avoidance, and robustness.
Autonomous driving poses high demands on computing and communication resources. Vehicular edge computing is presented to offload real-time computing tasks from connected and automated vehicles (CAVs) to high-performance edge servers. However, it brings additional communication overhead due to limited bandwidth, and increases delay of tasks. To solve it, this work first proposes an offloading architecture including multiple CAVs, roadside units and cloud. We minimize the total cost of a hybrid system by jointly considering task offloading ratios, and allocation of communication and computing resources. Furthermore, a mixed integer non-linear program is formulated and solved by a novel meta-heuristic algorithm called Self-adaptive Gray Wolf Optimizer with Genetic Operations (SGWOGO). SGWOGO achieves joint optimization of computation offloading among CAVs, roadside units and cloud, and allocation of their resources. Finally, real-life data-driven simulation results demonstrate that SGWOGO achieves lower cost in fewer iterations compared with its several state-of-the-art peers.
Forests are among the most diverse ecosystems on the planet, and their biomass serves as a key measure for assessing the biological productivity and carbon cycle of terrestrial forest ecosystems. Recognizing the factors that impact forest ecosystems is essential for assessing their health and developing effective conservation strategies to preserve species diversity and ecological equilibrium. This study considered forest biomass as the explained variable, economic density as the explanatory variable, and human activities, land use, and forestland protection as the control variables. Panel data encompassing 448 counties within the Yellow River Basin (YRB) for the years 2008, 2013, and 2018 were utilized as inputs for ArcGIS spatial analysis and two-way fixed-effects modeling. This approach aimed to evaluate the impact of socio-economic factors on forest biomass. The findings indicate that, (1) from both temporal and spatial viewpoints, the distribution of forest biomass in the upper reaches of the Yellow River demonstrated an improvement over the period from 2008 to 2018. Notably, in 2013, there was a significant reduction in the forest biomass distribution in the middle and lower sections, although the levels remained substantially above the average for those regions. Throughout the period from 2008 to 2018, the overall forest biomass within the YRB displayed a spatial distribution pattern, with elevated levels observed in the western areas and diminished levels in the eastern regions. (2) A one-unit increase in economic density led to a 1.002% increase in forest biomass. In the YRB, a positive correlation was observed between the economic density and forest biomass, especially in the middle and lower reaches of the river. (3) In the upstream region, forest biomass was strongly negatively correlated with cultivated land but significantly positively correlated with forest land protection. In the middle reaches, although population growth and arable land expansion led to a decrease in forest biomass, primary industry development and urbanization promoted forest biomass growth. The development of primary industries other than planting, such as the forestry industry, can contribute to the forest biomass. Moreover, in the downstream area, a strong negative correlation was observed between the number of permanent residents and forest biomass. We recommend modifications to human activities to enhance the forest biomass and the preserve forest ecosystem stability.
Mobile edge computing (MEC), as a promising paradigm, delivers computation and storage capacities at the edge of the network. It supports delay-sensitive services for mobile users (MUs). However, dynamic and stochastic characteristics of MEC networks necessitate constant migration of installed services across edge servers to keep up with the mobility of MUs. As a result, the cost of maintaining the network increases significantly. Existing studies of MEC rarely consider the cost of service migration due to MU mobility. To minimize the long-term cost for microservices in a hybrid cloudedge system comprising of MUs, small base stations (SBSs), and a cloud data center (CDC), the total cost minimization is formulated as a constrained mixed-integer nonlinear program. To solve it, this work designs a novel meta-heuristic optimization algorithm called Multi-swarm Grey-wolf-optimizer based on Genetic-learning (MGG), which effectively combines strong local search capabilities of grey wolf optimizer with superior global search capabilities of genetic algorithm. MGG simultaneously optimizes service request routing among MUs, SBSs, and CDC, CPU speeds of SBSs, service deployment of SBSs, service migration cost of SBSs, as well as MUs' transmission power and channel bandwidth allocation. Simulation results with Google cluster trace demonstrate that MGG outperforms several state-of-the-art peers with respect to the overall cost of the hybrid system.
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