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, i...
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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 bilayer 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-epsilon C 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-epsilon C 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.
The reactive multi-objectivemulti-skilled project scheduling under resource disruptions is more complex than the general scheduling problem. This complexity stems from the incorporation of overtime and proactive pree...
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The reactive multi-objectivemulti-skilled project scheduling under resource disruptions is more complex than the general scheduling problem. This complexity stems from the incorporation of overtime and proactive preemption as response strategies, as well as the consideration of the impact of a resource's skill level on setup time. We propose a reactive multi-objective scheduling model to address this problem. The objective is to generate a new schedule that minimizes deviations in activity start times and resource allocations from the baseline schedule, while also minimizing resource usage costs. To enhance the efficiency of commercial solvers in solving this model, some of the formulas are linearized. Furthermore, we propose a hybrid multi-objective evolutionary algorithm (HMOEA). To boost the performance of the HMOEA, several improvement strategies are introduced, including an adaptive procedure for fitness assignment and density estimation, a hybrid evolutionary strategy, and a local search strategy. Numerical experiments demonstrate the effectiveness of both the linearization measures and the improvement strategies. Five performance metrics and convergence comparisons are employed to assess the solution quality of the proposed algorithm in terms of convergence, diversity, and distribution. Computational results demonstrate that HMOEA leads to significant improvements in all metrics compared to five state-of-the-art algorithms.
Due to advancements in hardware capabilities and computation power, multi-objective optimization has recently been used in many industrial problems. These problems usually have multiple objectives of conflicting natur...
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ISBN:
(纸本)9781450392372
Due to advancements in hardware capabilities and computation power, multi-objective optimization has recently been used in many industrial problems. These problems usually have multiple objectives of conflicting nature. To solve such problems, multi-objective evolutionary algorithms (MOEAs) utilize Non-dominated Sorting (NDS) algorithms to rank the viability of the solutions efficiently. Researchers have focused on improving the time complexity of NDS algorithms for a long time due to their wide range of applications. With the increase in the use of GPUs for general-purpose scientific computing, it is now becoming possible to reduce the computation cost of such algorithms. In this paper, we have analyzed one such NDS algorithm, Corner Sort, and highlighted two areas within it with a high scope of parallelism. We propose a highly efficient, parallelized version of Corner Sort, implemented using CUDA framework. Utilizing the thousands of cores in a GPU, our algorithm is able to break the solution set into smaller chunks and simultaneously process them. Furthermore, the comparison between two solutions across all the objectives is done parallelly as well. On benchmark datasets, our algorithm performs up to 10x faster than the serial algorithm, and its performance improves for larger datasets, irrespective of the number of objectives.
PSE (Parameter Sweep Experiments) applications represent a relevant class of computational applications in science, engineering and industry. These applications involve many computational tasks that are both resource-...
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ISBN:
(数字)9783030898175
ISBN:
(纸本)9783030898175;9783030898168
PSE (Parameter Sweep Experiments) applications represent a relevant class of computational applications in science, engineering and industry. These applications involve many computational tasks that are both resource-intensive and independent. For this reason, these applications are suited for Cloud environments. In this sense, Cloud autoscaling approaches are aimed to manage the execution of different kinds of applications on Cloud environments. One of the most recent approaches proposed for autoscaling PSE applications is MIA, which is based on the multi-objective evolutionary algorithm NSGA-III. We propose to endow MIA with other multi-objective optimization algorithms, to improve its performance. In this respect, we consider two well-known multi-objective optimization algorithms named SMS-EMOA and SMPSO, which have significant mechanic differences with NSGA-III. We evaluate MIA endowed with each of these algorithms, on three real-world PSE applications, considering resources available in Amazon EC2. The experimental results show that MIA endowed with each of these algorithms significantly outperforms MIA based on NSGA-III.
Optimizing spatial pattern of best management practices (BMPs) is crucial for managing non-point source pollution at the watershed scale. However, uncertainties of BMPs effects caused by variant hydro-meteorological c...
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Optimizing spatial pattern of best management practices (BMPs) is crucial for managing non-point source pollution at the watershed scale. However, uncertainties of BMPs effects caused by variant hydro-meteorological conditions can pose challenges to achieving water quality management goals, emphasizing the need to incorporate these uncertainties into decision-making. This study established a credibility chance-constrained programming (CCP) framework that incorporates multiple techniques, including uncertainty modelling, stochastic simulation-optimization technique, clustering and fuzzy set theory. Correlated uncertainties of BMPs effects were described by vine copulas and propagated to the watershed outlet via a Markov-based surrogate model. Fast non-dominated sorting genetic algorithm (NSGA-II), was adopted to optimize cost and pollutant reduction goals while quantifying the reliability of each solution. The developed framework can provide the best compromising solutions (BCSs) among numerous solutions generated by NSGA-II. A case study was conducted to manage Total Nitrogen (TN) and Total Phosphorus (TP) pollution in the upper Boyang River watershed, China. The results show that the system cost increased by up to 3.4 times with the increase of reduction goal (30-60%). Specially, higher credibility levels allow for slight increases in pollution loads (1.48%-5.67%) without significantly raising costs. Overall, by incorporating uncertainty into pollutant reduction scenarios, the framework enables decision-makers to balance costs and environmental benefits while ensuring robust and reliable decisions. This approach is highly adaptable to BMP planning in complex environmental systems, enhancing its practicality for multi-objective watershed management.
This paper addresses the problem of optimizing a Demand Responsive Transport (DRT) service. A DRT is a flexible transportation service that provides on-demand transport for users who formulate requests specifying desi...
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ISBN:
(纸本)9781450367486
This paper addresses the problem of optimizing a Demand Responsive Transport (DRT) service. A DRT is a flexible transportation service that provides on-demand transport for users who formulate requests specifying desired locations and times of pick-up and delivery. The vehicle routing and scheduling procedures are performed based on a set of requests. This problem is modeled as a multi-objective Dial-a-Ride problem (DARP), in which a set of objectives related to costs and user inconvenience is optimized while respecting a set of constraints imposed by the passengers and vehicles, as time windows and capacity. The resulting model is solved by means of three multi-objective evolutionary algorithms (MOEA) associated with feasibility-preserving operators. Computational experiments were performed on benchmark instances and the results were analyzed by means of performance quality indicators widely used for multi-objectivealgorithms comparison. The proposed approaches demonstrate efficient and higher performance when optimizing this DRT service compared to another algorithm from the literature.
Despite the studies on human resource allocation, a problem of dissatisfied employees arises in developing and under-developed countries while decentralizing human resources nationwide since rural areas have fewer fac...
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ISBN:
(纸本)9783031220388;9783031220395
Despite the studies on human resource allocation, a problem of dissatisfied employees arises in developing and under-developed countries while decentralizing human resources nationwide since rural areas have fewer facilities than urban areas. Randomly allocating employees contributes to employees' dissatisfaction if they are displeased with where they are assigned, leading to an unstable work environment. However, allocating employees solely based on their satisfaction may lead to a centralized solution around the urban cities. Therefore, employee satisfaction and dispersion are the two most essential but opposing factors for employee decentralization in developing countries. In this study, we have addressed the problem of employee decentralization by proposing a multi-objective Optimization approach that maximizes the two conflicting objectives: employee satisfaction (ES) and employee dispersion (ED). A neural network is applied that predicts the ES of an employee allocated in an area/city. Moreover, we have formulated a dispersion function that provides a score based on how well dispersed a specific allocation is. Using a multi-objective evolutionary algorithm, we have developed an allocation framework that maximizes these conflicting objectives and finds optimal allocations.
In addressing the complexities of modern logistics, this study introduces a novel multi-objective formulation for vehicle routing problems with time windows (MO-VRPTW), targeting minimizing travel distance, enhancing ...
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In addressing the complexities of modern logistics, this study introduces a novel multi-objective formulation for vehicle routing problems with time windows (MO-VRPTW), targeting minimizing travel distance, enhancing customer satisfaction, and equalizing driver workloads. We introduce an innovative hybrid multi-objective evolutionary algorithm (MOEA) leveraging nondominated sorting simplified swarm optimization to effectively merge the advantages of various optimization strategies. A key aspect of this advancement is the incorporation of the Lin- KernighanHelsgaun (LKH) heuristic, which delivers a superior initial solution, thereby markedly enhancing the speed of convergence. Additionally, we pioneered a local search method inspired by the A* algorithm designed to refine the search process's exploration and exploitation stages. Solomon's benchmark instances, a recognized standard in the VRPTW field, were used to validate our algorithm's effectiveness. Our algorithm demonstrated superior performance in addressing MO-VRPTW through meticulous statistical analysis, outperforming state-of-the-art algorithms, such as MOPSO, NSGA-II, MOEA/D, and SPEA2, regarding efficiency and solution diversity. This study not only advances algorithmic performance but also thoughtfully considers the interests of key supply chain stakeholders.
Short-term power load forecasting plays an important role in ensuring the stable operation of power systems and improving economic benefits. However, most of the previous studies ignored the limitations of a single pr...
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Short-term power load forecasting plays an important role in ensuring the stable operation of power systems and improving economic benefits. However, most of the previous studies ignored the limitations of a single pre-diction model and the useful information in the error factors, resulting in low prediction accuracy. Therefore, this paper proposes a multi-stage integrated model based on decomposition, error factors, and a multi-objective evolutionary algorithm based on decomposition (MOEA/D). The proposed model consists of three stages: in the first stage, the gated recurrent unit (GRU) is used to predict the components of complete ensemble empirical modal decomposition with adaptive noise, and new data sets are obtained by combining them with the original data sets to fully mine the data characteristics. In the second stage, the MOEA/D based on angle and distance selection strategy and adaptive population generation strategy is used to optimize GRU network parameters with accuracy and diversity as the objective functions, obtaining several load forecasting models and error forecasting models that consider accuracy and diversity. In the third stage, a new nonlinear integration method based on GRU optimized by MOEA/D is used to integrate load forecasting values and error forecasting values, considering error factors to further improve forecasting accuracy. Experimental results on the Australian wholesale electricity market and energy market datasets show that the proposed model outperforms the comparative model in terms of accuracy and generalization and can be widely applied in load forecasting.
Network pruning aims to enhance the performance of deep neural networks by eliminating redundant components from the model. However, existing pruning methods typically require a well -trained model and employ fixed, s...
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Network pruning aims to enhance the performance of deep neural networks by eliminating redundant components from the model. However, existing pruning methods typically require a well -trained model and employ fixed, single pruning criteria throughout the pruning cycles. To address these limitations, we propose a novel method called evolutionary Filter Criteria (EvoFC). This method enables the automated search for the network pruning ratio and criterion during a population -based heuristic search process. We introduce a unique encoding space that represents the chosen pruning criterion and ratio for each layer, facilitating the acquisition of optimal architecture configurations for candidate networks during iterations. Additionally, we devise a novel weight inheritance mechanism to mitigate the computational burden associated with the population -based nature of the method, resulting in a significant reduction in overall training time. We validate our method by applying it to randomly initialized networks and conducting empirical experiments on CIFAR-10/100, ILSVRC2012 and Places365 datasets. The results demonstrate that our method effectively reduces the number of FLOPs while striking a fine balance between accuracy and computational efficiency. This underscores the practical value of our method in optimizing performance while efficiently utilizing computational resources, particularly when pruning networks starting from random initialization.
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