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14th International Conferences on Computational Logistics (ICCL)

作者： Azhar, Nurul Asyikeen Binte Gunawan, Aldy Cheng, Shih-Fen Leonardi, Erwin Singapore Management Univ Sch Comp & Informat Syst 80 Stamford Rd Singapore 178902 Singapore Rio Tinto Ltd Tower 312 Marina BlvdMarina Bay Financial Ctr Singapore 018982 Singapore

ISBN: (纸本)9783031436116;9783031436123

The logistical complication of long-term mine planning involves deciding the sequential extraction of materials from the mine pit and their subsequent processing steps based on geological, geometrical, and resource constraints. The net present value (NPV) of profit over the mine's lifespan usually forms the sole objective for this problem, which is considered as the NP-hard precedence-constrained production scheduling problem (PCPSP) as well. However, increased pressure for more sustainable and carbon-aware industries also calls for environmental indicators to be considered. In this paper, we enhance the generic PCPSP formulation into a multi-objective optimization (MOO) problem whereby carbon cost forms an additional objective. We apply the Non-Dominated Sorting Genetic algorithm II (NSGA-II) to this formulation and experiment with variants to the solution generation. Our tailored application of the NSGA-II using a set of real-world inspired datasets can form an approximated Pareto front for planners to observe stipulated annual carbon emission targets. It also displays that tailored variants of the NSGA-II can produce diverse solutions that are close to the true Pareto front.

关键词： Precedence-constraint production scheduling Resource capacity optimization multi-objective evolutionary algorithm Sustainability

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multi-objective evolutionary algorithm Determined Radar Phase Codes

Multi-Objective Evolutionary Algorithm Determined Radar Phas...

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IEEE Radar Conference (RADAR)

作者： Stringer, Jeremy Lamont, Gary Akers, Geoffrey USAF Dept Elect & Comp Engn Inst Technol Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781467306584

Solving complex real world multi-objective Optimization problems is the forte of multi-objective evolutionary algorithms (MOEA). Such algorithms have been part of many scientific and engineering endeavors. This study applies the NSGA-II and SPEA2 MOEAs to the radar phase coded waveform design problem. The MOEAs are used to generate a series of radar waveform phase codes that have excellent range resolution and Doppler resolution capabilities. The study compares the ability of NSGA-II and SPEA2 to continually evolve (phase code) solutions on the Pareto front for the problem while maintaining a diversity of solutions (phase codes). Results demonstrate that for the radar phase code problem NSGA-II provides a more diverse population of acceptable solutions and therefore a greater number of different viable phase codes when compared to the solutions provided by SPEA2

关键词： multi-objective evolutionary algorithm radar phase coding Distance resolution design problems waveforms

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A new stopping criterion for multi-objective evolutionary algorithms: application in the calibration of a hydrologic model

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COMPUTATIONAL GEOSCIENCES 2019年第6期23卷 1219-1235页

作者： Ticona Gutierrez, Juan Carlos Adamatti, Daniela Santini Bravo, Juan Martin Univ Fed Rio Grande do Sul Inst Pesquisas Hidraul Porto Alegre RS Brazil

multi-objective genetic algorithms have been successfully applied in a wide variety of problems. Although widely used, there are few theoretical guidelines for determining when to stop the search. Many users commonly use rules like stopping when there is no significant improvement during the last generations or when a certain number of generations are reached. In this paper, we propose a new stopping criterion approach and evaluate its performance with three widely used evolutionary algorithms in the calibration of a hydrologic model. The stopping criterion is based on the minimum number of generations required to achieve a determined number of non-dominated solutions in Pareto Front. The new stopping criterion was tested in the lumped hydrologic model IPH-II calibration, using the genetic algorithms NSGA-II, NSGA-III, and SPEA-II and two objective functions. The generational distance, spacing, and maximum spread metrics were used to assess the performance of the proposed stopping criterion in comparison to the standard criterion. Results show no significant loss in goodness of fit associated with the proposed stopping criterion, both in calibration and validation periods. Performance metrics have shown similar values when the standard and the proposed stopping criteria were compared. However, the average computational time to complete the optimization process was reduced up to 38.2% when the proposed stopping criterion was used. Thus, it can be concluded that the new stopping criterion reduces the iteration workload without compromising the accuracy of solution sets.

关键词： multi-objective evolutionary algorithm Lumped hydrologic model Stopping criterion NSGA-II NSGA-III SPEA-II

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Pre-Trained Hybrid-Damping Message Passing Detector for multiple MIMO Scenarios 23

Pre-Trained Hybrid-Damping Message Passing Detector for Mult...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Ge, Yingmeng Xiong, Baiping Wang, Haibo Zhang, Zaichen Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratory Nanjing211111 China

ISBN: (纸本)9798350325959

Among various multiple-input multiple-output (MIMO) detection algorithms, message passing algorithms (MPAs) have been widely considered to balance performance and complexity. Damping factors can greatly affect MPAs' convergence behavior and stability. Deep neural network (DNN) based detectors with learnable damping factors have shown improved convergence performance in MIMO systems but require retraining in different system configurations. In this paper, a pre-trained hybrid-damping (PHD) message passing detection (MPD) for multi-scenarios using multi-objective evolutionary algorithm (MOEA) is proposed, which only requires one pre-training step and can be adapted to multiple scenarios. Numerical results indicate that the proposed PHD scheme can provide better convergence and flexibility than unified damping (UD) and DNN-based ones. Furthermore, the proposed PHD scheme can also be applied to other MPAs. © 2023 IEEE.

关键词： damping factor message passing MIMO detector multi-objective evolutionary algorithm

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

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2022年第4期26卷 570-580页

作者： Zhang, Hao Dou, Lihua Xin, Bin Zhang, Ruowei Wang, Qing Beijing Inst Technol Sch Automat 5 Zhongguancun South St Beijing 100081 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst 5 Zhongguancun South St Beijing 100081 Peoples R China

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.

关键词： multi-objective evolutionary algorithm heterogeneous UAVs task planning task allocation path planning

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Parallelization of Corner Sort with CUDA for Many-objective Optimization

Parallelization of Corner Sort with CUDA for Many-Objective ...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Bharti, Vandana Singhal, Aryan Saxena, Anant Biswas, Bhaskar Shukla, Kaushal Kumar Indian Inst Technol BHU Varanasi Uttar Pradesh India

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.

关键词： Pareto Dominance Non-dominated Sorting multi-objective evolutionary algorithm Parallelism CUDA

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Endowing the MIA Cloud Autoscaler with Adaptive evolutionary and Particle Swarm multi-objective Optimization algorithms 1

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20th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Yannibelli, Virginia Pacini, Elina Monge, David Mateos, Cristian Rodriguez, Guillermo ISISTAN UNICEN CONICET Tandil Buenos Aires Argentina UNCUYO ITIC Mendoza Argentina Fac Ingn UNCuyo Mendoza Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina

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.

关键词： Parameter Sweep Experiments Cloud autoscaling multi-objective evolutionary algorithm multi-objective particle swarm optimization CloudSim Amazon EC2

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Optimization of a Demand Responsive Transport Service Using multi-objective evolutionary algorithms 19

Optimization of a Demand Responsive Transport Service Using ...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Viana, Renan J. S. Santos, Andre G. Martins, Flavio V. C. Wanner, Elizabeth F. CEFET MG Programa Posgrad Modelagem Matemat & Computac Belo Horizonte MG Brazil Univ Fed Vicosa Vicosa MG Brazil Ctr Fed Educ Tecnol Minas Gerais Belo Horizonte MG Brazil Aston Univ Birmingham W Midlands England

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-objective algorithms comparison. The proposed approaches demonstrate efficient and higher performance when optimizing this DRT service compared to another algorithm from the literature.

关键词： Demand Responsive Transport Dial-a-Ride Problem multi-objective evolutionary algorithm

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Decentralizing and Optimizing Nation-Wide Employee Allocation While Simultaneously Maximizing Employee Satisfaction 5th

Decentralizing and Optimizing Nation-Wide Employee Allocatio...

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4th International Conference on Optimization and Learning (OLA)

作者： Tabassum, B. Aniqua Ahmed, Ashna Nawar Alam, Subhe Tasnia Tabassum, Noushin Mahbub, Md Shahriar Seattle Univ Seattle WA 98122 USA Ahsanullah Univ Sci & Technol Dhaka Bangladesh

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.

关键词： Employee dispersion Employee satisfaction Human resource allocation in developing countries multi-objective evolutionary algorithm

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A multi-stage ensemble model for power load forecasting based on decomposition, error factors, and multi-objective optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年第PartB期155卷

作者： Fan, Chaodong Nie, Shanghao Xiao, Leyi Yi, Lingzhi Wu, Yuetang Li, Gongrong Xiangtan Univ Coll Automat & Elect Informat Xiangtan 411105 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Hunan Software Vocat & Tech Univ Sch Software & Informat Engn Xiangtan 411100 Peoples R China Hunan Univ Finance & Econ Sch Informat Technol & Management Changsha 410205 Peoples R China

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.

关键词： Short-term load forecasting GRU network Ensemble learning multi-objective evolutionary algorithm

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