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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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Evolving filter criteria for randomly initialized network pruning in image classification

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NEUROCOMPUTING 2024年 594卷

作者： Chen, Xiangru Liu, Chenjing Hu, Peng Lin, Jie Gong, Yunhong Chen, Yingke Peng, Dezhong Geng, Xue Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Singapore 138632 Singapore Sichuan Newstrong UHD Video Technol Co Ltd Chengdu 610095 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST England

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.

关键词： Genetic algorithm Evolving deep learning Network pruning multi-objective evolutionary algorithm

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Modeling the Optimal Transition of an Urban Neighborhood towards an Energy Community and a Positive Energy District

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ENERGIES 2024年第16期17卷 4047页

作者： Viesi, Diego Borelli, Gregorio Ricciuti, Silvia Pernigotto, Giovanni Mahbub, Md Shahriar Fdn Bruno Kessler FBK Ctr Sustainable Energy Via Sommar 18 I-38123 Trento Italy Free Univ Bozen Bolzano UNIBZ Fac Engn Piazza Univ 5 I-39100 Bolzano Italy Ahsanullah Univ Sci & Technol AUST Dept Comp Sci & Engn Love Rd 141&142Tejgaon Ind Area Dhaka 1208 Bangladesh

Building renovation is a key initiative to promote energy efficiency, the integration of renewable energy sources (RESs), and a reduction in CO2 emissions. Supporting these goals, emerging research is dedicated to energy communities and positive energy districts. In this work, an urban neighborhood of six buildings in Trento (Italy) is considered. Firstly, the six buildings are modeled with the Urban Modeling Interface tool to evaluate the energy performances in 2024 and 2050, also accounting for the different climatic conditions for these two time periods. Energy demands for space heating, domestic hot water, space cooling, electricity, and transport are computed. Then, EnergyPLAN coupled with a multi-objective evolutionary algorithm is used to investigate 12 different energy decarbonization scenarios in 2024 and 2050 based on different boundaries for RESs, energy storage, hydrogen, energy system integration, and energy community incentives. Two conflicting objectives are considered: cost and CO2 emission reductions. The results show, on the one hand, the key role of sector coupling technologies such as heat pumps and electric vehicles in exploiting local renewables and, on the other hand, the higher costs in introducing both electricity storage to approach complete decarbonization and hydrogen as an alternative strategy in the electricity, thermal, and transport sectors. As an example of the quantitative valuable finding of this work, in scenario S1 "all sectors and EC incentive" for the year 2024, a large reduction of 55% of CO2 emissions with a modest increase of 11% of the total annual cost is identified along the Pareto front.

关键词： urban modeling interface EnergyPLAN multi-objective evolutionary algorithm energy community positive energy district

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Solving multi-objective weapon-target assignment considering reliability by improved MOEA/D-AM2M

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NEUROCOMPUTING 2024年 563卷

作者： Yi, Xiaojian Yu, Huiyang Xu, Tao Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing 314019 Zhejiang Peoples R China Beijing Inst Technol Tangshan Res Inst Tangshan 063099 Hebei Peoples R China

The weapon-target assignment problem is a challenging optimization issue, but reliability is seldom considered in the majority of existing literature. To address the high-reliability weapon-target assignment problem, this paper integrates weapon reliability and mission reliability into a multi-objective optimization model (MOD) and presents an improved algorithm termed MOEA/D-iAM2M to the problem. This algorithm effectively combines the strengths of adaptive search space decomposition-based MOEA (MOEA/D-AM2M) and two-stage hybrid learning-based MOEA (HLMEA), resulting in a faster convergence rate and a more extensive distribution of the Pareto solution set. Furthermore, a reference point is incorporated into MOEA/D-iAM2M to facilitate the adaptive weight adjustment. Numerical experiments are carried out to confirm the effectiveness of the proposed MOEA/D-iAM2M. This research is significant in the field of optimization and has practical value in the defense industry.

关键词： multi-objective optimization Weapon-target assignment Reliability multi-objective evolutionary algorithm

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Distributed assembly shop scheduling problem for complex products considering multiskilled worker assignment and transportation time

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年

作者： Gao, Qinglin Hu, Hanlin Liu, Jianhua Zhuang, Cunbo Ding, Hongyan Li, Huiting Beijing Inst Technol Sch Mech Engn Lab Digital Mfg Beijing 100081 Peoples R China Beijing Inst Technol Tangshan Res Inst Tangshan 063015 Peoples R China

The distributed assembly hybrid flow shop scheduling problem (DAHFSP) is a type of distributed shop scheduling problem, and each distributed shop can be regarded as a hybrid flow shop. In distributed assembly processes for complex products such as satellites and missiles, transportation time and worker assignment have important effects on production scheduling. Based on real production situations, this study proposes a novel DAHFSP considering worker assignment and transportation time, aiming to minimise the makespan and the imbalance degree of worker workloads. First, to solve these problems, we construct a mathematical model and design a two-layer chromosome coding scheme including worker assignment and task sequence. Then, in the local search stage, we propose a mutation-based search method and an elite search method. On that basis, we propose a multi-objective evolutionary algorithm with reinforced elite retention strategy (MOEA-RERS). Finally, based on a set of 12 test instances generated by actual enterprise production data, we compare the MOEA-RERS algorithm with five multi-objective evolutionary algorithms. The results show that the MOEA-RERS algorithm is superior to other algorithms in terms of solution quality and distribution.

关键词： Distributed assembly shop scheduling hybrid flow shop scheduling complex products workers assignment multi-objective evolutionary algorithm

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A framework for constrained large-scale multi-objective white-box problems based on two-scale optimization through decision transfer

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INFORMATION SCIENCES 2024年 665卷

作者： Wang, Qingzhu Li, Tianyang Meng, Fanqi Li, Bin Northeast Elect Power Univ Sch Comp Sci Jilin 132012 Peoples R China Guangdong Atv Acad Performing Arts Sch Informat Engn Dongguan 523710 Peoples R China

Most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not so efficient when handling constrained large-scale multi-objective problems (CLSMOPs). To overcome whitebox CLSMOPs with definitive objective functions, a two-scale optimization framework based on decision transfer, which integrates dimensionality reduction of large-scale decision variables and constraint handling technology, is proposed. The Lagrange multiplier is first used to construct the two-scale optimization model, which bridges original large-scale decision space of variables and small-scale (2-scale) decision space of objective-constraint parameter. The decision transfer algorithm is then designed to switch between large-scale original decision space and small-scale parametric decision space, while achieving the maximum dimensionality reduction. Finally, the two-scale evolution strategy is proposed for the alternative optimizations in the two decision spaces, which emphasize objectives and constraints, respectively. In summary, the optimization in the large-scale space pushes the population to unconstrained Pareto front (PF), the optimization in the small-scale space helps the population cross the infeasible areas to approach constrained PF, and the offspring generation by Lagrange multiplier is beneficial to both objectives and constraints. Eight representative and state-of-the-art CMOEAs have been embedded into the CLDTEA framework to demonstrate its effectiveness through comparative experiments on CLSMOPs with equality and inequality constraints and 1000 decision variables. Experimental results show that CLDTEA can significantly improve the performance of these basic CMOEAs.

关键词： Constrained large-scale multi-objective problem multi-objective evolutionary algorithm Decision transfer Lagrange multiplier

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