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parallel evolutionary algorithm for Water Distribution Network Design, using the Masters-Students model in distributed environment

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APPLIED SOFT COMPUTING 2023年 135卷

作者： Avila-Melgar, Erika Yesenia Cruz-Chavez, Marco Antonio Martinez-Bahena, Beatriz Erana-Diaz, Marta Lilia Cruz-Rosales, Martin H. Autonomous Univ Morelos State Res Ctr Engn & Appl Sci Cuernavaca Morelos Mexico Autonomous Univ Morelos State Fac Accounting Adm & Informat Cuernavaca Morelos Mexico

The Water Distribution Networks Design Problem study is widespread in the scientific community due to its practical applicability. In this work, a sequential evolutionary algorithm has been designed, developed, and successfully applied to solve small and medium instances;it has been called the evolutionary algorithm for Water Distribution Network Design Problem. This algorithm is executed in centralized environments and can perfectly solve these instances in less than minutes. For large-scale real-world instances, the evolutionary algorithm has been adapted to work in distributed environments by using a novel parallel model, also proposed in this work, called the Masters-Students model. This model has been used for designing, developing, and implementing the resulting parallel evolutionary algorithm, named PEA-WDND. The evolutionary algorithm would last for days in the case of real-world instances, but the parallel algorithm solves them in seconds or, at maximum, in minutes. This study shows that the parallel algorithm yields an execution time lower than the execution time obtained from the evolutionary algorithm for different theoretical and practical instances.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Water Distribution Network Design Problem Large-scale instance parallel evolutionary algorithm Grid Environment Performance evaluation Local search

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Procedural Enemy Generation through parallel evolutionary algorithm 20

Procedural Enemy Generation through Parallel Evolutionary Al...

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20th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)

作者： Pereira, Leonardo T. Viana, Breno M. F. Toledo, Claudio F. M. Univ Sao Paulo Inst Ciencias Matemat & Comp ICMC Sao Carlos SP Brazil

ISBN: (纸本)9781665401890

This research presents a parallel evolutionary algorithm (PEA) that generates enemies with diverse characteristics, such as the enemy's health, weapons, and movement. Our PEA aims to create enemies matching their difficulty degrees with the difficulty goal given as input parameter. We designed our algorithm in this way to be future used in an online adaptive generation system. We experimented with a set of generated enemies with an Action-Adventure game prototype as a testbed. The results show that players evaluated our approach positively, successfully creating enemies considered easy, medium, or hard to face, as defined by their original fitness' target value. Besides, the players found the game fun to play for all difficulty levels played, and the perceived challenge rose as the PEA fitness was higher. In terms of performance results, our PEA converged into the input solution in less than a second for most cases, denoting its future use in online adaptive applications.

关键词： enemy generation procedural content generation video games parallel evolutionary algorithm

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Exterior lighting computer-automated design based on multi-criteria parallel evolutionary algorithm: optimized designs for illumination quality and energy efficiency

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 45卷 208-222页

作者： Rocha, Hugo Peretta, Igor S. Lima, Gerson Flavio M. Marques, Leonardo G. Yamanaka, Keiji Fed Inst Goias BR-75524010 Itumbiara Go Brazil Univ Fed Uberlandia Fac Elect Engn BR-38400902 Uberlandia MG Brazil

A proper professional lighting design implies in a continuous search for the best compromise between both low power consumption and better lighting quality. This search converts this design into a hard to solve multi-objective optimization problem. evolutionary algorithms are widely used to attack that type of hard optimization problems. However, professionals could not benefit from that kind of assistance since evolutionary algorithms have been unexplored by several commercial lighting design computer-aided softwares. This work proposes a system based on evolutionary algorithms which implement a computer-automated exterior lighting design both adequate to irregular shaped areas and able to respect lighting pole positioning constraints. The desired lighting design is constructed using a cluster of computers supported by a web client, turning this application into an efficient and easy tool to reduce project cycles, increase quality of results and decrease calculation times. This ELCAutoD-EA system consists in a proposal for a parallel multi-objective evolutionary algorithm to be executed in a cluster of computers with a Java remote client. User must choose lighting pole heights, allowed lamps and fixtures, as well as the simplified blue print of the area to be illuminated, marking the sub-areas with restrictions to pole positioning. The desired average illuminance must also be informed as well as the accepted tolerance. Based on user informed data, the developed application uses a dynamic representation of variable size as a chromosome and the cluster executes the evolutionary algorithm using the Island model paradigm. Achieved solutions comply with the illumination standards requirements and have a strong commitment to lighting quality and power consumption. In the present case study, the evolved design used 37.5% less power than the reference lighting design provided by a professional and at the same time ensured a 227.3% better global lighting uniformity. A bette

关键词： Computer-automated exterior lighting design Illumination quality Energy efficiency parallel evolutionary algorithm Multi-objective optimization NSGA-II

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Multi Operators-based Partial Connected parallel evolutionary algorithm

Multi Operators-based Partial Connected Parallel Evolutionar...

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IEEE Congress on evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Laili, Yuanjun Tao, Fet Zhang, Lin Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781509006229

With the increase of dimensions and complexity of current engineering problems, parallel evolutionary algorithm which take advantage of population division and information exchange among processors has been introduced for years. However, low solution ability of each sub-group and high communication load between them are always seen as the biggest bottlenecks which hinder parallel evolutionary algorithm to be more efficient. To overcome this two problems, a multi operators-based partial connected parallel evolutionary algorithm, i. e. MO-PCPEA is proposed. By combining multiple evolutionary operators, an adaptive strategy for operator configuration inside each parallel group is designed to ensure the searching ability of the algorithm for wider range of problems. More importantly, a partial connection topology is proposed to guide the periodic communication between each group. Computational results in two typical permutation combinatorial optimization benchmarks and one practical case study demonstrate that MO-PCPEA is highly competitive compared with most tailored serial and parallel evolutionary algorithms in terms of not only searching time, but also solution quality.

关键词： Partial connected topology parallel evolutionary algorithm multi-operators configuration permutation combinatorial optimization

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HAS-EA: a fast parallel surrogate-assisted evolutionary algorithm

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MEMETIC COMPUTING 2023年第1期15卷 103-115页

作者： Li, Yixian Zhong, Jinghui South China Univ Technol Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China

Many real-world phenomena are modeled as expensive optimization problems (EOPs) that are not readily solvable without extensive computational cost. Surrogate-assisted mechanisms and parallel computing techniques are effective approaches to improving the search performance of evolutionary algorithms for these EOPs. However, the search efficiency of existing methods are limited by a combination of synchronization barriers and a failure to use heterogeneous computing resources fully. Therefore, we propose an efficient heterogeneous asynchronous parallel surrogate-assisted evolutionary algorithm (HAS-EA). The proposed HAS-EA incorporates an improved asynchronous parallel evolutionary algorithm module on the CPU, a surrogate module on the GPU, and an improved asynchronous recommendation module on the CPU. By performing these operations in parallel on heterogeneous computing resources, the search performance can be accelerated. Test results of our proposed method with several benchmark problems and a real-world model calibration problem demonstrate that HAS-EA offers better performance than other recently published methods in solving EOPs.

关键词： evolutionary algorithm parallel evolutionary algorithm parallel surrogate-assisted evolutionary algorithm Heterogeneous parallelism

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Optimization of buffer allocation in mixed production systems with multiple classes, variable batch sizes and worker fatigue considerations

ROBOTIC INTELLIGENCE AND AUTOMATION

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ROBOTIC INTELLIGENCE AND AUTOMATION 2025年第1期45卷 144-158页

作者： Gao, Sixiao Liao, Yinsheng Hu, Zhiming BYD Auto Ind Co Ltd Shenzhen Peoples R China

PurposeWith rapid market customized demand and short development cycles, mixed production with multiple classes and variable batches has been popular, and its buffer allocation problem has become a new challenge. The mixed production cannot be analyzed based on the assumption of a stationary demand process which was typically used in previous studies. Furthermore, mixed production is still in human-machine cooperation mode where dynamic working efficiency because of workers' fatigue causes uncertain processes. Therefore, the purpose of this study is to solve the buffer allocation problem in mixed production systems with multiple classes, variable batch sizes and worker fatigue ***/methodology/approachA dynamic modeling method of mixed production with multiple classes and variable batches is improved, which uses nonstationary demand processes to model the dynamic nature of multiple classes and variable batches. Human working efficiency decreasing due to fatigue is modeled as the time-varying service rate to represent human-machine cooperation. Furthermore, a parallel evolutionary algorithm that combines global and local search strategies parallelly is developed to solve the buffer allocation problem in mixed production for the first *** examples demonstrate the efficacy of the proposed algorithm. The proposed algorithm achieves better solution quality than the state of art ***/valueThis study improves the dynamic modeling of mixed production to consider human factors and develops a hybrid algorithm to effectively solve the buffer allocation problem in dynamic mixed production.

关键词： Buffer allocation Mixed production Dynamic modeling parallel evolutionary algorithm Variable neighborhood

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Communication Intensive Task Offloading With IDMZ for Secure Industrial Edge Computing

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2025年第2期13卷 560-577页

作者： Laili, Yuanjun Gong, Jiabei Kong, Yusheng Wang, Fei Ren, Lei Zhang, Lin Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Zhongguancun Lab Beijing 100094 Peoples R China Beihang Univ Hangzhou Int Innovat Inst Hangzhou 311115 Peoples R China

The Industrial Internet of Things provides an opportunity for flexible and collaborative manufacturing, but introduces more risk and more communication overhead from the Internet to the industrial field. To avoid attacks from unreliable service providers and requesters, Industrial Demilitarized Zone (IDMZ) is introduced in conjunction with firewalls to provide new communication modes between edge servers and industrial devices. As the number of tasks being offloaded to the edge side increases, optimal task offloading to balance the risk and the communication overhead with limited demilitarized buffer size becomes a challenge. Therefore, this paper establishes a mathematical model for secure task offloading in the Industrial Internet-of-Things considering dense communication with different communication modes. Then, a parallel Gbest-centric differential evolution (P-G-DE) is designed to solve this task offloading problem with a heuristic-embedded initialization strategy, a modified Gbest-centric differential evolutionary operator and a circular-rotated parallelization scheme. The experimental results verify that the proposed method is capable of providing a high-quality solution with a lower risk and a shorter execution time in seconds, compared to six state-of-the-art evolutionary algorithms.

关键词： Industrial Internet of Things Servers Heuristic algorithms Cloud computing Firewalls (computing) Energy consumption Optimization Mathematical models Data communication Costs Industrial Internet-of-Things task offloading industrial DMZ heuristic embedding parallel evolutionary algorithm

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Scalable parallel evolutionary optimization on high performance computing

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Aerospace Traffic and Safety 2024年第2期1卷 93-102页

作者： Chen Jin Daren Zheng Shuke He Ao Cheng Gang Liu Key Laboratory of Civil Aviation Emergency Science&Technology CAACNanjing University of Aeronautics and AstronauticsNanjingChina Zhejiang Scientific Research Institute of Transport HangzhouChina School of Computer Science and Information Technology RMIT UniversityMelbourneAustralia Key Laboratory of Aircraft Environment Control and Life Support MIITNanjing University of Aeronautics and AstronauticsNanjingChina Zhejiang Jiande Institute of General Aviation HangzhouChina Zhejiang ATM Sub-Bureau.CAAC HangzhouChina

To improve the efficiency of evolutionary algorithms(EAs)for solving complex problems with large populations,this paper proposes a scalable parallel evolution optimization(SPEO)framework with an elastic asynchronous migration(EAM)*** addresses two main challenges that arise in large-scale parallel EAs:(1)heavy communication workload from extensive information exchange across numerous processors,which reduces computational efficiency,and(2)loss of population diversity due to similar solutions generated and shared by many *** EAM mechanism introduces a self-adaptive communication scheme to mitigate communication overhead,while a diversity-preserving buffer helps maintain diversity by filtering similar *** results on eight CEC2014 benchmark functions using up to 512 CPU cores on the Australian National Computational Infrastructure(NCI)platform demonstrate that SPEO not only scales efficiently with an increasing number of processors but also achieves improved solution quality compared to state-of-the-art island-based EAs.

关键词： parallel evolutionary algorithm High performance computing(HPC) Computational efficiency Asynchronous migration

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Spark-ITGO: a parallel invasive tumor growth optimization algorithm on spark

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第4期25卷 2633-2660页

作者： Lin, Jiaqin Xiao, Anghong Dong, Shoubin Zhou, Jing South China Univ Technol Sch Comp Sci & Engn Guangdong Key Lab Comp Network Wushan Rd 381 Guangzhou Peoples R China

The rapidly increasing complexity and scale of optimization problems pose challenges to search ability and performance of traditional evolutionary algorithms which could be only executed sequentially without scalability and are difficult to obtain an ideal solution in a reasonable time. In this paper, Spark-ITGO, a parallel and scalable invasive tumor growth optimization algorithm on Spark, is presented based on the serial invasive tumor growth optimization (ITGO). In Spark-ITGO, a parallel multiple-tumor evolution model is proposed to search the optimal solution of problems. A balanced multi-island optimal migration strategy is designed to increase diversity of population and prevent converging into a local optimum. Additionally, a universal parallel evolutionary algorithm framework is implemented based on resilient distributed dataset (RDD) and central broadcast mechanism. Spark-ITGO is evaluated on benchmark experiments of CEC2013 and CEC2010 LSGO and the results show that it achieves great scalability and performs better than other evolutionary algorithms.

关键词： parallel evolutionary algorithm Spark Island model Migration strategy

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Deploying massive runs of evolutionary algorithms with ECJ and Hadoop: Reducing interest points required for face recognition

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2018年第5期32卷 706-720页

作者： Chavez, Francisco de Vega, Francisco Fernandez Lanza, Daniel Benavides, Cesar Villegas, Juan Trujillo, Leonardo Olague, Gustavo Roman, Graciela Univ Extremadura Dept Comp Sci Merida Venezuela CERN European Org Nucl Res Geneva Switzerland Univ Autonoma Metropolitana Dept Ingn Mexico City DF Mexico Univ Autonoma Metropolitana Dept Elect Mexico City DF Mexico Inst Tecnol Tijuana Calzada Tecnol S-N Mexico City DF Mexico CICESE Mexico City DF Mexico

In this paper we present a new strategy for deploying massive runs of evolutionary algorithms with the well-known evolutionary Computation Library (ECJ) tool, which we combine with the MapReduce model so as to allow the deployment of computing intensive runs of evolutionary algorithms on big data infrastructures. Moreover, by addressing a hard real life problem, we show how the new strategy allows us to address problems that cannot be solved with more traditional approaches. Thus, this paper shows that by using the Hadoop framework ECJ users can, by means of a new parameter, choose where the run will be launched, whether in a Hadoop based infrastructure or in a desktop computer. Moreover, together with the performed tests we address the well-known face recognition problem with a new purpose: to allow a genetic algorithm to decide which are the more relevant interest points within the human face. Massive runs have allowed us to reduce the set from about 60 to just 20 points. In this way, recognition tasks based on the solution provided by the genetic algorithm will work significantly quicker in the future, given that just 20 points will be required. Therefore, two goals have been achieved: (a) to allow ECJ users to launch massive runs of evolutionary algorithms on big data infrastructures and also (b) to demonstrate the capabilities of the tool to successfully improve results regarding the problem of face recognition.

关键词： ECJ face recognition Hadoop parallel evolutionary algorithm

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