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Strategy for Individuals Distribution by Incident Nodes Participation in Star Topology of distributed evolutionary algorithms

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CYBERNETICS AND INFORMATION TECHNOLOGIES 2016年第1期16卷 80-88页

作者： Balabanov, Todor Zankinski, Iliyan Barova, Maria BAS Inst Informat & Commun Technol Sofia 1113 Bulgaria

One of the strongest advantages of distributed evolutionary algorithms (DEAs) is that they can be implemented in distributed environment of heterogeneous computing nodes. Usually such computing nodes differ in hardware and operating systems. distributed systems are limited by network latency. Some evolutionary algorithms (EAs) are quite suitable for distributed computing implementation, because of their high level of parallelism and relatively less intensive network communication demands. One of the most widely used topologies for distributed computing is the star topology. In a star topology there is a central node with global EA population and many remote computation nodes which are working on a local population (usually sub-population of the global population). This model of distributed computing is also known as island model. What is common for DEAs is an operation called migration that transfers some individuals between local populations. In this paper, the term 'distribution' will be used instead of the term 'migration', because it is more accurate for the model proposed. This research proposes a strategy for distribution of EAs individuals in star topology based on incident node participation (INP). Solving the Rubik's cube by a Genetic Algorithm (GA) will be used as a benchmark. It is a combinatorial problem and experiments are done with a C++ program which uses OpenMPI.

关键词： distributed evolutionary algorithms migration strategy incident nodes participation

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An Asynchronous distributed Cooperative Coevolutionary Algorithm for Multilayer Influence Maximization

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年

作者： Yang, Guo Wei, Feng-Feng Hu, Xiao-Min Jeon, Sang-Woon Zhang, Jun Chen, Wei-Neng South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510006 Peoples R China South China Univ Technol State Key Lab Subtrop Bldg & Urban Sci Guangzhou 510006 Peoples R China Guangdong Univ Technol Dept Comp Sci Guangzhou 510006 Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan 15588 South Korea Nankai Univ Coll Artificial Intelligence Tianjin 30071 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan 15588 South Korea

The influence maximization (IM) problem in large-scale social networks has attracted great attention. Considering the interactions among multiple online social platforms, the multilayer IMproblem poses further challenges (i.e., high-simulation burden and low-optimization quality). To solve these problems, this article proposes a susceptible-exposed-infected1-infected2-infected12-vigilant (SE3IV) model to simulate the information spreading process in multilayer networks. The spreading dynamic is modeled by mean-field equations considering the effect of cross-layer propagation. To optimize the multilayer information maximization modeled by SE3IV, an asynchronous distributed cooperative coevolutionary algorithm (ADCA) is proposed. To improve the efficiency of the algorithm in multilayer networks, the multilayer community detection first decompresses the network into a single layer by dimension-based method. Then, the Louvain method is adopted to decompose the problems into subcomponents with lower dimensionality. The populations with the same size evolve corresponding subcomponents in an asynchronous and distributed way based on the pool model. Besides, an asynchronous communication mechanism is devised to manage the communication among the shared pool. An adaptive seeds regulation strategy is designed to adjust the number of seeds of subcomponents. Numerous experiments on different networks show that ADCA possesses good scalability and efficiency, especially in large-scale networks.

关键词： Nonhomogeneous media Social networking (online) Computational modeling Mathematical models Integrated circuit modeling Optimization Heuristic algorithms Search problems Scalability Regulation distributed evolutionary algorithms distributed optimization influence maximization (IM)

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Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models

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COMPUTATIONAL GEOSCIENCES 2023年第6期27卷 939-954页

作者： Chandra, Rohitash Sharma, Yash Vardhan Univ New South Wales Sch Math & Stat Transit Artificial Intelligence Res Grp Sydney Australia Indian Inst Technol Roorkee Mech & Ind Engn Dept Roorkee India

evolutionary algorithms provide gradient-free optimisation which is beneficial for models that have difficulty in obtaining gradients;for instance, geoscientific landscape evolution models. However, such models are at times computationally expensive and even distributed swarm-based optimisation with parallel computing struggle. We can incorporate efficient strategies such as surrogate-assisted optimisation to address the challenges;however, implementing inter-process communication for surrogate-based model training is difficult. In this paper, we implement surrogate-based estimation of fitness evaluation in distributed swarm optimisation over a parallel computing architecture. We first test the framework on a set of benchmark optimisation problems and then apply to a geoscientifc model that features landscape evolution model. Our results demonstrate very promising results for benchmark functions and the Badlands landscape evolution model. We obtain a reduction in computationally time while retaining optimisation solution accuracy through the use of surrogates in a parallel computing environment. The major contribution of the paper is in the application of surrogate-based optimisation for geoscientific models which can in the future help in better understanding of paleoclimate and geomorphology.

关键词： distributed evolutionary algorithms Surrogate-assisted optimization Bayesian optimisation Parallel computing Neuroevolution

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distributed Bayesian optimisation framework for deep neuroevolution

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NEUROCOMPUTING 2022年第0期470卷 51-65页

作者： Chandra, Rohitash Tiwari, Animesh Univ New South Wales UNSW Data Sci Hub Sydney NSW Australia Univ New South Wales Sch Math & Stat Sydney NSW Australia Indian Inst Technol Guwahati Dept Civil Engn Gauhati Assam India

Neuroevolution is a machine learning method for evolving neural networks parameters and topology with a high degree of flexibility that makes them applicable to a wide range of architectures. Neuroevolution has been popular in reinforcement learning and has also shown to be promising for deep learning. The major feature of Bayesian optimisation is in reducing computational load by approximating the actual model with an acquisition function (surrogate model) that is computationally cheaper. A major limitation of neuroevolution is the high computational time required for convergence since learning (evo-lution) typically does not utilize gradient information. Bayesian optimisation, which is also known as surrogate-assisted optimisation, has been popular for expensive engineering optimisation problems and hyper-parameter tuning in machine learning. It has potential for training deep learning models via neuroevolution given large datasets and complex models. Recent advances in parallel and distributed computing have enabled efficient implementation of neuroevolution for complex and computationally expensive neural models. In this paper, we present a Bayesian optimisation framework for deep neu-roevolution using a distributed architecture to provide computational efficiency in training. Our results demonstrate promising results for simple to deep neural network models such as convolutional neural networks which motivates further applications. (c) 2021 Elsevier B.V. All rights reserved.

关键词： distributed evolutionary algorithms Surrogate-assisted optimization Bayesian optimisation Parallel computing Neuroevolution

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Random Selection of Parameters in Asynchronous Pool-Based evolutionary algorithms

Random Selection of Parameters in Asynchronous Pool-Based Ev...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Garcia-Valdez, Mario Marquez, Rene Trujillo, Leonardo Merelo, J. J. Inst Tecnol Tijuana Tijuana BC Mexico Univ Granada Comp Architecture & Technol Granada Spain

ISBN: (纸本)9781728183923

Synchronous operation is not the most natural, as in biologically inspired, mode to run distributed algorithms. In many grid, cloud or volunteer setups nodes are heterogeneous, or simply are not available at the exact same time;this is a challenge for the researcher if their full performance is going to be actually leveraged. Asynchronous distributed evolutionary algorithms try to solve this by dropping the homogeneity, as well as the synchronicity, assumption. These algorithms share the population between distributed workers which execute the actual evolutionary process by taking samples of the population, and replacing them in the population pool by evolved individuals. The performance of these EAs depends in part on the selection of parameters for the EA running in each worker. In this paper we study how randomly varying parameters in distributed evolutionary algorithms affects performance. Experiments were conducted in the AWS cloud using 2, 6 and 12 virtual machine configurations, with both homogeneous and heterogeneous random settings using five test functions for real-valued optimization and the OneMax binary problem. The results suggest that this method can produce a performance that is competitive with instances of the algorithm using workers with parameters specially tuned for the benchmark.

关键词： distributed evolutionary algorithms Volunteer Computing Cloud Computing pool-based evolutionary algorithms parameter optimization

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A Hybrid distributed EA Approach for Energy Optimisation on Smartphones

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EMPIRICAL SOFTWARE ENGINEERING 2022年第6期27卷 155-155页

作者： Bokhari, Mahmoud A. Alexander, Bradley Taibah Univ Comp Sci Dept Software Engn Res Grp Medina Saudi Arabia Univ Adelaide Sch Comp Sci Optimisat & Logist Adelaide SA Australia

For many people, mobile platforms are now an essential part of everyday life. A defining feature of mobile platforms is their reliance on battery performance. Due to this reliance, there is a pressing need for mobile applications that minimise their own impact on batteries. While mobile platforms are improving their capabilities in terms of policing the energy use of applications and rationing energy-hungry devices, mobile application developers still lack knowledge in how to write energy efficient programs. Recent work in automatic program improvement using heuristic search over randomly generated program variants has shown some promise in terms of producing reductions in programs' energy-use. A challenge in this work is accurately measuring the energy-use of program variants. One approach to measurement is to use each platform's internal meter to assess variants on the device itself. This approach has advantages in terms of measuring actual energy-use on each platform but is not ideal for the search for program variants that perform well across multiple platforms. The work in this paper addresses this problem by using an island-like evolutionary search mode to simultaneously evolve variants on multiple platforms. Island models of evolutionary search conduct search on multiple platforms in parallel and share promising variants. The results show that this approach has advantages over isolated evolution in terms of speeding up evolution on each platform and improving the efficiency of search. Validation results show that the island-inspired model is able to evolve variants with good cross-platform performance. In addition, it evolves a solution that outperforms best found solutions using a sequential evolutionary algorithm on it is native platform with an effect size greater than 90%.

关键词： Search-based software engineering Genetic improvement of software distributed evolutionary algorithms Islands model Energy optimisation Android

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A distributed Swarm Optimizer With Adaptive Communication for Large-Scale Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2020年第7期50卷 3393-3408页

作者： Yang, Qiang Chen, Wei-Neng Gu, Tianlong Zhang, Huaxiang Yuan, Huaqiang Kwong, Sam Zhang, Jun Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Engn Guilin 541004 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China Dongguan Univ Technol Sch Comp Sci & Network Secur Dongguan 523808 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Large-scale optimization with high dimensionality and high computational cost becomes ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed evolutionary computation algorithms is imperative. To this end, this paper proposes a distributed swarm optimizer based on a special master-slave model. Specifically, in this distributed optimizer, the master is mainly responsible for communication with slaves, while each slave iterates a swarm to traverse the solution space. An asynchronous and adaptive communication strategy based on the request-response mechanism is especially devised to let the slaves communicate with the master efficiently. Particularly, the communication between the master and each slave is adaptively triggered during the iteration. To aid the slaves to search the space efficiently, an elite-guided learning strategy is especially designed via utilizing elite particles in the current swarm and historically best solutions found by different slaves to guide the update of particles. Together, this distributed optimizer asynchronously iterates multiple swarms to collaboratively seek the optimum in parallel. Extensive experiments on a widely used large-scale benchmark set substantiate that the distributed optimizer could: 1) achieve competitive effectiveness in terms of solution quality as compared to the state-of-the-art large-scale methods;2) accelerate the execution of the algorithm in comparison with the sequential one and obtain almost linear speedup as the number of cores increases;and 3) preserve a good scalability to solve higher dimensional problems.

关键词： Optimization Adaptation models Computational modeling Computer science Topology Sociology Statistics distributed evolutionary algorithms elite-guided learning (EGL) high-dimensional problems large-scale optimization particle swarm optimization (PSO)

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Solving Combinatorial Puzzles with Parallel evolutionary algorithms 12th

Solving Combinatorial Puzzles with Parallel Evolutionary Alg...

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12th International Conference on Large-Scale Scientific Computations (LSSC)

作者： Balabanov, Todor Ivanov, Stoyan Ketipov, Rumen Bulgarian Acad Sci Inst Informat & Commun Technol Acad Georgi Bonchev StrBlock 2 Sofia 1113 Bulgaria

ISBN: (纸本)9783030410322;9783030410315

Rubik's cube is the most popular combinatorial puzzle. It is well known that solutions of the combinatorial problems are generally hard to find. If 90. clockwise rotations of the cube's sides are taken as operations it will give a minimal cube's grammar. By building formal grammar sentences with the usage of the six operations ([L]eft, [R]ight, [T]op, [D]own, [F]ront, [B]ack) all cube's permutations can be achieved. In an evolutionary algorithms (like genetic algorithms for example) set of formal grammar sentences can be represented as population individuals. Single cut point crossover can be efficiently applied when population individuals are strings. Changing randomly selected operation with another randomly selected operation can be used as efficient mutation operator. The most important part of such global optimization is the fitness function. For better individuals fitness value evaluation a combination between Euclidean and Hausdorff distances is proposed in this research. The experiments in this research are done as parallel program written in C++ and Open MPI.

关键词： distributed evolutionary algorithms Combinatorial puzzles Integer optimization

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distributed multi-objective evolutionary optimization using island-based selective operator application

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APPLIED SOFT COMPUTING 2019年 85卷 105757-000页

作者： Garcia-Sanchez, P. Ortega, J. Gonzalez, J. Castillo, P. A. Merelo, J. J. Univ Cadiz Dept Comp Sci & Engn ESI Cadiz Spain Univ Granada Dept Comp Architecture & Technol ETSIIT CITIC Granada Spain

Parallel island-model co-evolutionary algorithms are well-known methods, suitable for dealing with large multi-objective optimization problems. This paper proposes a version of these algorithms where each island modifies a fragment of the chromosome that encodes a possible solution to the problem. The objective of this paper is to demonstrate that automatically setting the size of the overlapping fragments depending on the number of islands obtains better results than using a fixed overlapping size. This method has been compared to other parallel evolutionary techniques considering a different number of islands, chromosome sizes and benchmarks. The analysis of the obtained experimental results, by using different metrics, shows that our approach can provide statistically significant improvements with respect to the base algorithm in high-dimensional, un-decomposable, multiobjective problems. This opens a very promising line to automatically adapt the overlapping sizes in this kind of algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-objective algorithms NSGA-II distributed evolutionary algorithms Island model

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Heterogenní ostrovní modely

Heterogenní ostrovní modely

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作者： Balcar, Štěpán Charles University of Prague

The work deals with heterogeneous island models. The work designs and implements a new island model based on knowledge of homogeneous models of evolutionary algorithms. The model allows dynamic replanning of general c... 详细信息

关键词： distributed evolutionary algorithms adaptability island architecture

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