research-article

evospace-js: asynchronous pool-based execution of heterogeneous metaheuristics

Authors:

Mario García-Valdez,

JJ MereloAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1202 - 1208

https://doi.org/10.1145/3067695.3082473

Published: 15 July 2017 Publication History

Abstract

This paper is part of a continuing effort in the field of EC to develop algorithms that follow an opportunistic approach to computing, allowing the exploitation of freely available services over the Internet by using free tiers of cloud services or volunteer computing resources; the EvoSpace model is able to tap from both kind of resources, using asynchronous evolutionary algorithms. We present its design, which follows an an event-driven architecture and asynchronous I/O model, and its implementation, with a server-side tier programmed in Node.js that uses Redis as an in-memory and high performance data store for the population. This population store is exposed to clients running population-based and nature-inspired metaheuristics through a REST API. Additional capabilities where implemented in this version to allow the logging of experiments where heterogeneous algorithms are executed in parallel. These logs can then be transformed to other formats. As a case study an hybrid global optimization algorithm has been implemented mixing two algorithms: a PSO algorithm from the EvoloPy library and a GA using the DEAP framework. The result was transformed to files compatible to the Comparing Continuous Optimizer platform in order to use their post-processing code. Clients in this case have been developed in the Python language, the language used to implement both libraries. The results from this case study suggest, first, that EvoSpace can be used as a paradigm- and language-agnostic platform for population-based optimization algorithms, and also that this software can yield performance improvements and a viable platform to execute and compare asynchronous pool-based metaheuristics.

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Cited By

García-Valdez MMancilla ACastillo OMerelo-Guervós J(2023)Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy ControllersSymmetry10.3390/sym1502046715:2(467)Online publication date: 9-Feb-2023
https://doi.org/10.3390/sym15020467
Garcia-Valdez MMarquez RTrujillo LMerelo J(2021)Random Selection of Parameters in Asynchronous Pool-Based Evolutionary Algorithms2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504880(2531-2538)Online publication date: 28-Jun-2021
https://doi.org/10.1109/CEC45853.2021.9504880
Khalloof HMohammad MShahoud SDuepmeier CHagenmeyer V(2021)A generic flexible and scalable framework for hierarchical parallelization of population-based metaheuristicsInternet of Things10.1016/j.iot.2021.100433(100433)Online publication date: Jul-2021
https://doi.org/10.1016/j.iot.2021.100433
Show More Cited By

Index Terms

evospace-js: asynchronous pool-based execution of heterogeneous metaheuristics
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2017

1934 pages

ISBN:9781450349390

DOI:10.1145/3067695

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 15 July 2017

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Ministerio espanol de Economia y Competitividad

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GECCO '17

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

García-Valdez MMancilla ACastillo OMerelo-Guervós J(2023)Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy ControllersSymmetry10.3390/sym1502046715:2(467)Online publication date: 9-Feb-2023
https://doi.org/10.3390/sym15020467
Garcia-Valdez MMarquez RTrujillo LMerelo J(2021)Random Selection of Parameters in Asynchronous Pool-Based Evolutionary Algorithms2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504880(2531-2538)Online publication date: 28-Jun-2021
https://doi.org/10.1109/CEC45853.2021.9504880
Khalloof HMohammad MShahoud SDuepmeier CHagenmeyer V(2021)A generic flexible and scalable framework for hierarchical parallelization of population-based metaheuristicsInternet of Things10.1016/j.iot.2021.100433(100433)Online publication date: Jul-2021
https://doi.org/10.1016/j.iot.2021.100433
Khalloof HMohammad MShahoud SDuepmeier CHagenmeyer VChbeir RManolopoulos YDamiani EBenslimane DBellatreche LMorzy T(2020)A Generic Flexible and Scalable Framework for Hierarchical Parallelization of Population-Based MetaheuristicsProceedings of the 12th International Conference on Management of Digital EcoSystems10.1145/3415958.3433041(124-131)Online publication date: 2-Nov-2020
https://dl.acm.org/doi/10.1145/3415958.3433041
Khalloof HJakob WShahoud SDuepmeier CHagenmeyer V(2020)A Generic Scalable Method for Scheduling Distributed Energy Resources Using Parallelized Population-Based MetaheuristicsProceedings of the Future Technologies Conference (FTC) 2020, Volume 210.1007/978-3-030-63089-8_1(1-21)Online publication date: 1-Nov-2020
https://doi.org/10.1007/978-3-030-63089-8_1
Khalloof HOstheimer PJakob WShahoud SDuepmeier CHagenmeyer V(2020)A Distributed Modular Scalable and Generic Framework for Parallelizing Population-Based MetaheuristicsParallel Processing and Applied Mathematics10.1007/978-3-030-43229-4_37(432-444)Online publication date: 19-Mar-2020
https://doi.org/10.1007/978-3-030-43229-4_37
Zhang SWang QKanemasa YShan HHu L(2019)The Impact of Event Processing Flow on Asynchronous Server EfficiencyIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2019.2938500(1-1)Online publication date: 2019
https://doi.org/10.1109/TPDS.2019.2938500
Khalloof HOstheimer PJakob WShahoud SDuepmeier CHagenmeyer V(2019)Superlinear Speedup of Parallel Population-Based Metaheuristics: A Microservices and Container Virtualization ApproachIntelligent Data Engineering and Automated Learning – IDEAL 201910.1007/978-3-030-33607-3_42(386-393)Online publication date: 18-Oct-2019
https://doi.org/10.1007/978-3-030-33607-3_42
Khalloof HJakob WLiu JBraun EShahoud SDuepmeier CHagenmeyer VTakadama K(2018)A generic distributed microservices and container based framework for metaheuristic optimizationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208253(1363-1370)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208253

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