research-article

Evolving multi-objective neural networks using differential evolution for dynamic economic emission dispatch

Authors:

Enda HowleyAuthors Info & Claims

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

Pages 1287 - 1294

https://doi.org/10.1145/3067695.3082480

Published: 15 July 2017 Publication History

Abstract

This research presents a novel framework for evolving Multi-Objective Neural Networks using Differential Evolution (MONNDE). In recent years, the Differential Evolution algorithm has shown to be an effective and robust global optimisation algorithm. The algorithm uses evolutionary operators to optimise complex and continuous problem spaces and has been applied to a range of problems, recently including neural networks. This research continues this trend by utilizing differential evolution to evolve neural networks capable of addressing dynamic problems with multiple objectives. The proposed MONNDE framework is applied to the Dynamic Economic Emission Dispatch (DEED) problem. This problem consists of scheduling a group of power generators in a manner that minimises both cost and emissions produced by the generators. The power generators must also meet a series of constraints relating to their power output, power demand and network loss. The proposed MONNDE is performs very competitively when compared to algorithms such as NSGA-II, PSO, PSOAWL and MARL.

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Cited By

Shaw RHowley EBarrett E(2019)An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload ClassificationService-Oriented Computing10.1007/978-3-030-33702-5_20(251-266)Online publication date: 22-Oct-2019
https://doi.org/10.1007/978-3-030-33702-5_20
Shaw RHowley EBarrett E(2018)A Predictive Anti-Correlated Virtual Machine Placement Algorithm for Green Cloud Computing2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)10.1109/UCC.2018.00035(267-276)Online publication date: Dec-2018
https://doi.org/10.1109/UCC.2018.00035
Duggan MShaw RDuggan JHowley EBarrett E(2018)A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centersSoftware: Practice and Experience10.1002/spe.263549:4(617-639)Online publication date: 16-Sep-2018
https://doi.org/10.1002/spe.2635
Show More Cited By

Index Terms

Evolving multi-objective neural networks using differential evolution for dynamic economic emission dispatch
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic algorithms
      2. Neural networks
2. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Continuous optimization
        Bio-inspired optimization
      2. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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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 ACM 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 July 2017

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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

Shaw RHowley EBarrett E(2019)An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload ClassificationService-Oriented Computing10.1007/978-3-030-33702-5_20(251-266)Online publication date: 22-Oct-2019
https://doi.org/10.1007/978-3-030-33702-5_20
Shaw RHowley EBarrett E(2018)A Predictive Anti-Correlated Virtual Machine Placement Algorithm for Green Cloud Computing2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)10.1109/UCC.2018.00035(267-276)Online publication date: Dec-2018
https://doi.org/10.1109/UCC.2018.00035
Duggan MShaw RDuggan JHowley EBarrett E(2018)A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centersSoftware: Practice and Experience10.1002/spe.263549:4(617-639)Online publication date: 16-Sep-2018
https://doi.org/10.1002/spe.2635
Duggan MMason KDuggan JHowley EBarrett E(2017)Predicting host CPU utilization in cloud computing using recurrent neural networks2017 12th International Conference for Internet Technology and Secured Transactions (ICITST)10.23919/ICITST.2017.8356348(67-72)Online publication date: Dec-2017
https://doi.org/10.23919/ICITST.2017.8356348

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