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A self-adaptive multiagent evolutionary algorithm for electrical machine design

Published: 07 July 2007 Publication History

Abstract

This paper presents a self-adaptive algorithm that hybridises evolutionary and multiagent concepts. Each evolutionary individual is implemented as a simple agent capable of re-production and predation. The transitions between these two states depend on the agent's local environment. Thus, no explicit global process is defined to select neither the mates nor the preys. The convergence of the algorithm emerges from the behaviour of the agents. This brings interesting properties, such as population size self-regulation. Two sets of experimental results are provided: a comparison with Saw-Tooth Algorithm and micro-GA using four classical functions and an optimisation of the efficiency and the weight of an electrical motor. Some possible evolutions and prospects are finally proposed.

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Cited By

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  • (2023)Exploring the Potential of Agent Systems for MetaheuristicsNew Metaheuristic Schemes: Mechanisms and Applications10.1007/978-3-031-45561-2_2(11-74)Online publication date: 7-Nov-2023
  • (2020)A new metaheuristic approach based on agent systems principlesJournal of Computational Science10.1016/j.jocs.2020.10124447(101244)Online publication date: Nov-2020
  • (2019)Trade-Off Between Diversity and Convergence in Multi-objective Genetic AlgorithmsData-Driven Modeling for Sustainable Engineering10.1007/978-3-030-13697-0_4(37-50)Online publication date: 22-Jun-2019
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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
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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New York, NY, United States

Publication History

Published: 07 July 2007

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Author Tags

  1. genetic algorithm
  2. multiagent system
  3. optimisation
  4. self adaptation

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2023)Exploring the Potential of Agent Systems for MetaheuristicsNew Metaheuristic Schemes: Mechanisms and Applications10.1007/978-3-031-45561-2_2(11-74)Online publication date: 7-Nov-2023
  • (2020)A new metaheuristic approach based on agent systems principlesJournal of Computational Science10.1016/j.jocs.2020.10124447(101244)Online publication date: Nov-2020
  • (2019)Trade-Off Between Diversity and Convergence in Multi-objective Genetic AlgorithmsData-Driven Modeling for Sustainable Engineering10.1007/978-3-030-13697-0_4(37-50)Online publication date: 22-Jun-2019
  • (2013)Introduction of a combination vector to optimise the interpolation of numerical phantomsExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.07.07740:2(492-499)Online publication date: 1-Feb-2013
  • (2012)Adaptive multi-objective genetic algorithm using multi-pareto-rankingProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330228(449-456)Online publication date: 7-Jul-2012
  • (2011)Using an evolutionary algorithm to optimize the broadcasting methods in mobile ad hoc networksJournal of Network and Computer Applications10.1016/j.jnca.2011.01.00434:6(1794-1804)Online publication date: 1-Nov-2011
  • (2010)Agent Based Evolutionary Approach: An IntroductionAgent-Based Evolutionary Search10.1007/978-3-642-13425-8_1(1-11)Online publication date: 2010
  • (2008)Tuning an evolutionary algorithm with taguchi methods and application to the dimensioning of an electrical motorProceedings of the 5th international conference on Soft computing as transdisciplinary science and technology10.1145/1456223.1456279(265-272)Online publication date: 28-Oct-2008
  • (2008)Optimizing communications in vehicular ad hoc networks using evolutionary computation and simulationProceedings of the 5th international conference on Soft computing as transdisciplinary science and technology10.1145/1456223.1456259(158-165)Online publication date: 28-Oct-2008
  • (2008)Permanent magnet motor multiobjective optimization using multiple runs of an evolutionary algorithm2008 IEEE Vehicle Power and Propulsion Conference10.1109/VPPC.2008.4677611(1-5)Online publication date: Sep-2008

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