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Fine-Grained Cooperative Coevolution in a Single Population: Between Evolution and Swarm Intelligence

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Artificial Evolution (EA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14091))

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Abstract

Particle Swarm Optimisation (PSO) and Evolutionary Algorithms (EAs) differ in various ways, in particular with respect to information sharing and diversity management, making their scopes of applications very diverse. Combining the advantages of both approaches is very attractive and has been successfully achieved through hybridisation. Another possible improvement, notably for addressing scalability issues, is cooperation. It has first been developed for co-evolution in EA techniques and it is now used in PSO. However, until now, attempts to make PSO cooperate have been based on multi-population schemes almost exclusively. The focus of this paper is set on single-population schemes, or fine-grained cooperation. By analogy with an evolutionary scheme that has long been proved effective, the fly algorithm (FA), we design and compare a cooperative PSO (coPSO), and a PSO-flavoured fly algorithm. Experiments run on a benchmark, the Lamp problem, show that fine-grained cooperation based on marginal fitness evaluations and steady-state schemes outperforms classical techniques when the dimension of the problem increases. These preliminary results highlight interesting future directions of research on fine-grained cooperation schemes, by combining features of PSO and FA.

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Notes

  1. 1.

    via inter-individual communications in PSO or genetic inheritance in EAs.

  2. 2.

    [18] defines cooperative search for any method as strategies that have several search modules running and exchanging information to improve search capability.

  3. 3.

    However, an application to the generation of improvised music [7] implements both types of cooperation, coarse and fine grained (this is not quite an optimisation, but rather an exploration task). It was performed with multi-swarms: each particle being a note (loudness, pulse and pitch of a MIDI event), each swarm a voice or instrument, and the whole system being considered as an improvising ensemble. Coherence is reached by self-organisation of particles and swarms.

  4. 4.

    Positive or negative contribution of the individual to the global fitness, i.e. the difference between the fitness of the population, when complete or deprived from this particular individual. This concept has been successfully used in various applications, see for instance [2]. In the absence of additional information at the local level for building a specific “local fitness”, marginal fitness is a convenient option.

  5. 5.

    Except for the largest instance (size 500) for which only 50 runs were done.

  6. 6.

    Reproducibility: code available at http://doi.org/10.5281/zenodo.7101160.

  7. 7.

    A synthetic scatterplot is also provided in https://evelyne-lutton.fr/Lutton_EA2022-Additional.pdf for assessing the balance between both measurements.

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Acknowledgements

We would like to thank Supercomputing Wales for the supercomputer used to generate all the experimental results (https://www.supercomputing.wales/).

Author information

Authors and Affiliations

  1. UMR 518 MIA, INRAE, Palaiseau, France

    É. Lutton & A. Tonda

  2. School of Computer Science and Electronic Engineering, Bangor University, Bangor, UK

    S. Al-Maliki & F. P. Vidal

  3. Computer Science Department, College of Science, Basrh University, Basra, Iraq

    S. Al-Maliki

  4. EFREI, Villejuif, France

    J. Louchet

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  1. É. Lutton
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Correspondence to É. Lutton .

Editor information

Editors and Affiliations

  1. University of Bordeaux, Talence, France

    Pierrick Legrand

  2. University of Lille, Lille, France

    Arnaud Liefooghe

  3. University of Exeter, Exeter, UK

    Edward Keedwell

  4. Université de Haute-Alsace, Mulhouse, France

    Julien Lepagnot

  5. Université de Haute Alsace, Mulhouse, France

    Lhassane Idoumghar

  6. Ecole Polytechnique de Université de Tours, Tours, France

    Nicolas Monmarché

  7. INRA, Thirverval-Grignon, France

    Evelyne Lutton

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Lutton, É., Al-Maliki, S., Louchet, J., Tonda, A., Vidal, F.P. (2023). Fine-Grained Cooperative Coevolution in a Single Population: Between Evolution and Swarm Intelligence. In: Legrand, P., et al. Artificial Evolution. EA 2022. Lecture Notes in Computer Science, vol 14091. Springer, Cham. https://doi.org/10.1007/978-3-031-42616-2_8

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