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An artificial ecosystem algorithm applied to the travelling salesman problem

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GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 155 - 156

https://doi.org/10.1145/2598394.2598438

Published: 12 July 2014 Publication History

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Abstract

An ecosystem inspired algorithm that aims to take advantage of highly distributed computer architectures is proposed. Our motivation is to grasp the phenomenal properties of ecosystems and use them for large-scale real-world problems. Just as an ecosystem comprises of many separate components that adapt together to form a single synergistic whole, the Artificial Ecosystem Algorithm (AEA) solves a problem by adapting subcomponents such that they fit together and form a single optimal solution. Typical biology inspired algorithms like GA, PSO, BCO, and ACO, represent candidate solutions as individuals in a population. However, AEA uses populations of solution components that are solved individually such that they combine to form the candidate solution. Like species in an ecosystem, AEA has different species that represent sub-parts of the solution, these species evolve and cooperate to form a complete solution.

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References

[1]

Divya Gupta. Solving tsp using various meta-heuristic algorithms. International Journal of Recent Contributions from Engineering, Science & IT (iJES), 1(2):pp--26, 2013.

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Jon Kleinberg. An impossibility theorem for clustering. Advances in neural information processing systems, pages 463--470, 2003.

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Gohar Vahdati, Mehdi Yaghoubi, Mahdieh Poostchi, and S. Naghibi. A new approach to solve traveling salesman problem using genetic algorithm based on heuristic crossover and mutation operator. In Soft Computing and Pattern Recognition, 2009. SOCPAR'09. International Conference of, pages 112--116. IEEE, 2009.

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Hatamlou A(2018)Solving travelling salesman problem using black hole algorithmSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-017-2760-y22:24(8167-8175)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s00500-017-2760-y
Ismkhan H(2017)Effective three-phase evolutionary algorithm to handle the large-scale colorful traveling salesman problemExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.09.02267:C(148-162)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.eswa.2016.09.022
Sourabh Joshi Sarabjit Kaur (2016)Ant Colony Optimization Meta-heuristic for Solving Real Travelling Salesman ProblemEmerging Research in Computing, Information, Communication and Applications10.1007/978-981-10-0287-8_5(55-63)Online publication date: 10-May-2016
https://doi.org/10.1007/978-981-10-0287-8_5

Index Terms

An artificial ecosystem algorithm applied to the travelling salesman problem
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
    2. Search methodologies
      1. Heuristic function construction

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GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2014

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

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

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Hatamlou A(2018)Solving travelling salesman problem using black hole algorithmSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-017-2760-y22:24(8167-8175)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s00500-017-2760-y
Ismkhan H(2017)Effective three-phase evolutionary algorithm to handle the large-scale colorful traveling salesman problemExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.09.02267:C(148-162)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.eswa.2016.09.022
Sourabh Joshi Sarabjit Kaur (2016)Ant Colony Optimization Meta-heuristic for Solving Real Travelling Salesman ProblemEmerging Research in Computing, Information, Communication and Applications10.1007/978-981-10-0287-8_5(55-63)Online publication date: 10-May-2016
https://doi.org/10.1007/978-981-10-0287-8_5

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Building a better heuristic for the traveling salesman problem: combining edge assembly crossover and partition crossover

Biogeography-Based Optimization for the Traveling Salesman Problems

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