PICA: Multi-Population Implementation of Parallel Imperialist Competitive Algorithms

Amin Majd; Shahriar Lotfi; Golnaz Sahebi; Masoud Daneshtalab; Juha Plosila

doi:10.1109/PDP.2016.93

PICA: Multi-Population Implementation of Parallel Imperialist Competitive Algorithms

Amin Majd, Shahriar Lotfi, Golnaz Sahebi, Masoud Daneshtalab, Juha Plosila

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

10 Citations (Scopus)

Abstract

The importance of optimization and NP-problems solving cannot be over emphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary methods that are mostly sequential, and some others have parallel implementation. We propose a method to parallelize Imperialist Competitive Algorithm (Multi-Population). The algorithm has been implemented with MPI on two platforms and have tested our algorithms on a shared-memory and message passing architecture. An outstanding performance is obtained, which indicates that the method is efficient concern to speed and accuracy. In the second step, the proposed algorithm is compared with a set of existing well known parallel algorithms and is indicated that it obtains more accurate solutions in a lower time.

Original language	Undefined/Unknown
Title of host publication	2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)
Publisher	IEEE
Pages	–
DOIs	https://doi.org/10.1109/PDP.2016.93
Publication status	Published - 2016
MoE publication type	A4 Article in a conference publication
Event	Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) - 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) Duration: 17 Feb 2016 → 19 Feb 2019

Conference

Conference	Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)
Period	17/02/16 → 19/02/19

Access to Document

10.1109/PDP.2016.93

Cite this

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title = "PICA: Multi-Population Implementation of Parallel Imperialist Competitive Algorithms",

abstract = "The importance of optimization and NP-problems solving cannot be over emphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary methods that are mostly sequential, and some others have parallel implementation. We propose a method to parallelize Imperialist Competitive Algorithm (Multi-Population). The algorithm has been implemented with MPI on two platforms and have tested our algorithms on a shared-memory and message passing architecture. An outstanding performance is obtained, which indicates that the method is efficient concern to speed and accuracy. In the second step, the proposed algorithm is compared with a set of existing well known parallel algorithms and is indicated that it obtains more accurate solutions in a lower time.",

author = "Amin Majd and Shahriar Lotfi and Golnaz Sahebi and Masoud Daneshtalab and Juha Plosila",

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doi = "10.1109/PDP.2016.93",

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booktitle = "2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)",

publisher = "IEEE",

note = "Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) ; Conference date: 17-02-2016 Through 19-02-2019",

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Majd, A, Lotfi, S, Sahebi, G, Daneshtalab, M & Plosila, J 2016, PICA: Multi-Population Implementation of Parallel Imperialist Competitive Algorithms. in 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP). IEEE, pp. –, Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), 17/02/16. https://doi.org/10.1109/PDP.2016.93

PICA: Multi-Population Implementation of Parallel Imperialist Competitive Algorithms. / Majd, Amin; Lotfi, Shahriar; Sahebi, Golnaz et al.
2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP). IEEE, 2016. p. –.

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

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T1 - PICA: Multi-Population Implementation of Parallel Imperialist Competitive Algorithms

AU - Majd, Amin

AU - Lotfi, Shahriar

AU - Sahebi, Golnaz

AU - Daneshtalab, Masoud

AU - Plosila, Juha

PY - 2016

Y1 - 2016

N2 - The importance of optimization and NP-problems solving cannot be over emphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary methods that are mostly sequential, and some others have parallel implementation. We propose a method to parallelize Imperialist Competitive Algorithm (Multi-Population). The algorithm has been implemented with MPI on two platforms and have tested our algorithms on a shared-memory and message passing architecture. An outstanding performance is obtained, which indicates that the method is efficient concern to speed and accuracy. In the second step, the proposed algorithm is compared with a set of existing well known parallel algorithms and is indicated that it obtains more accurate solutions in a lower time.

AB - The importance of optimization and NP-problems solving cannot be over emphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary methods that are mostly sequential, and some others have parallel implementation. We propose a method to parallelize Imperialist Competitive Algorithm (Multi-Population). The algorithm has been implemented with MPI on two platforms and have tested our algorithms on a shared-memory and message passing architecture. An outstanding performance is obtained, which indicates that the method is efficient concern to speed and accuracy. In the second step, the proposed algorithm is compared with a set of existing well known parallel algorithms and is indicated that it obtains more accurate solutions in a lower time.

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