Multi-Population Parallel Imperialist Competitive Algorithm for Solving Systems of Nonlinear Equation

Amin Majd; Mahdi Abdollahi; Golnaz Sahebi; Davoud Abdollahi; Masoud Daneshtalab; Juha Plosila; Hannu Tenhunen

doi:10.1109/HPCSim.2016.7568412

Multi-Population Parallel Imperialist Competitive Algorithm for Solving Systems of Nonlinear Equation

Amin Majd, Mahdi Abdollahi, Golnaz Sahebi, Davoud Abdollahi, Masoud Daneshtalab, Juha Plosila, Hannu Tenhunen

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

6 Citations (Scopus)

Abstract

The widespread importance of optimization and solving NP-hard problems, like solving systems of nonlinear equations, is indisputable in a diverse range of sciences. Vast uses of non-linear equations are undeniable. Some of their applications are in economics, engineering, chemistry, mechanics, medicine, and robotics. There are different types of methods of solving the systems of nonlinear equations. One of the most popular of them is Evolutionary Computing (EC). This paper presents an evolutionary algorithm that is called Parallel Imperialist Competitive Algorithm (PICA) which is based on a multi-population technique for solving systems of nonlinear equations. In order to demonstrate the efficiency of the proposed approach, some well-known problems are utilized. The results indicate that the PICA has a high success and a quick convergence rate.

Original language	Undefined/Unknown
Title of host publication	2016 International Conference on High Performance Computing & Simulation (HPCS)
Publisher	IEEE
Pages	–
DOIs	https://doi.org/10.1109/HPCSim.2016.7568412
Publication status	Published - 2016
MoE publication type	A4 Article in a conference publication
Event	International Conference on High Performance Computing & Simulation (HPCS) - 2016 International Conference on High Performance Computing & Simulation (HPCS) Duration: 18 May 2016 → 22 Jul 2016

Conference

Conference	International Conference on High Performance Computing & Simulation (HPCS)
Period	18/05/16 → 22/07/16

Access to Document

10.1109/HPCSim.2016.7568412

https://ieeexplore.ieee.org/document/7568412

Cite this

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title = "Multi-Population Parallel Imperialist Competitive Algorithm for Solving Systems of Nonlinear Equation",

abstract = "The widespread importance of optimization and solving NP-hard problems, like solving systems of nonlinear equations, is indisputable in a diverse range of sciences. Vast uses of non-linear equations are undeniable. Some of their applications are in economics, engineering, chemistry, mechanics, medicine, and robotics. There are different types of methods of solving the systems of nonlinear equations. One of the most popular of them is Evolutionary Computing (EC). This paper presents an evolutionary algorithm that is called Parallel Imperialist Competitive Algorithm (PICA) which is based on a multi-population technique for solving systems of nonlinear equations. In order to demonstrate the efficiency of the proposed approach, some well-known problems are utilized. The results indicate that the PICA has a high success and a quick convergence rate.",

author = "Amin Majd and Mahdi Abdollahi and Golnaz Sahebi and Davoud Abdollahi and Masoud Daneshtalab and Juha Plosila and Hannu Tenhunen",

year = "2016",

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publisher = "IEEE",

note = "International Conference on High Performance Computing & Simulation (HPCS) ; Conference date: 18-05-2016 Through 22-07-2016",

}

Majd, A, Abdollahi, M, Sahebi, G, Abdollahi, D, Daneshtalab, M, Plosila, J & Tenhunen, H 2016, Multi-Population Parallel Imperialist Competitive Algorithm for Solving Systems of Nonlinear Equation. in 2016 International Conference on High Performance Computing & Simulation (HPCS). IEEE, pp. –, International Conference on High Performance Computing & Simulation (HPCS), 18/05/16. https://doi.org/10.1109/HPCSim.2016.7568412

Multi-Population Parallel Imperialist Competitive Algorithm for Solving Systems of Nonlinear Equation. / Majd, Amin; Abdollahi, Mahdi; Sahebi, Golnaz et al.
2016 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2016. p. –.

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

TY - GEN

T1 - Multi-Population Parallel Imperialist Competitive Algorithm for Solving Systems of Nonlinear Equation

AU - Majd, Amin

AU - Abdollahi, Mahdi

AU - Sahebi, Golnaz

AU - Abdollahi, Davoud

AU - Daneshtalab, Masoud

AU - Plosila, Juha

AU - Tenhunen, Hannu

PY - 2016

Y1 - 2016

N2 - The widespread importance of optimization and solving NP-hard problems, like solving systems of nonlinear equations, is indisputable in a diverse range of sciences. Vast uses of non-linear equations are undeniable. Some of their applications are in economics, engineering, chemistry, mechanics, medicine, and robotics. There are different types of methods of solving the systems of nonlinear equations. One of the most popular of them is Evolutionary Computing (EC). This paper presents an evolutionary algorithm that is called Parallel Imperialist Competitive Algorithm (PICA) which is based on a multi-population technique for solving systems of nonlinear equations. In order to demonstrate the efficiency of the proposed approach, some well-known problems are utilized. The results indicate that the PICA has a high success and a quick convergence rate.

AB - The widespread importance of optimization and solving NP-hard problems, like solving systems of nonlinear equations, is indisputable in a diverse range of sciences. Vast uses of non-linear equations are undeniable. Some of their applications are in economics, engineering, chemistry, mechanics, medicine, and robotics. There are different types of methods of solving the systems of nonlinear equations. One of the most popular of them is Evolutionary Computing (EC). This paper presents an evolutionary algorithm that is called Parallel Imperialist Competitive Algorithm (PICA) which is based on a multi-population technique for solving systems of nonlinear equations. In order to demonstrate the efficiency of the proposed approach, some well-known problems are utilized. The results indicate that the PICA has a high success and a quick convergence rate.

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