PICA: Multi-population Implementation of Parallel Imperialist Competitive Algorithms

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

doi:10.1109/pdp.2016.93

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…In this paper, a parallel implementation of the ICA (PICA – Parallel ICA) is proposed. We extend our previous works (see the works of Majd et al()) by presenting two different parallel implementations of PICA (multi‐population and master‐slave) to improve the speed and accuracy. The results are extensively analyzed with eight benchmarks and three case studies.…”

Section: Introductionmentioning

confidence: 87%

“…The migration interval varies from a run to another. All migrations between the processors take place synchronously (simultaneously), and, therefore, the numbers of the countries in all processors are equal after each migration event . The behavior of the multi‐population PICA is illustrated in Figure , and the pseudocode is presented in Figure .…”

Section: Proposed Methodsmentioning

confidence: 99%

“…All migrations between the processors take place synchronously (simultaneously), and, therefore, the numbers of the countries in all processors are equal after each migration event. 9 The behavior of the multi-population PICA is illustrated in Figure 3, and the pseudocode is presented in Figure 4. The sparse connected ring topology enables a low migration rate due to the simple routing scheme and the short (single-hop) migration distance.…”

Section: Multi-population Picamentioning

confidence: 99%

“…Among the EC methods, the Imperialist Competitive Algorithm (ICA) is an efficient approach for continuous problems. In this paper, a parallel implementation of the ICA (PICA – Parallel ICA) is proposed.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Parallel imperialist competitive algorithms

Majd

Sahebi

Daneshtalab

et al. 2018

Concurrency and Computation

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Summary The importance of optimization and NP‐problem solving cannot be overemphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary methods; they are mostly sequential but some of them have parallel implementations as well. We propose a multi‐population method to parallelize the Imperialist Competitive Algorithm. The algorithm has been implemented with the Message Passing Interface on 2 computer platforms, and we have tested our method based on shared memory and message passing architectural models. An outstanding performance is obtained, demonstrating that the proposed method is very efficient concerning both speed and accuracy. In addition, compared with a set of existing well‐known parallel algorithms, our approach obtains more accurate results within a shorter time period.

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Section: Introductionmentioning

confidence: 87%

Section: Proposed Methodsmentioning

confidence: 99%

Section: Multi-population Picamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parallel imperialist competitive algorithms

Majd

Sahebi

Daneshtalab

et al. 2018

Concurrency and Computation

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“…10 By simulating the self-organizing pattern of bee colonies, Karaboga and Basturk proposed artificial bee colony (ABC) algorithm. 5 In addition, there are such optimization algorithms as invasive weed optimization (IWO) proposed by Mehrabian and Lucas 11 and fireworks algorithm (FWA) proposed by Tan and Zhu, 12 bat algorithm (BA) proposed by Yang 13 and imperialist competitive algorithms (ICA) proposed by Majd et al 14 Garden balsam is an annual herbaceous flower also known as ''Touch me not'', because its fruit will crack at slight touch and eject seeds.…”

Section: Introductionmentioning

confidence: 99%

Garden balsam optimization algorithm

Sun

2019

Concurrency and Computation

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Summary This paper introduces a method called garden balsam optimization algorithm (GBO) for optimization problems. Garden balsam randomly ejects the seeds within a certain range by virtue of mechanical force originating from cracking of mature seed pods. The seeds scattered to suitable growth area will have greater reproductive capacity in the next generation, followed by iteration until the most suitable point for growth in a particular space is eventually found. Mimicking the transmission of garden balsam seeds, GBO generates the seeds by the mechanical transmission and second transmission operator to search the global optimum in the problem space. The convergence of GBO is tested in details through a set of benchmark multi‐dimensional functions. Secondly, the effect of tuning parameters on algorithm performance is studied. Finally, several typical problems were selected in the standard test set to test the performance of GBO. The experimental results show that the algorithm has advantages in optimization problems. In conclusion, the performance of GBO has a reasonable performance for all the test functions.

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