A new parallel approach for the exploitation of the search space based on PSO algorithm

Zemzami, Maria; Elhami, Norelislam; Itmi, Mhamed; Hmina, Nabil

doi:10.1109/cist.2016.7805024

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…1. The particle movement by basic PSO (proposed by [7]). Although the PSO meta-heuristic is declared one of the most efficient methods in the field of optimization, it has some disadvantages, namely, premature convergence and high running time.…”

Section: The Proposed Parallel Modelsmentioning

confidence: 99%

“…For more details about this model, the reader is referred to [7]. The proposed NPM algorithm is described in the figure below (see Fig.…”

Section: Multi-pso Parallel Model (Mpm)mentioning

confidence: 99%

“…In fact, MPM model is efficient and gives good results, especially for small size optimization problems with many local optima, given that the whole search space is explored. The obtained results are detailed in [7]. However, this model is not adequate for large optimization problems (high dimension), because the division of the global search space and the creation of the threads is costly in terms of calculation time.…”

Section: Mpm Model Analysismentioning

confidence: 99%

See 2 more Smart Citations

A comparative study of three new parallel models based on the PSO algorithm

Zemzami

Hami

Itmi

et al. 2020

Int. J. Simul. Multidisci. Des. Optim.

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Meta-heuristic PSO has limits, such as premature convergence and high running time, especially for complex optimization problems. In this paper, a description of three parallel models based on the PSO algorithm is developed, on the basis of combining two concepts: parallelism and neighborhood, which are designed according to three different approaches in order to avoid the two disadvantages of the PSO algorithm. The third model, SPM (Spherical-neighborhood Parallel Model), is designed to improve the obtained results from the two parallel NPM (Neighborhood Parallel Model) and MPM (Multi-PSO Parallel Model) models. The experimental results presented in this paper show that SPM model performed much better than both NPM and MPM models in terms of computing time and solution quality.

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Section: The Proposed Parallel Modelsmentioning

confidence: 99%

“…For more details about this model, the reader is referred to [7]. The proposed NPM algorithm is described in the figure below (see Fig.…”

Section: Multi-pso Parallel Model (Mpm)mentioning

confidence: 99%

Section: Mpm Model Analysismentioning

confidence: 99%

See 1 more Smart Citation

A comparative study of three new parallel models based on the PSO algorithm

Zemzami

Hami

Itmi

et al. 2020

Int. J. Simul. Multidisci. Des. Optim.

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“…According to the author of [21], the use of Dynamic Neighborhood makes it possible to better explore and exploit the search space by updating neighborhoods at each iteration of the algorithm. For example: a particle i which has place with a group N5 at the iteration N115 is declared best of its group; then at the iteration N116, this particle i has a place with another group N9 and offers its information with its new group: this segment improves the movement of its new group and speed up the movement of the other particles towards the optimum.…”

Section: Parallel Particle Swarm Optimizationmentioning

confidence: 99%

Optimization in collaborative information systems for an enhanced interoperability network

Koulou

Zemzami

Hami

et al. 2020

Int. J. Simul. Multidisci. Des. Optim.

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Today's constraints on collaborative enterprises are often directly reflected in the interoperability of their information systems. The goal is to improve and effectively evolve the global interoperability of collaboration by distributing the required effort in an optimal way on different information systems to achieve the target goal. This method of enhancement will be realized by applying an IMA (Interoperability Measurement Approach) to generate a ratio representing the degree of interoperability of an information system within the collaboration, as well as applying the heuristic optimization method PPSO (Paralleled Particle Swarm Optimization) to reach the optimal distribution of effort.

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“…From here comes the idea of parallelization for PSO to improve its performance. Several researchers in the field of metaheuristics have been interested in the concept of parallelization, different parallel PSO models have been proposed [8]- [12], for our parallel implementation allowing the parallelization of calculations. Indeed, threads, kind of processes perform calculations in parallel on sets of particles situated in several neighborhoods.…”

Section: The Proposed Hybrid Approachmentioning

confidence: 99%

An evolutionary hybrid algorithm for complex optimization problems

Zemzami¹,

Elhami²,

Itmi³

et al. 2019

IJATCSE

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This paper describes an evolutionary hybrid model linking two algorithms: Particle Swarm Optimization (PSO) and Simulated Annealing (SA). The basic idea behind using a hybrid model is improving the reliability of the obtained results from our first model, namely MPSO (Modified PSO) based on PSO algorithm, by adding SA algorithm which is quite popular for its powerful feature of effective escaping from the trap of local minima. MPSO model uses the concept of evolutionary neighborhoods associated to parallel computation, to overcome to the two essential disadvantages of PSO: high running time and premature convergence. The presented algorithm has two essential operations: first running PSO algorithm in parallel using the new concept of evolutionary neighborhood to obtain a global best solution, then improving the results with SA algorithm to get the global optimal solution. By testing this hybrid algorithm (H-MPSO-SA) on a set of standard benchmark functions and according to the obtained results, the program have given satisfactory results of the hybrid model compared to the basic PSO and MPSO algorithms.

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A new parallel approach for the exploitation of the search space based on PSO algorithm

Cited by 9 publications

References 22 publications

A comparative study of three new parallel models based on the PSO algorithm

A comparative study of three new parallel models based on the PSO algorithm

Optimization in collaborative information systems for an enhanced interoperability network

An evolutionary hybrid algorithm for complex optimization problems

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