Hybrid evolutionary optimization algorithm MPSO-SA

Hami, Norelislam El; Ellaia, Rachid; Itmi, Mhamed

doi:10.1051/ijsmdo/2010004

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…In this case, the change of velocity in the equation ( 1) is changed by introducing a new term in the formula. Proposed by [4], its illustration appears in Figure . 3 (see [5]).…”

Section: The Concept Of Neighborhoodmentioning

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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“…In this case, the change of velocity in the equation ( 1) is changed by introducing a new term in the formula. Proposed by [4], its illustration appears in Figure . 3 (see [5]).…”

Section: The Concept Of Neighborhoodmentioning

confidence: 99%

An evolutionary hybrid algorithm for complex optimization problems

Zemzami¹,

Elhami²,

Itmi³

et al. 2019

IJATCSE

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show abstract

“…The individual best position related with a particle i is the best position that the particle has visited. The best position of all particles in the swarm is represented by the vector X gbest [19,20]. The flowchart of the PSO algorithm is given in Figure 2.…”

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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“…These methods have shown their ability to tackle a wide range of complex real-world problems (Hakima et al, 2022;N. El Hami, 2012;N. El Hami et al, 2010;Reddad et al, 2022;Rhouas & El Hami, 2022;Zemzami et al, 2017Zemzami et al, , 2020, in diverse fields such as engineering design (Chien et al, 2021;Granados-Rojas et al, 2021;N.…”

Section: Introductionmentioning

confidence: 99%

Hybrid model of the FEM method and the SAR algorithm to perform reliability-based design optimization of the solder joints in the BGA assembly

Reddad,

Zemzami,

Hami

et al. 2024

Preprint

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Metaheuristic optimization algorithms are powerful for solving complex optimization problems, almost when we lack information about the nature of their objective function. Meanwhile, the Finite Element Method (FEM) is a very powerful method for predicting and simulating the behavior of systems subjected to various stresses, whether electrical, thermal, mechanical, vibratory or magnetic. In the current study, we have proposed an optimization methodology that couples a metaheuristic algorithm - the Search and Rescue Algorithm (SAR) - with the finite element method. We then investigated the proposed methodology for optimizing the reliability of solder balls with respect to their geometry in a Ball Grid Array (BGA) assembly subjected to thermal cycling load. First, we performed a sensitivity analysis to determine the parameters influencing solder ball reliability, then we optimized the plastic deformation of solder balls, hence increasing their reliability as a function of their geometry through the proposed methodology, which results in improving the reliability of the entire electronic component.

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Hybrid evolutionary optimization algorithm MPSO-SA

Cited by 9 publications

References 21 publications

An evolutionary hybrid algorithm for complex optimization problems

An evolutionary hybrid algorithm for complex optimization problems

Optimization in collaborative information systems for an enhanced interoperability network

Hybrid model of the FEM method and the SAR algorithm to perform reliability-based design optimization of the solder joints in the BGA assembly

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