The Immigration Genetic Approach to Improve the Optimization of Constrained Assignment Problem of Human Resources

Tkatek, Said; Abdoun, Otman; Abouchabaka, Jâafar; Rafalia, Najat

doi:10.1007/978-3-030-11928-7_60

Cited by 3 publications

(8 citation statements)

References 6 publications

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“…The buckling and failure load factors values for 48 ply laminate and 64 ply laminate are presented in Table 2. The obtained results were compared with the results gained from [6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Bucking Load Optimization Resultsmentioning

confidence: 99%

“…Populations will be sorted according to fitness values. The SIG consists of inserting an immigration operator based on the creation of a random population and crossing the population concluded since the uniform evolution and the randomly created population [15]. A mutation operator is also used by replacing a sub-vector of the chromosome with another randomly including gene values from the set of chromosome genes [23].…”

Section: Figure 3: Mutation Operatormentioning

confidence: 99%

“…As for the AIG, it consists of operating on immigration populations resulting from the selection operations by the roulette wheel selection. It consists of choosing three best individuals to conclude 60% since the first, 10% since the second and 30% since the third individual in order to create random vectors [15]. It implicates combining the 20% of random individuals from the roulette wheel selection with the best solutions concluded from the elitist selection by tournament.…”

Section: Figure 3: Mutation Operatormentioning

confidence: 99%

“…This work proposes a new optimization approach called Parallel Genetic Immigration. The proposed approach is based on the new genetic immigration operator [15] and on the Multi-Processor parallel calculation method [4] to optimize the problem of stratified structures. The resolution is thus made by the integration of an immigration operator based on the random selection and an immigration operator via the roulette wheel selection.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Approach for Multi-Objective Design Optimization of Composite Structures: Parallel Genetic Immigration

Majdoubi¹

2020

IJATCSE

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An isotropic mechanical structure cannot withstand shocks, accidents, and mechanical loading. In this perspective, composite structures have been introduced to improve the performance of mechanical structures. We are interested in the study of laminates with the aim to find an optimal structure resistant to vibrations and buckling. To remedy the problem of vibrations and buckling, we use artificial intelligence methods to optimize the design of composite structures. As it is a typical method of artificial intelligence adapted to this studied problem, we use genetic algorithms based on a new genetic operator. In the present article, an optimization procedure based on the new genetic operator called genetic immigration operator is developed to determine the maximum buckling load and fundamental frequency of the laminated plate with plies oriented at -45•/45• ,0•, and 90•. The aim of this paper is the use of two different methods for their effectiveness. These optimization works consist of first maximizing the buckling load factor with UGA (Uniform Genetic Algorithm) and a new evolutionary search strategies called Immigration Genetic: SIG (Standard Immigration Genetic Algorithm) and AIG (Improved Genetic Immigration Algorithm) and second of solving a multi-objective problem with minimizing the cost and weight of the hybrid laminate made of the fibers of two composite materials. The resolution of this problem by the proposed genetic immigration approach is reinforced by the optimization of the CPU computation time which is due to the exploitation of the parallel architecture based on the multi-processor parallel computation.

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Section: Bucking Load Optimization Resultsmentioning

confidence: 99%

Section: Figure 3: Mutation Operatormentioning

confidence: 99%

Section: Figure 3: Mutation Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Approach for Multi-Objective Design Optimization of Composite Structures: Parallel Genetic Immigration

Majdoubi¹

2020

IJATCSE

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“…For a long time, parallelism has been used in computer science to solve major scientific problems related to a number of fields that are modeled on the problem of crunch: economics, meteorology, and bioinformatics) in order to be able to generate solutions more quickly [24,25]. Even with the use of GAs as one of the metaheuristic methods, one of these complex problems like the knapsack problem needs huge computational capacities as well as time to solve.…”

Section: Parallel Genetic Algorithm Architecture For Intelligent Systemmentioning

confidence: 99%

Intelligent system for recruitment decision making using an alternative parallel-sequential genetic algorithm

Tkatek

Bahti

Abdoun

et al. 2021

IJEECS

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<p>The human resources (HR) manager needs effective tools to be able to move away from traditional recruitment processes to make the good decision to select the good candidates for the good posts. To do this, we deliver an intelligent recruitment decision-making method for HR, incorporating a recruitment model based on the multipack model known as the NP-hard model. The system, which is a decision support tool, often integrates a genetic approach that operates alternately in parallel and sequentially. This approach will provide the best recruiting solution to allow HR managers to make the right decision to ensure the best possible compatibility with the desired objectives. Operationally, this system can also predict the altered choice of parallel genetic algorithm (PGA) or sequential genetic algorithm (SeqGA) depending on the size of the instance and constraints of the recruiting posts to produce the quality solution in a reduced CPU time for recruiting decision-making. The results obtained in various tests confirm the performance of this intelligent system which can be used as a decision support tool for intelligently optimized recruitment.</p>

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A Hybrid Genetic Algorithms and Sequential Simulated Annealing for a Constrained Personal Reassignment Problem to Preferred Posts

Tkatek.¹

2020

IJATCSE

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This paper discusses the implementation of a new hybrid algorithm to efficacy solve a constrained personnel reassignment to preferred posts by generating a high quality and optimal solution. This optimization of posts can be occupied by a qualified employee during a job rotation or redeployment (reassignment) staff operation. This operation is organized by a decision-maker to adapt each post in priority to qualified employees for improving the productivity of enterprise. Generally, this problem is a NP hard problem that has been modeled into a combinatorial optimization problem. Next we have developed a hybrid graph Flow and genetic algorithm called HFGA, to solve it [1]. However, we have noticed that the stagnation phenomenon of this HFGA algorithm is usually persisted producing an increase in the number of iterations and consequently also a significant consummation of CPU time. In order to remedy this problem and improving in parallel the optimal solution and convergence rate, we propose as our first contribution a hybrid genetic HFGA and Adaptive Genetic Immigration AIG operator [2] called HFGA-AIG. In our second contribution, we propose another hybridization of HFGA-AIG with a Simulated Annealing SA sequentially called Hybrid Genetic Simulated Annealing Algorithms HFGA-SA-AIG. Our aim is to obtain an improved solution quality of this NP Hard problem. This proposed algorithm can be adapted to a personnel assignment optimization model and allows us also to ensure an excellent allocation of resources for maximizing the productivity in a multisite entreprise. The obtained numerical results indicate that the HFGA-SA-AIG algorithm outperforms both HFGA-AIG and HFGA. In terms of solution quality and remedying of the stagnation phenomenon.

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The Immigration Genetic Approach to Improve the Optimization of Constrained Assignment Problem of Human Resources

Cited by 3 publications

References 6 publications

Artificial Intelligence Approach for Multi-Objective Design Optimization of Composite Structures: Parallel Genetic Immigration

Artificial Intelligence Approach for Multi-Objective Design Optimization of Composite Structures: Parallel Genetic Immigration

Intelligent system for recruitment decision making using an alternative parallel-sequential genetic algorithm

A Hybrid Genetic Algorithms and Sequential Simulated Annealing for a Constrained Personal Reassignment Problem to Preferred Posts

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