Genetic Algorithm for Shortest Path in Ad Hoc Networks

Khankhour, Hala; Abouchabaka, Jâafar; Abdoun, Otman

doi:10.1007/978-3-030-33103-0_15

Cited by 8 publications

(9 citation statements)

References 6 publications

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“…The genetic algorithm is an evolutionary metaheuristic. It is an iterative algorithm (Figure 4) operating on populations of individuals compounds of chromosome vectors represented in integer, binary or real code [2]. These individuals will evolve from one generation to another to constitute a set of solutions for the problem.…”

Section: Figure 3: Mutation Operatormentioning

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: Figure 3: Mutation Operatormentioning

confidence: 99%

“…It has proved successful algorithms for solving problems of optimization of composite structures. Evolutionary algorithms have proved their adaptations to the constraints of problems and have been able to overcome local minima problems such as GA (Genetic Algorithm) [2], ACO (Ant Colony Optimization) [3] and TS (Tabu search) [4].…”

Section: Introductionmentioning

confidence: 99%

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

Majdoubi¹

2020

IJATCSE

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“…When conducting a simulation experiment, the minimum operating time of GA with the CPU-architecture processing 5,000 chromosomes was 1,397 ms, and the maximum was 18,311 ms: but, at the same time, in other cases, the running time of the algorithm did not exceed 3,100 ms. Therefore, let's consider the distribution of the duration of the algorithm in ms on the interval [1,200,3,200] in more detail.…”

Section: The Research Issuementioning

confidence: 99%

“…According to the data presented in Fig. 5, it can be argued that the set G * contains two spaces [1,400,1,599] and [1,600,1,799].…”

Section: The Research Issuementioning

confidence: 99%

“…The stochastic behaviour of bio-inspired algorithms makes it difficult to determine both effective algorithmic structures, and also the choice of hardware architecture with which the search procedure will be implemented. Typical representatives of bio-inspired approaches are genetic algorithms (GA), currently used to solve a wide range of optimization problems [1]. Herein after, the situation under consideration is analyzed in the context of GA using but with some modifications, it can be extended to a significant part of bio-inspired approaches.…”

Section: Introductionmentioning

confidence: 99%

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On the issue of fuzzy timing estimations of the algorithms running at GPU and CPU architectures

Agibalov¹,

Венцов

2019

E3S Web Conf.

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We consider the task of comparing fuzzy estimates of the execution parameters of genetic algorithms implemented at GPU (graphics processing unit’ GPU) and CPU (central processing unit) architectures. Fuzzy estimates are calculated based on the averaged dependencies of the genetic algorithms running time at GPU and CPU architectures from the number of individuals in the populations processed by the algorithm. The analysis of the averaged dependences of the genetic algorithms running time at GPU and CPU-architectures showed that it is possible to process 10’000 chromosomes at GPU-architecture or 5’000 chromosomes at CPUarchitecture by genetic algorithm in approximately 2’500 ms. The following is correct for the cases under consideration: “Genetic algorithms (GA) are performed in approximately 2, 500 ms (on average), ” and a sections of fuzzy sets, with a = 0.5, correspond to the intervals [2, 000.2399] for GA performed at the GPU-architecture, and [1, 400.1799] for GA performed at the CPU-architecture. Thereby, it can be said that in this case, the actual execution time of the algorithm at the GPU architecture deviates in a lesser extent from the average value than at the CPU.

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Optimization of the Ad Hoc Network by Using Hybridization of Genetic Algorithm with a Two-Optimization Algorithm

Khankhour

Abdoun

Abouchabaka

2021

Digital Technologies and Applications

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Genetic Algorithm for Shortest Path in Ad Hoc Networks

Cited by 8 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

On the issue of fuzzy timing estimations of the algorithms running at GPU and CPU architectures

Optimization of the Ad Hoc Network by Using Hybridization of Genetic Algorithm with a Two-Optimization Algorithm

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