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Development of an Adaptive Genetic Algorithm to Optimize the Problem of Unequal Facility Location

Budi Hendrik Setia, Elveny Marischa, Zhuravlev Pavel, Jalil Abduladheem Turki, Al-Janabi Samaher, Alkaim Ayad F., Saleh Marwan Mahmood, Shichiyakh Rustem Adamovich, Sutarto

Foundations of Computing and Decision Sciences

2022

Vol. 47, No. 2

111--125

The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents an efficient method for optimizing the problem of unequal facility layouts. In this method, the genetic algorithm is improved and developed into an adaptive genetic algorithm. In this algorithm, the mutation operator is applied only when the similarity of chromosomes in each population reaches a certain level. This intelligence prevents jumps in situations where they are not needed and reduces computational time. In order to measure the performance of the proposed algorithm, its performance is compared with the performance of conventional genetic algorithms and refrigeration simulators. Computational results show that the adaptive genetic algorithm is able to achieve higher-quality solutions.

Design and development of 3-stage determination of damage location using Mamdani-Adaptive Genetic-Sugeno model

Sahu S., Kumar P. B., Parhi D. R.

Journal of Theoretical and Applied Mechanics

2017

Vol. 55 nr 4

1325--1339

Damage detection in structural elements like beams is one of important research areas for health monitoring. Initiation of a fault in the form of a crack or any damage puts a limitation on the service life of a structural member. So, in this paper, a method is proposed which uses the advantages of soft computing techniques like Fuzzy Inference Systems (Mamdani and Sugeno) and Adaptive Genetic Algorithm for three stage refinement of the data base generated using dynamic responses from a cracked fixed-free aluminum alloy beam element. For the crack element reference, a finite element model of a single transverse crack has been considered. The proposed method describes both Mamdani and Sugeno Fuzzy Inference Systems for training of damage parameters. In the Adaptive Genetic Algorithm, a statistics based method has been incorporated to limit the randomness of the search process. Finally, the results from the Mamdani-Adaptive Genetic-Sugeno model (MAS) are validated with the results from the experimental analysis.