Abstract
This paper presents a hybrid genetic algorithm with collective communication (HGACC) using distributed processing for the job shop scheduling problem. The genetic algorithm starts with a set of elite micro-populations created randomly, where the fitness of these individuals does not exceed a tuned upper bound in the makespan value. The computational processes distribute the micro-populations collectively. In the micro-populations, each individual’s search for good solutions is directed toward the solution space of the fittest individual, identified by an approximation of genetic traits. In each generation of the genetic algorithm, the best individual from each micro-population migrates to another micro-population to maintain diversity in populations. Changes in the genetic sequence are applied to each individual by the simulated annealing algorithm (iterative mutation). In this paper, the results obtained show that the genetic algorithm achieves excellent results, as compared to other genetic algorithms. It is also better than other non-genetic meta heuristics or competes with them.
Highlights
The Job Shop Scheduling Problem (JSSP) has a variety of applications in the manufacturing industry, one of which is found in the maquiladora industries
hybrid genetic algorithm with collective communication (HGACC) is evaluated only with square instances because they are more difficult to solve than the rectangular type [21]
HGACC is compared with twenty algorithms that are found in the literature, most of population
Summary
The Job Shop Scheduling Problem (JSSP) has a variety of applications in the manufacturing industry, one of which is found in the maquiladora industries. Cruz Chávez et al.: Hybrid Micro Genetic Multi-Population Algorithm With Collective Communication for the JSSP population, which helps local optimal solutions stand out They propose a local search operator that improves the performance of GA. In [6] present a simulation of a parallel quantum genetic algorithm model, in which they propose a migration scheme that applies a control strategy between universes (sub-populations) They apply a quantum crossover strategy, where they transfer fitness information of one or more individuals from universe j to universe i. In [11] present a hybrid island model genetic algorithm and proposes a naturally inspired self-adaptation phase strategy, which obtains a better balance between exploration and exploitation in the search within the solution space.
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