Hybrid Micro Genetic Multi-Population Algorithm With Collective Communication for the Job Shop Scheduling Problem

Cruz-Chávez, Marco Antonio; Rosales, Martin H. Cruz; Dı́az, José; Aguilar, José Alberto Hernández; León, Abelardo Rodríguez; Avelino, J. C. Prince; Ortiz, Martha Elena Luna; Salinas, Oscar H.

doi:10.1109/access.2019.2924218

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…GA is a parallel and efficient search method that adaptively optimizes the whole world by simulating the genetic and evolutionary process of organisms in the natural environment. Given that the GA has strong global search ability and the search process is very fast, it achieves good results in solving relatively complex path planning problems [33,34].…”

Section: A Algorithm Selectionmentioning

confidence: 99%

“…The mutation probability set in this paper is 0.01. When this probability is reached, two gene loci are randomly selected from the optimal individual after selection and crossover operation to achieve the mutation operation [33], as shown in Figure 1. In the operation process, if g G  , the operation will continue from step (3) until the individual with the maximum fitness and the optimal solution are output after the operation is terminated algebra [37].…”

Section: ) Cross Operationmentioning

confidence: 99%

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Research on e-Commerce Distribution Optimization of Rice Agricultural Products Based on Consumer Satisfaction

et al. 2021

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With the continuous development of agricultural product e-commerce platforms and the rapid growth of trading volume, conducting in-depth mining and analysis of online review data to improve consumer satisfaction is of considerable importance. This paper uses JingDong's self-run online reviews of rice agricultural products as the research object and analyzes the logistics factors that affect consumer satisfaction through text mining technology. A multi-objective model for logistics center distribution path optimization under a soft time window was constructed. The model used the results of online review analysis, namely, packaging integrity, delivery timeliness, and logistics cost, as the goals, and the model used ant colony algorithm (ACO) and genetic algorithm (GA) to solve the optimal distribution solution to minimize the penalty cost and transportation cost. Through examples to solve the optimal distribution vehicle number and shipping routes, in addition, a comparison of the two types of algorithm performance of the model under different node number indicated that the number of nodes affects algorithm performance. With a node number below 50, the ant ACO has high precision and a better distribution path. With a node number above 50, GA has more comprehensive performance. The average efficiency of the GA is 12.28% higher than that of ACO.

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Section: A Algorithm Selectionmentioning

confidence: 99%

Section: ) Cross Operationmentioning

confidence: 99%

Research on e-Commerce Distribution Optimization of Rice Agricultural Products Based on Consumer Satisfaction

et al. 2021

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“…Examples of cooperative MAMP-HLH include the work of Zhang et al [22], Cruz-Chávez et al [58], and Łapa et al [59]. Zhang et al [22] hybridized CSA [33] with DE [56] to solve constrained engineering problems which can find satisfactory global optima and avoid premature convergence.…”

Section: Figure 2 Population-based Meta-heuristic Algorithm Implement...mentioning

confidence: 99%

“…This work divides the population into two subgroups and adopts CSA and DE for these two subgroups independently. In another work, Cruz-Chávez et al [58] present a hybrid GA [37] with collective communication using distributed processing for the job shop scheduling problem. In this hybrid, diversification is performed by iterative SA [4] and the intensification of the search space is made through genetic approximation.…”

Section: Figure 2 Population-based Meta-heuristic Algorithm Implement...mentioning

confidence: 99%

Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Zamli

Kader

Azad

et al. 2021

Neural Comput & Applic

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Henry Gas Solubility Optimization Algorithm (HGSO) is a recently developed population-based metaheuristic algorithm in the literature. One notable feature of HGSO is that the algorithm divides its (single) population into a set of clusters that are individually mapped to an independent HGSO with its parameter settings (as well as its local best). At a glance, having multiple independent HGSO serving the given clusters in the population can definitely boost exploration (i.e., in terms of roaming the new potential region in the search space for better solution alternatives). However, a closer look reveals two main limitations. Firstly, HGSO-to-cluster mapping is statically defined. To be specific, the defined HGSO-to-cluster mapping does not consider its adaptive performance for the subsequent iteration. Secondly, HGSO implementation ignores the opportunity for hybridization with other meta-heuristic algorithms. With hybridization, one can compensate the limitation of a host algorithm with other algorithms' strength. Best results in the literature have often been associated with hybridization. Addressing these limitations, this paper proposes the development of Hybrid HGSO (HHGSO). Taking HGSO as the host algorithm, HHGSO is hybridized with four recently developed meta-heuristic algorithms, including Jaya Algorithm (JA), Sooty Tern Optimization Algorithm (STOA), Butterfly Optimization Algorithm (BOA) and Owl Search Algorithm (OSA). The individual mapping of each algorithm is made dynamic based on penalized and reward adaptive probability. Comparative performance of HHGSO with the aforementioned algorithms is conducted with a well-known Searchbased Software Engineering (SBSE) problem involving team formation problem. Additionally, the defined hybridization approach has also been adopted as a hybridization template for solving the combinatorial test generation problem with the same meta-heuristic algorithm combinations. Comparative performance is also undertaken against recently developed hyper-heuristic algorithms involving Exponential Monte Carlo with Counter, Modified Choice Function, Improvement Selection Rules, and Fuzzy Inference Selection. Our results indicate that the HHGSO hybridization has usefully improved the performance of the original HGSO and gives superior performance against the given competing algorithms.

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“…Pongchairerks [20] presented a two-level meta-heuristic method for solving JSSP, where the upper-level algorithm (UPLA) was a population-based algorithm that serves as an input-parameter controller of the lower-level algorithm (LOLA). Aiming at the JSSP, a collective communication hybrid genetic algorithm using distributed processing is proposed [21].…”

Section: Introductionmentioning

confidence: 99%

Improved Hybrid Heuristic Algorithm Inspired by Tissue-Like Membrane System to Solve Job Shop Scheduling Problem

Tian

Liu

2021

Processes

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In real industrial engineering, job shop scheduling problem (JSSP) is considered to be one of the most difficult and tricky non-deterministic polynomial-time (NP)-hard problems. This study proposes a new hybrid heuristic algorithm for solving JSSP inspired by the tissue-like membrane system. The framework of the proposed algorithm incorporates improved genetic algorithms (GA), modified rumor particle swarm optimization (PSO), and fine-grained local search methods (LSM). To effectively alleviate the premature convergence of GA, the improved GA uses adaptive crossover and mutation probabilities. Taking into account the improvement of the diversity of the population, the rumor PSO is discretized to interactively optimize the population. In addition, a local search operator incorporating critical path recognition is designed to enhance the local search ability of the population. Experiment with 24 benchmark instances show that the proposed algorithm outperforms other latest comparative algorithms, and hybrid optimization strategies that complement each other in performance can better break through the original limitations of the single meta-heuristic method.

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Hybrid Micro Genetic Multi-Population Algorithm With Collective Communication for the Job Shop Scheduling Problem

Cited by 11 publications

References 34 publications

Research on e-Commerce Distribution Optimization of Rice Agricultural Products Based on Consumer Satisfaction

Research on e-Commerce Distribution Optimization of Rice Agricultural Products Based on Consumer Satisfaction

Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Improved Hybrid Heuristic Algorithm Inspired by Tissue-Like Membrane System to Solve Job Shop Scheduling Problem

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