Parallelized GA-PSO algorithm for solving Job Shop Scheduling Problem

Mudjihartono, Paulus; Jiamthapthaksin, Rachsuda; Tanprasert, Thitipong

doi:10.1109/icsitech.2016.7852616

Cited by 8 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hybrid algorithm between GA and PSO is proposed to overcome the drawbacks of these two classic intelligent algorithms. Three main types of hybrid algorithm are parallel algorithm [35][36][37], serial algorithm [38][39][40], and embedded algorithm [41][42][43]. The hybrid method, advantages, disadvantages and common application scenario of 3 types of hybrid algorithms are demonstrated in Table I.…”

Section: Survey On Hybrid Algorithmsmentioning

confidence: 99%

Digital Twin Power Grid Oriented Mobile Edge Network Resource Allocation Model

Han,

Chen,

et al. 2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

The integration of vast quantities of clean energy causes intense impact on the power grid reliability. Digital Twin can be used to handle the high‐growth data flow generated in the power grid, and thus accelerate its digitalization process to fulfill the clean energy consumption target. As the traditional central cloud network structure can no longer effectively support the operation of digital twin power grid, the mobile edge network can be used as a reasonable substitute. To support the information interaction process of the mobile edge network, considering total profit, time delay and energy consumption, this paper proposes the mobile edge network communication and computing resource joint allocation model. On the basis, the GA‐PSO algorithm is proposed to solve the resource allocation optimization function. The feasibility and effectiveness of the proposed model is verified by the related simulation experiment. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

show abstract

Section: Survey On Hybrid Algorithmsmentioning

confidence: 99%

Digital Twin Power Grid Oriented Mobile Edge Network Resource Allocation Model

Han,

Chen,

et al. 2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…Figure 14 is its pruning and grading result. e DSM representing the sequential constraint relationship between operations is shown in equation (9). For this case, the algorithm parameters were set as follows: the initial population size was 30, the maximum genetic algebra was 50, the crossover probability was 0.9, and the mutation probability was 0.1.…”

Section: Casementioning

confidence: 99%

“…Nagano [8] addressed the m-machine no-wait flow-shop scheduling problem with the objective of minimizing makespan subject to an upper bound on total completion time and proposed an iterated greedy with local search algorithm. Mudjihartono [9] proposed a GA-PSO algorithm, which implements it in both parallel and nonparallel modes, and compared to original GA, the GA-PSO gives a 4.58% better solution on average. Liu [10] proposed a multiobjective optimization model aimed at minimizing carbon footprints of all products and makespan and designed a hybrid fruit fly optimization algorithm to solve the proposed model.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Algorithm Based on Genetic Simulated Annealing Algorithm for Complex Multiproduct Scheduling Problem with Zero-Wait Constraint

Liang

Liu

Zhong

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Aiming at the complex multiproduct scheduling problem with 0-wait constraint, a hybrid algorithm based on genetic algorithm (GA) and simulated annealing (SA) algorithm was studied. Based on the results of pruning and grading to the operation tree of complex multiproduct, the design structure matrix (DSM) with precedence constraints was established. Then, an initial population coding method based on DSM was proposed and three strategies to optimize the initial population were proposed to improve the quality of the initial population for the situation of multiple operations in the same grade which need to be processed on the same machine. The specific process flow and the setting method of related parameters for the hybrid algorithm were given out. For the infeasible solution produced in the crossover operation, the repair method was proposed. In the decoding process with makespan as the optimization objective, the chromosome genes were classified and the decoding for complex multiproduct scheduling problem with 0-wait constraint was realized through the analysis of its characteristics. The effectiveness of the proposed algorithm for complex multiproduct scheduling problem with 0-wait constraint is verified by the test of related examples in the existing literature.

show abstract

“…In particular, the Particle Swarm Optimization (PSO) technique stands out among SI techniques due to its less control parameters, simple implementation, and excellent optimization ability. It has been widely used in various fields such as combinatorial optimization [10][11][12][13], image matching and enhancement [14,15], and data mining [16,17]. Besides, the PSO algorithm has been successfully applied to some routing problems with good results [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

Liu

Jing

Zhou

et al. 2023

Preprint

View full text Add to dashboard Cite

In the modern Very Large Scale Integration (VLSI) circuit design, the short-circuit problem is one of the key factors affecting routability. Due to the increased circuit size and net density, the short-circuit problem grows significantly in detailed routing. Furthermore, global routing ignores many problems in detailed routing, making for an increased mismatch between global routing and detailed routing. As a crucial intermediate phase between global routing and detailed routing, track assignment is an excellent phase to pre-process the short-circuit problem. However, existing track assignment algorithms face the problem of falling into local optimality. As one of the typical representatives of the swarm intelligence techniques, Particle Swarm Optimization (PSO) is a powerful tool to solve large-scale discrete problems. Therefore, we propose an effective Track Assignment Algorithm Based on Social Learning Discrete Particle Swarm Optimization (SLDPSO-TA). First, the proposed algorithm considers local nets to better guide detailed routing and an effective encoding method is designed to adapt the evolutionary algorithms better. Second, the social learning mode based on example pool mechanism is presented to improve the algorithm performance. Finally, a negotiation-based refining strategy is utilized to further reduce the overlap. Experimental results show that SLDPSO-TA can obtain the best overlap cost optimization among similar works, and reduce the congestion in key routing areas.

show abstract

Parallelized GA-PSO algorithm for solving Job Shop Scheduling Problem

Cited by 8 publications

References 7 publications

Digital Twin Power Grid Oriented Mobile Edge Network Resource Allocation Model

Digital Twin Power Grid Oriented Mobile Edge Network Resource Allocation Model

Hybrid Algorithm Based on Genetic Simulated Annealing Algorithm for Complex Multiproduct Scheduling Problem with Zero-Wait Constraint

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

Contact Info

Product

Resources

About