Self-Optimizing A Multi-Agent Scheduling System: A Racing Based Approach

Pereira, Ivo; Madureira, Ana

doi:10.1007/978-3-319-25017-5_26

Cited by 3 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taguchi experiments search for the control inputs that allow the meta-heuristic to withstand the uncontrollable variation of the noise inputs. Other parametrization by DOE techniques such as F-Race and Sequential Parameter Optimization (SPO), can be found in [51][52][53][54][55][56][57][58][59][60][61][62][63][64].…”

Section: Parametrization By Doementioning

confidence: 99%

“…It is clear the proposed framework outperformed the conventionally parametrized DABC. In order to conclude about the performance of the self-parameterization framework, when compared with the conventionally parametrized DABC, a one-way ANOVA (analysis of variance) was used [61]. μMin, μMax and μMed are the mean of the relative deviation from the optimum from the self-parameterization framework and μDABC is the mean of the relative deviation from conventionally parametrized DABC.…”

Section: Twt Problemmentioning

confidence: 99%

“…Shapiro-Wilk for the TSP.Since it was not possible to demonstrate that the sample does not come from a normal distributed, the self-parametrization framework and conventionally parametrized DABC will be compared with the Student's. It compares the means of normal distributions samples[61]. Student's t-test hypotheses are:…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Self-Parametrization Framework for Meta-Heuristics

2022

View full text Add to dashboard Cite

Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are time-consuming, requiring considerable computational effort and they do not take advantage of the meta-heuristics that they parameterize. In order to approach the parameterization of meta-heuristics, in this paper, a self-parameterization framework is proposed. It will automatize the parameterization as an optimization problem, precluding the user from spending too much time on parameterization. The model will automate the parameterization through two meta-heuristics: A meta-heuristic of the solution space and one of the parameter space. To analyze the performance of the framework, a self-parameterization prototype was implemented. The prototype was compared and analyzed in a SP (scheduling problem) and in the TSP (traveling salesman problem). In the SP, the prototype found better solutions than those of the manually parameterized meta-heuristics, although the differences were not statistically significant. In the TSP, the self-parameterization prototype was more effective than the manually parameterized meta-heuristics, this time with statistically significant differences.

show abstract

Section: Parametrization By Doementioning

confidence: 99%

Section: Twt Problemmentioning

confidence: 99%

See 1 more Smart Citation

A Self-Parametrization Framework for Meta-Heuristics

2022

View full text Add to dashboard Cite

show abstract

Research on Multi-Agent based Optimization in Smart Production Line

Zhiyao

Zhang

2020

2020 IEEE 6th International Conference on Computer and Communications (ICCC)

View full text Add to dashboard Cite

A Hybrid Metaheuristics Parameter Tuning Approach for Scheduling through Racing and Case-Based Reasoning

et al. 2021

View full text Add to dashboard Cite

In real manufacturing environments, scheduling can be defined as the problem of effectively and efficiently assigning tasks to specific resources. Metaheuristics are often used to obtain near-optimal solutions in an efficient way. The parameter tuning of metaheuristics allows flexibility and leads to robust results, but requires careful specifications. The a priori definition of parameter values is complex, depending on the problem instances and resources. This paper implements a novel approach to the automatic specification of metaheuristic parameters, for solving the scheduling problem. This novel approach incorporates two learning techniques, namely, racing and case-based reasoning (CBR), to provide the system with the ability to learn from previous cases. In order to evaluate the contributions of the proposed approach, a computational study was performed, focusing on comparing our results previous published results. All results were validated by analyzing the statistical significance, allowing us to conclude the statistically significant advantage of the use of the novel proposed approach.

show abstract

Self-Optimizing A Multi-Agent Scheduling System: A Racing Based Approach

Cited by 3 publications

References 10 publications

A Self-Parametrization Framework for Meta-Heuristics

A Self-Parametrization Framework for Meta-Heuristics

Research on Multi-Agent based Optimization in Smart Production Line

A Hybrid Metaheuristics Parameter Tuning Approach for Scheduling through Racing and Case-Based Reasoning

Contact Info

Product

Resources

About