Investigating parallel genetic algorithms on job shop scheduling problems

Lin, Shyh-Chang; Goodman, Erik D.; Punch, William F.

doi:10.1007/bfb0014827

Cited by 65 publications

(51 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The model shown in Fig. 6(d) was initially used by [63] and has received some attention in works like [7], [8], [56], and [79]. In this model, the basic islands perform a cEA, thus trying to get the combined advantages of the two models.…”

Section: Parallel Implementations Of Easmentioning

confidence: 99%

Parallelism and evolutionary algorithms

Alba

Tomassini

2002

IEEE Trans. Evol. Computat.

696

318

View full text Add to dashboard Cite

Abstract-This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: first, the different families of EAs have naturally converged in the last decade while parallel EAs (PEAs) seem still to lack unified studies, and second, there is a large number of improvements in these algorithms and in their parallelization that raise the need for a comprehensive survey. We stress the differences between the EA model and its parallel implementation throughout the paper. We discuss the advantages and drawbacks of PEAs. Also, successful applications are mentioned and open problems are identified. We propose potential solutions to these problems and classify the different ways in which recent results in theory and practice are helping to solve them. Finally, we provide a highly structured background relating PEAs in order to make researchers aware of the benefits of decentralizing and parallelizing an EA.

show abstract

Section: Parallel Implementations Of Easmentioning

confidence: 99%

Parallelism and evolutionary algorithms

Alba

Tomassini

2002

IEEE Trans. Evol. Computat.

696

318

View full text Add to dashboard Cite

show abstract

“…However, this baseline is obtained without any anticipation about the uncertainties. The repairing or revising process is usually repetitive and is considered as a local search process [31], [45]. Since this approach is based on existence of the baseline solution, some researchers have labeled it as 'predictivereactive'.…”

Section: B Adaptationmentioning

confidence: 99%

Adaptation in Dynamic Environments: A Case Study in Mission Planning

Bui

Michalewicz

Parkinson

et al. 2012

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

Abstract-Many random events usually are associated with executions of operational plans at various companies and organizations. For example, some tasks might be delayed and/or executed earlier. Some operational constraints can be introduced due to new regulations or business rules. In some cases, there might be a shift in the relative importance of objectives associated with these plans. All these potential modifications create a huge pressure on planning staff for generating plans that can adapt quickly to changes in environment during execution. In this paper we address adaptation in dynamic environments.Many researchers in evolutionary community addressed the problem of optimization in dynamic environments. Through an overview on applying evolutionary algorithms for solving dynamic optimization problems, we classify the work into two main categories: (1) finding/tracking optima and (2) adaptation and we discuss their relevance for solving planning problems. Based on this discussion, we propose a computational approach to adaptation within the context of planning. This approach models the dynamic planning problem as a multi-objective optimization problem and an evolutionary mechanism is incorporated, this adapts the current solution to new situations when a change occurs.As the multi-objective model is used, the proposed approach produces a set of non-dominated solutions after each planning cycle. This set of solutions can be perceived as an informationrich data set which can be used to support the adaptation process against the effect of changes. The main question is how to exploit this set efficiently? In this paper we propose a method based on the concept of centroids over a number of changing-time steps, at each step we obtain a set of non-dominated solutions.We carried out a case study on this proposed approach. Mission planning was used for our experiments and experimental analysis. We selected mission planning as our test environment because battlefields are always highly dynamic and uncertain and can be conveniently used to demonstrate different types of changes, especially time-varying constraints. The obtained results support the significance of our centroid-based approach.

show abstract

“…There are not many researchers who pay attention to migration strategies, even though they agree that improvement can be achieved if a different migration strategy is applied to their algorithm [10]. One researcher who has done a study specific to migration strategy is Cao Yijia [11].…”

Section: Introductionmentioning

confidence: 99%

An Investigation on the Effect of Migration Strategy on Parallel GA-Based Shortest Path Routing Algorithm

Yussof¹,

Razali²

2012

View full text Add to dashboard Cite

Genetic algorithm (GA) is one of the alternative approaches for solving the shortest path routing problem. In previous work, we have developed a coarse-grained parallel GA-based shortest path routing algorithm. With parallel GA, there is a GA operator called migration, where a chromosome is taken from one sub-population to replace a chromosome in another sub-population. Which chromosome to be taken and replaced is subjected to the migration strategy used. There are four different migration strategies that can be employed: best replace worst, best replace random, random replace worst, and random replace random. In this paper, we are going to evaluate the effect of different migration strategies on the parallel GA-based routing algorithm that has been developed in the previous work. Theoretically, the migration strategy best replace worst should perform better than the other strategies. However, result from simulation shows that even though the migration strategy best replace worst performs better most of the time, there are situations when one of the other strategies can perform just as well, or sometimes better.

show abstract

Investigating parallel genetic algorithms on job shop scheduling problems

Cited by 65 publications

References 10 publications

Parallelism and evolutionary algorithms

Parallelism and evolutionary algorithms

Adaptation in Dynamic Environments: A Case Study in Mission Planning

An Investigation on the Effect of Migration Strategy on Parallel GA-Based Shortest Path Routing Algorithm

Contact Info

Product

Resources

About