A Memetic Cooperative Optimization Schema and Its Application to the Tool Switching Problem

Amaya, Jhon Edgar; Cotta, Carlos; Leiva, Antonio José Fernández

doi:10.1007/978-3-642-15844-5_45

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Only the results from the cooperative SDI technique seemed promising, but they were still inferior to the MA plus HC proposed by Amaya, Cotta, and Fernández (2008). At about the same time, Amaya, Cotta, and Leiva (2010a) and Amaya, Cotta, and Fernández-Leiva (2011) extend the above mentioned cooperative scheme and combine the features of memetic agent models supported by different LS mechanisms, namely HC and TS. The results show that especially heterogeneous memetic agents that exchange current best solutions outperform individual agents.…”

Section: The Uniform Sspmentioning

confidence: 95%

The job sequencing and tool switching problem: state-of-the-art literature review, classification, and trends

Calmels

2018

International Journal of Production Research

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The job sequencing and tool switching problem is a combinatorial optimisation problem that appears in various industries, mainly in the manufacturing sector. Although tool switching is only part of a much larger decision-making process in manufacturing systems, it has a major impact on the overall performance of the system by affecting the total set-up time as well as the machine and tool utilisation. Over the past few decades, various approaches have been applied to tool switching problems. This study provides a comprehensive review of the literature on the job sequencing and tool switching problem. Studies are identified and compared through a structured literature review and classified according to a new framework which allows the identification of problem perspectives and solution methods. The results show that current research does not fully exploit real-world situations and studies are often limited to a single-machine and uniform tool size and set-up time. Finally, this literature review summarises the current research results and provides directions for future research.. comparatively review, in an organised fashion, the existing unclassified tool switching literature with respect to problem formulation development and applied solution methods, . develop a classification scheme that may help to identify aspects of the tool switching problem that have been insufficiently explored and show how far the existing modelling efforts have progressed towards real-world needs, . synthesise the past trends of tool switching studies, and . offer some suggestions and directions for future research.The remainder of this paper is structured as follows. Section 2 introduces the generic tool switching problem, its subproblems, and related optimisation problems. Subsequently, the research approach is described in section 3, and the classification framework is described in section 4. The literature review is presented in section 5. Section 6 outlines the research gaps and implications for future research. Section 7 concludes the paper with a short summary.

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Section: The Uniform Sspmentioning

confidence: 95%

The job sequencing and tool switching problem: state-of-the-art literature review, classification, and trends

Calmels

2018

International Journal of Production Research

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“…Cooperative co-evolution is increasingly becoming the basis of successful applications [1,6,8,15,19], including learning problems, see for instance [3]. These approaches can be shared into two main categories: co-evolution process that happens between a fixed number of separate populations [5,13,14] or within a single population [7,10,18].…”

Section: Introductionmentioning

confidence: 99%

Lamps: A Test Problem for Cooperative Coevolution

Tonda

Lutton

Squillero

2011

Nature Inspired Cooperative Strategies for Optimization (NICSO 2011)

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“…Cooperative co-evolution is increasingly becoming the basis of successful applications [2,8,11,22,28], including learning [5] and scheduling problems [15]. These approaches can be shared into two main categories: heterogeneous co-evolution that happens between a fixed number of separate populations [7,20,21], and homogeneous co-evolution, that occurs within a single population [17,27,29].…”

Section: Introductionmentioning

confidence: 99%

A benchmark for cooperative coevolution

2012

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Cooperative co-evolution algorithms (CCEA) are a thriving sub-field of evolutionary computation. This class of algorithms makes it possible to exploit more efficiently the artificial Darwinist scheme, as soon as an optimisation problem can be turned into a co-evolution of interdependent sub-parts of the searched solution. Testing the efficiency of new CCEA concepts, however, it is not straightforward: while there is a rich literature of benchmarks for more traditional evolutionary techniques, the same does not hold true for this relatively new paradigm. We present a benchmark problem designed to study the behavior and performance of CCEAs, modeling a search for the optimal placement of a set of lamps inside a room. The relative complexity of the problem can be adjusted by operating on a single parameter. The fitness function is a trade-off between conflicting objectives, so the performance of an algorithm can be examined by making use of different metrics. We show how three different cooperative strategies, Parisian Evolution (PE), Group Evolution (GE) and Allopatric Group Evolution (AGE), can be applied to the problem. Using a Classical Evolution (CE) approach as comparison, we analyse the behavior of each algorithm in detail, with respect to the size of the problem capito.

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A Memetic Cooperative Optimization Schema and Its Application to the Tool Switching Problem

Cited by 4 publications

References 16 publications

The job sequencing and tool switching problem: state-of-the-art literature review, classification, and trends

The job sequencing and tool switching problem: state-of-the-art literature review, classification, and trends

Lamps: A Test Problem for Cooperative Coevolution

A benchmark for cooperative coevolution

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