A study of a parallelized immune coevolutionary algorithm for division-of-labor problems

Toma, Naruaki; Endo, Satoshi; Yamada, Kôji

doi:10.1007/s10015-004-0328-2

Cited by 10 publications

(10 citation statements)

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“…Many inspirations from biology, physics, chemistry, economics, sociology, anthropology, psychology and others are adopted as its coevolutionary mechanisms. And the application areas also vary in an adequate broad range (Aitkenhead, 2008;Roh, Oh, & Han, 2003;Toma et al, 2005). It appears to have many advantages over traditional evolutionary methods in dealing with the large searching space, non-intrinsic or complex objective measures and searching space with complex structures.…”

Section: Co-evolutionary Algorithmmentioning

confidence: 98%

“…In the widely studied immune algorithms, many results are about the clonal selection (Dong, Shi, & Zhang, 2007), negative selection (Cao et al, 2007) and immune network (Freschi & Repetto, 2006). Another newly developed field is the study of cooperative model (Toma, Endo, & Yamada, 2005) in immune system. Immune system is very complex and expresses its immune function by cooperation of many immune entities.…”

Section: Immune Algorithmmentioning

confidence: 99%

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Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system

Ding

Shao

2009

Expert Systems with Applications

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Section: Co-evolutionary Algorithmmentioning

confidence: 98%

Section: Immune Algorithmmentioning

confidence: 99%

Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system

Ding

Shao

2009

Expert Systems with Applications

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“…Most of them are implemented based on three immune principles, the clonal selection [26], negative selection [21] and immune network [27]. Immune inspired co-evolutionary models are new mechanisms for algorithm design [28].…”

Section: Immune Algorithmmentioning

confidence: 99%

“…Many inspirations from biology, physics, chemistry, economics, sociology, anthropology, psychology and others are adopted as co-evolutionary mechanisms [29]. And the application areas vary in an adequate broad range [28,30,31]. It appears to have advantages over traditional evolutionary methods in dealing with the larger searching space, non-intrinsic or complex objective measures and searching space with complex structures.…”

Section: Co-evolutionary Algorithmmentioning

confidence: 99%

A Decision Support System for Tobacco Distribution Partition Optimization Based on Immune Co-Evolutionary Algorithm

Hu¹,

Yang²,

Huang³

2010

JCP

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<span style="font-size: 10pt; font-family: "Times New Roman"; mso-fareast-font-family: 宋体; mso-fareast-language: EN-US; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">The tobacco distribution in China is organized by the tobacco company in the unit of city. The distribution cycle is commonly a week with five fixed distribution districts started from the center depot, and the routes in each district are fixed for the vehicles and drivers. This method with low efficiency and high cost has continued for several decades because of the poor technology and economic reasons. In this paper, we propose a feasible and optimal method for the tobacco distribution partition balance problem (TDPBP) by breaking the fixed partitions. An immune co-evolutionary algorithm (ICEA) is proposed to search the optimal partitions. Moreover, the decision support system (DSS) for partition balance is designed. Linfen city in China as a real-world case is proved that the DSS can discover the efficient distribution planning. The comparisons among three solutions, ‘Fixed’, ‘the DSS’ and ‘Pure VRP’, further prove that the DSS is an effective decision support tool for TDPBP.</span>

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“…It introduces the interactive immune co-evolution mechanisms [11][12][13][14][15], and proposes an interactive immune coevolution algorithm (IICEA) model. Also, an expert evaluation model is established for the differential fiber spinning process with IICEA model.…”

Section: Introductionmentioning

confidence: 99%

An interactive immune co-evolutionary design approach for the spinning of differential fibers

Ren

Ding

et al. 2011

2011 6th IEEE Conference on Industrial Electronics and Applications

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Co-evolutionary algorithm behaves in a complicated and counterintuitive way for some complex problems. The introduction of interactive immune concept can increase its convergence rate and effectiveness. This paper represents an optimization process of polyester filament in melt spinning with the interactive immune coevolutionary design approach and also the expert evaluation with entropy-based fuzzy comprehensive evaluation. The simulation results demonstrate its effectiveness. This study also has important value and widespread application prospect regarding the spinning process optimization and the subsequent processing in fiber production lines.

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A study of a parallelized immune coevolutionary algorithm for division-of-labor problems

Cited by 10 publications

References 0 publications

Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system

Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system

A Decision Support System for Tobacco Distribution Partition Optimization Based on Immune Co-Evolutionary Algorithm

An interactive immune co-evolutionary design approach for the spinning of differential fibers

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