Optimization based on bacterial chemotaxis

Müller, Sibylle D.; Marchetto, J.; Airaghi, S.; Kournoutsakos, P.

doi:10.1109/4235.985689

Cited by 284 publications

(123 citation statements)

References 12 publications

Supporting

Mentioning

113

Contrasting

Unclassified

Order By: Relevance

“…In recent years, as a new bionic evolution algorithm, bacterial colony chemotaxis (BCC) algorithm was proposed in [18]. The algorithm simulates the reaction mechanism of bacteria action under the environment attractant.…”

Section: Bacterial Colony Chemotaxis Algorithmmentioning

confidence: 99%

Coordination Optimization of PSS Parameters based on Polymorphic Bacterial Chemotaxis Algorithm

Su¹

2014

IJHIT

View full text Add to dashboard Cite

show abstract

Section: Bacterial Colony Chemotaxis Algorithmmentioning

confidence: 99%

Coordination Optimization of PSS Parameters based on Polymorphic Bacterial Chemotaxis Algorithm

Su¹

2014

IJHIT

View full text Add to dashboard Cite

show abstract

“…Even though similar approaches have been previously reported, such as OBBC (Muller et al, 2002) or BEA (Nemiche et al, 2013), we have developed a proof-of-concept inspired by bacterial conjugation that allows us to show how, in artificial societies based on interactions between agents with bounded rationality, better results emerge by incrementing heterogeneity levels and decentralization of communication structures (Heylighen, 1999). We consider bounded rationality in the sense of Simon (1991), i.e., the rationality of social agents, as a solution-search-oriented process, is limited by information in a cognitive sense.…”

Section: Introductionmentioning

confidence: 99%

Decentralization and heterogeneity in complex adaptive systems

Gonzalez-Rodriguez

Hernandez-Carrion

2015

Kybernetes

View full text Add to dashboard Cite

Abstract. Following a bacterial-based modeling approach, the authors want to model and analyze the impact of both decentralization and heterogeneity on group behavior and collective learning. The paper aims to discuss these issues.Inspired by bacterial conjugation, the authors have defined an artificial society in which agents' strategies adapt to changes in resources location, allowing migration, and survival in a dynamic sugarscape-like scenario. To study the impact of these variables the authors have simulated a scenario in which resources are limited and localized. The authors also have defined three constraints in genetic information processing (inhibition of plasmid conjugation, inhibition of plasmid reproduction and inhibition of plasmid mutation). The results affirmed the hypothesis that efficiency of group adaptation to dynamic environments is better when societies are varied and distributed than when they are homogeneous and centralized.The authors have demonstrated that in a model based on free interactions among autonomous agents, optimal results emerge by incrementing heterogeneity levels and decentralization of communication structures, leading to a global adaptation of the system. This organic approach to model peer-to-peer dynamics in complex adaptive systems (CAS) is what the authors have named "bacterial-based algorithms" because agents exchange strategic information in the same way that bacteria use conjugation and share genome.

show abstract

“…Recently chemotaxis (i.e., the bacterial foraging behaviour) as a rich source of potential engineering applications and computational model has attracted more and more attentions. Several new evolutionary computing techniques have been developed to mimic bacterial foraging behaviour (Bremermann and Anderson, 1990;Müeller et al, 2002;Passino, 2002;Chen et al, 2009Chen et al, , 2011. Among them, Bacterial foraging optimisation (BFO) is one population-based numerical optimisation algorithm presented by Passino and successfully applied to many real world problems like optimal control (Kim and Cho, 2005), harmonic estimation (Mishra, 2005), transmission loss reduction (Tripathy et al, 2006), RFID network planning (Chen et al, 2010), active power filter for load compensation (Mishra and Bhende, 2007), power network (Tripathy and Mishra, 2007), and load forecasting (Ulagammai et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Bacterial colony foraging for multi-mode product colour planning

Chen¹,

Zhu²,

Ma³

et al. 2015

IJBIC

View full text Add to dashboard Cite

Abstract:In this work, in order to assist designer in colour planning during product development, an efficient synthesised evaluation model is presented to evaluate colour-combination schemes of multi-working modes products (MMP). A novel bacterial colony foraging (BCF) algorithm is proposed to search for the optimal colour-combination schemes of MMP based on the evaluation model. The proposed BCF extend original bacterial foraging algorithm to adaptive and cooperative mode by combining bacterial chemotaxis, cell-to-cell communication, and a self-adaptive foraging strategy. The experiment presents an exhaustive comparison of the proposed BCF and two successful bio-inspired search techniques, namely the genetic algorithm (GA) and particle swarm optimisation (PSO), on three MMP tested cases of different nature, namely a hair-drier with two-coloured areas and two working modes, and two arm-type aerial work platforms both two-coloured products while with two and three working modes, respectively. Simulation results demonstrate that the proposed method is feasible and efficient.

show abstract

Optimization based on bacterial chemotaxis

Cited by 284 publications

References 12 publications

Coordination Optimization of PSS Parameters based on Polymorphic Bacterial Chemotaxis Algorithm

Coordination Optimization of PSS Parameters based on Polymorphic Bacterial Chemotaxis Algorithm

Decentralization and heterogeneity in complex adaptive systems

Bacterial colony foraging for multi-mode product colour planning

Contact Info

Product

Resources

About