Collective Animal Behavior Algorithm for Multimodal Optimization Functions

2016 ◽  
pp. 153-174
Author(s):  
Erik Cuevas ◽  
Margarita Arimatea Díaz Cortés ◽  
Diego Alberto Oliva Navarro
Keyword(s):  
Animal Behavior ◽  
Multimodal Optimization ◽  
Collective Animal Behavior

Electromagnetism-like mechanism with collective animal behavior for multimodal optimization

2017 ◽  
Vol 48 (9) ◽  
pp. 2580-2612
Author(s):  
Jorge Gálvez ◽  
Erik Cuevas ◽  
Omar Avalos ◽  
Diego Oliva ◽  
Salvador Hinojosa
Keyword(s):  
Animal Behavior ◽  
Multimodal Optimization ◽  
Collective Animal Behavior

scholarly journals An Algorithm for Global Optimization Inspired by Collective Animal Behavior

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Erik Cuevas ◽  
Mauricio González ◽  
Daniel Zaldivar ◽  
Marco Pérez-Cisneros ◽  
Guillermo García
Keyword(s):  
Global Optimization ◽  
Animal Behavior ◽  
Food Source ◽  
Collective Motion ◽  
Global Optimum ◽  
Migration Routes ◽  
Collective Behaviors ◽  
Biological Laws ◽  
Harvesting Efficiency ◽  
Collective Animal Behavior

A metaheuristic algorithm for global optimization called the collective animal behavior (CAB) is introduced. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central locations, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of animals which interact with each other based on the biological laws of collective motion. The proposed method has been compared to other well-known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.

scholarly journals Probabilistic models of individual and collective animal behavior

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0193049 ◽  
Author(s):  
Katarína Bod’ová ◽  
Gabriel J. Mitchell ◽  
Roy Harpaz ◽  
Elad Schneidman ◽  
Gašper Tkačik
Keyword(s):  
Animal Behavior ◽  
Probabilistic Models ◽  
Collective Animal Behavior

scholarly journals Inference of Causal Information Flow in Collective Animal Behavior

2016 ◽  
Vol 2 (1) ◽  
pp. 107-116 ◽  
Author(s):  
Warren M. Lord ◽  
Jie Sun ◽  
Nicholas T. Ouellette ◽  
Erik M. Bollt
Keyword(s):  
Information Flow ◽  
Animal Behavior ◽  
Causal Information ◽  
Collective Animal Behavior

scholarly journals Collective Animal Behavior from Bayesian Estimation and Probability Matching

2011 ◽  
Vol 7 (11) ◽  
pp. e1002282 ◽  
Author(s):  
Alfonso Pérez-Escudero ◽  
Gonzalo G. de Polavieja
Keyword(s):  
Bayesian Estimation ◽  
Animal Behavior ◽  
Probability Matching ◽  
Collective Animal Behavior

FROM EMPIRICAL DATA TO INTER-INDIVIDUAL INTERACTIONS: UNVEILING THE RULES OF COLLECTIVE ANIMAL BEHAVIOR

2010 ◽  
Vol 20 (supp01) ◽  
pp. 1491-1510 ◽  
Author(s):  
ANDREA CAVAGNA ◽  
ALESSIO CIMARELLI ◽  
IRENE GIARDINA ◽  
GIORGIO PARISI ◽  
RAFFAELE SANTAGATI ◽  
...  
Keyword(s):  
Animal Behavior ◽  
Empirical Data ◽  
Large Scale ◽  
Empirical Studies ◽  
Theoretical Models ◽  
Fixed Number ◽  
Data Set ◽  
Self Organized ◽  
Theoretical Approaches ◽  
Collective Animal Behavior

Animal groups represent magnificent archetypes of self-organized collective behavior. As such, they have attracted enormous interdisciplinary interest in the last years. From a mechanistic point of view, animal aggregations remind physical systems of particles or spins, where the individual constituents interact locally, giving rise to ordering at the global scale. This analogy has fostered important research, where numerical and theoretical approaches from physics have been applied to models of self-organized motion. In this paper, we discuss how the physics methodology may provide precious conceptual and technical instruments in empirical studies of collective animal behavior. We focus on three-dimensional groups, for which empirical data have been extremely scarce until recently, and describe novel experimental protocols that allow reconstructing aggregations of thousands of individuals. We show how an appropriate statistical analysis of these large-scale data allows inferring important information on the interactions between individuals in a group, a key issue in behavioral studies and a basic ingredient of theoretical models. To this aim, we revisit the approach we recently used on starling flocks, and apply it to a much larger data set, never analyzed before. The results confirm our previous findings and indicate that interactions between birds have a topological rather than metric nature, each individual interacting with a fixed number of neighbors irrespective of their distances.

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