Considering direct interaction of artificial ant colony foraging simulation and animation

Meng, Zhigang; Zou, Beiji; Zeng, Yu

doi:10.1080/0952813x.2010.545999

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Multi-agent approach is suitable for many real world applications: mine detecting [1] [2], search in damaged buildings [3] [4], fire fighting [5], and exploration of spaces [6] [7]. Simple, local and individual rules can provide complex, global and collective behaviors [8], as in social insects (ants, bees and termites). Such biological systems use stigmergic communication (pheromones for example), to coordinate their actions aiming to improve the performances of the team.…”

Section: Introductionmentioning

confidence: 99%

A distributed foraging algorithm based on artificial potential field

Zedadra¹,

Séridi²,

Jouandeau³

et al. 2015

2015 12th International Symposium on Programming and Systems (ISPS)

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Simple collections of agents that perform collectively and use distributed control algorithms constitute the interests of swarm robotics. A key issue to improve system performances is to effectively coordinate the team of agents. We present in this paper a multi-agent foraging algorithm called Cooperative-Color Marking Foraging Agents (C-CMFA). It uses the coordination rules of the S-MASA (Stigmergic Multi-Ant Search Area) algorithm to (i) speed up the search process and (ii) allow agents to build an optimal Artificial Potential Field (APF) simultaneously while exploring. To benefit from multiple robots, we add one cooperation rule in the algorithm to attract large number of agents to the found food. This algorithm constitutes a distributed and synchronous version of the c-marking algorithm. Simulation results in comparison with the c-marking one show the superiority of C-CMFA in different environment configurations.

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Section: Introductionmentioning

confidence: 99%

A distributed foraging algorithm based on artificial potential field

Zedadra¹,

Séridi²,

Jouandeau³

et al. 2015

2015 12th International Symposium on Programming and Systems (ISPS)

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“…Direct Ant Colony Foraging DACF2 and DACF3 are two ant colony foraging models proposed by Meng et al (2012). The two models adapt (Panait and Luke 2004;Wilensky 1997) models respectively, by introducing direct interaction (via direct communication) besides indirect interaction (via pheromone).…”

Section: Foraging Related Workmentioning

confidence: 99%

Multi-Agent Foraging: state-of-the-art and research challenges

Zedadra

Jouandeau²,

Séridi

et al. 2017

Complex Adapt Syst Model

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BackgroundSwarm intelligence provides the design and implementation of systems composed of many simple individuals who interact locally and produce remarkable behavior as a whole (Dudek et al. 1996). It provides multiple benefits such as robustness where the performance of the system is not affected significantly with the failure of individuals, simplicity of computational and perceptual capabilities of individuals but still allowing global complex behaviors and scalability of the control mechanism that does not depend on the number of agents (Mitton and Simplot-Ryl 2014). The application of swarm intelligence to collective robotics is identified as Swarm Robotics in El Zoghby et al. (2014). Many artificial systems such as distributed computing systems and artificial intelligence systems are characterized by complex behaviors that emerge as a result of the nonlinear spatio-temporal interactions among a large number of system components at different levels of organization. These systems are known as Complex Adaptive Systems (CAS) as stated by Lansing (2003). Holland (2006) also considers CAS as dynamic systems able to adapt in and evolve with a changing environment. MAF problem is a benchmark problem for swarm robotics. It can be seen as a CAS and defined like in Niazi and Hussain Abstract Background: The foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth.Results: First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems Conclusions:Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic. Zedadra et al. Complex Adapt Syst Model (2017) et al. Complex Adapt Syst Model (2017) 5:3 (2012) as a system made up of multiple simple individuals which interact in a nonlinear fashion, thereby giving rise to global and often unpredictable behaviors. A good way to understand a CAS is to study them in special cases, thus to simulate dedicated behavior from particular perspectives. Holland (2006) states that the analysis of CAS is done through a combination of applied, theoretical and experimental methods (e.g. mathematics and computer simulations). Authors in Fortino and North (2013) state that Agent-Based Modeling (ABM) has proven to provide an effective set of tools for modeling and simulating different ...

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“…Agents construct paths simultaneously while exploring, by writing integer values in each visited cell. Direct Ant Colony Foraging (DACF2) and Direct Ant Colony Foraging (DACF3) are two ant colony foraging models [38] that adapt Panait's [39] and Wilinskey's [? ] models, by adding direct interaction between agents to exchange stored variables in order to create the shortest path.…”

Section: Related Workmentioning

confidence: 99%

A Cooperative Switching Algorithm for Multi-Agent Foraging

Zedadra

Séridi

Jouandeau

et al. 2016

Engineering Applications of Artificial Intelligence

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Foraging is the act of searching for, and when found transporting objects to one or more sinks. Although foraging can be accomplished by one single robot operating individually, it can be supported more efficiently by multiple robots operating collectively. The efficiency of the group of robots can be sufficiently improved through coordination. Pheromone-based communication is well suited for the coordination of swarm of robots. It is one of the main subjects of swarm intelligence that provides intelligent global behaviors from simple local behaviors. The goal of this work, is to study the benefit of collective foraging via the implicit recruitment when one agent alerts the others to the location of food. We present therefore, a multi-agent foraging algorithm named Cooperative Switching Algorithm for Foraging (C-SAF) inspired from the classical ant system, where agents use pheromone to collectively search and avoid already visited cells, and to communicate the food location. C-SAF provides a quick search, optimal paths to return to nest and quick exploitation of food. We then generalize the algorithm to produce a flexible and extensible framework for foraging, that can be reused or extended by other foraging algorithms. A qualitative comparison with some related search and foraging algorithms is presented in this paper. A quantitative comparison shows that our algorithm outperforms the reference c-marking algorithm across a range of scenarios that differ in terms of agent, environment and food parameters.

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Considering direct interaction of artificial ant colony foraging simulation and animation

Cited by 8 publications

References 13 publications

A distributed foraging algorithm based on artificial potential field

A distributed foraging algorithm based on artificial potential field

Multi-Agent Foraging: state-of-the-art and research challenges

A Cooperative Switching Algorithm for Multi-Agent Foraging

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