A Multi-Agent Based Approach for Particle Swarm Optimization

Ahmad, Raheel; Lee, Yung-Chuan; Rahimi, Shahram; Gupta, Bidyut

doi:10.1109/kimas.2007.369820

Cited by 15 publications

(3 citation statements)

References 6 publications

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“…Moreover, the modeling of the problem is not presented. In [16], the authors propose another agent-based PSO algorithm. With a higher degree of learning and an asynchronous execution, the particles in this algorithm have more autonomy.…”

Section: Related Work On the Hybridization Of Pso And Masmentioning

confidence: 99%

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A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

et al. 2019

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h i g h l i g h t s• Proving the efficiency of optimization algorithms in solving real-world problems.• A new concept of accent birds introduced to the particle swarm optimization. • A new hybrid scheme that integrates PSO and MAS.• The hybridization improves the performance of the original tested algorithms. • A comparison between simulation and experimental validation is given. a r t i c l e i n f o Keywords: Accent based PSO Multi-agent Many-objective optimization Experimental validation 3D indoor deployment DL-IoT collection networks a b s t r a c tThe 3D indoor deployment of sensor nodes is a complex real world problem, proven to be NP-hard and difficult to resolve using classical methods. In this context, we propose a hybrid approach relying on a novel bird's accent-based many objective particle swarm optimization algorithm (named acMaPSO) to resolve the problem of 3D indoor deployment on the Internet of Things collection networks. The new concept of bird's accent is presented to assess the search ability of particles in their local areas. To conserve the diversity of the population during searching, particles are separated into different accent groups by their regional habitation and are classified into different categories of birds/particles in each cluster according to their common manner of singing. A particle in an accent-group can select other particles as its neighbors from its group or from other groups (which sing differently) if the selected particles have the same expertise in singing or are less experienced compared to this particle. To allow the search escaping from local optima, the most expert particles (parents) ''die'' and are regularly replaced by a novice (newborn) randomly generated ones. Moreover, the hybridization of the proposed acMaPSO algorithm with multi-agent systems is suggested. The new variant (named acMaMaPSO) takes advantage of the distribution and interactivity of particle agents. Experimental, numerical and statistical found results show the effectiveness of the two proposed variants compared to different other recent state-of-the-art of many-objective evolutionary algorithms.✩ This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. * Correspondence to: bureau C107, Batiment C, 1 Place Georges positions and the topology of the network. In this study, we are interested in the deployment of nodes in a three-dimensional space that reflects the real topology of the RoI (region of interest) better than the two-dimensional case. We are particularly interested in improving the initial 3D indoor deployment by adding new nodes while optimizing a set of objectives such as coverage, connectivity, localization, quality of links and network utilization. These objectives will be detailed in the modeling section. To resolve the deployment problem in WSN, the topology of the network can be modeled as an identification problem in a graph as addressed in [1] and [2]. In this static case, the deployment algorithm is run off-...

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Section: Related Work On the Hybridization Of Pso And Masmentioning

confidence: 99%

“…If the maximum velocity of the particle is too low, the algorithm becomes too slow; else if it is too high, the algorithm becomes too unstable. Thus, the maximum particle velocity is set according to 5 − 4 * (it/tIT ) (16) where it is the current iteration and IT is the maximum number of iterations.…”

Section: Pso and Nsga-iii Parametersmentioning

confidence: 99%

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

et al. 2019

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“…DOI: http://dx.doi.org/10.5772/intechopen.89830 A number of signature algorithms have been developed to actualise swarm intelligence in various applications. The most common of these algorithms include ant colony optimization (ACO), bee colony optimization (BCO) [63] and particle swarm optimization (PSO) [64]. ACO is motivated by the foraging behaviour of ant colonies.…”

Section: Multi-agent Systems and Swarm Intelligencementioning

confidence: 99%

Sensor-Driven, Spatially Explicit Agent-Based Models

Oloo¹

2019

Swarm Intelligence - Recent Advances, New Perspectives and Applications

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Conventionally, agent-based models (ABMs) are specified from well-established theory about the systems under investigation. For such models, data is only introduced to ensure the validity of the specified models. In cases where the underlying mechanisms of the system of interest are unknown, rich datasets about the system can reveal patterns and processes of the systems. Sensors have become ubiquitous allowing researchers to capture precise characteristics of entities in both time and space. The combination of data from in situ sensors to geospatial outputs provides a rich resource for characterising geospatial environments and entities on earth. More importantly, the sensor data can capture behaviours and interactions of entities allowing us to visualise emerging patterns from the interactions. However, there is a paucity of standardised methods for the integration of dynamic sensor data streams into ABMs. Further, only few models have attempted to incorporate spatial and temporal data dynamically from sensors for model specification, calibration and validation. This chapter documents the state of the art of methods for bridging the gap between sensor data observations and specification of accurate spatially explicit agent-based models. In addition, this work proposes a conceptual framework for dynamic validation of sensor-driven spatial ABMs to address the risk of model overfitting.

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