Force-based Genetic Algorithm Research Articles

Performance metrics for self-positioning autonomous MANET nodes

We present quantitative techniques to assess the performance of mobile ad hoc network (MANET) nodes with respect to uniform distribution, the total terrain covered by the communication areas of all nodes, and distance traveled by each node before a desired network topology is reached. Our uniformity metrics exploit information from a Voronoi tessellation generated by nodes in a deployment territory. Since movement is one of the most power consuming tasks that mobile nodes execute, the average distance traveled by each node (ADT) before the network reaches its final distribution provides an important performance assessment tool for power-aware MANETs. Another performance metric, network area coverage (NAC) achieved by all nodes, can demonstrate how efficient the MANET nodes are in maximizing the area of operation. Using these metrics, we evaluate our node-spreading bio-inspired game (BioGame), that combines our force-based genetic algorithm (FGA) and game theory to guide autonomous mobile nodes in making movement decisions. Our simulation experiments demonstrate that these performance evaluation metrics are good indicators for assessing the efficiency of node distribution methods.

Real‐time, decentralized and bio‐inspired topology control for holonomic autonomous vehicles

PurposeThis paper aims to present an approach for a bio‐inspired decentralization topology control mechanism, called force‐based genetic algorithm (FGA), where a genetic algorithm (GA) is run by each holonomic autonomous vehicle (HAV) in a mobile ad hoc network (MANET) as software agent to achieve a uniform spread of HAVs and to provide a fully connected network over an unknown geographical terrain. An HAV runs its own FGA to decide its next movement direction and speed based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge.Design/methodology/approachThe objective function used in FGA is inspired by the equilibrium of the molecules in physics where each molecule tries to be in the balanced position to spend minimum energy to maintain its position. In this approach, a virtual force is assumed to be applied by the neighboring HAVs to a given HAV. At equilibrium, the aggregate virtual force applied to an HAV by its neighbors should sum up to zero. If the aggregate virtual force is not zero, it is used as a fitness value for the HAV. The value of this virtual force depends on the number of neighbors within the communication range of Rcom and the distance among them. Each chromosome in our GA‐based framework is composed of speed and movement direction. The FGA is independently run by each HAV as a topology control mechanism and only utilizes information from neighbors and local terrain to make movement and speed decisions to converge towards a uniform distribution of HAVs. The authors developed an analytical model, simulation software and several testbeds to study the convergence properties of the FGA.FindingsThe paper finds that coverage‐centric, bio‐inspired, mobile node deployment algorithm ensures effective sensing coverage for each mobile node after initial deployment. The FGA is also an energy‐aware self‐organization framework since it reduces energy consumption by eliminating unnecessary excessive movements. Fault‐tolerance is another important feature of the GA‐based approach since the FGA is resilient to losses and malfunctions of HAVs. Furthermore, the analytical results show that the authors' bio‐inspired approach is effective in terms of convergence speed and area coverage uniformity. As seen from the experimental results, the FGA delivers promising results for uniform autonomous mobile node distribution over an unknown geographical terrain.Originality/valueThe proposed decentralized and bio‐inspired approach for autonomous mobile nodes can be used as a real‐time topology control mechanism for commercial and military applications since it adapts to local environment rapidly but does not require global network knowledge.

Markov Chain Analysis of Self-organizing Mobile Nodes

Self-organization of autonomous mobile nodes using bio-inspired algorithms in mobile ad hoc networks (manets) has been presented in earlier work of the authors. In this paper, the convergence speed of our force-based genetic algorithm (called fga) is provided through analysis using homogeneous Markov chains. The fga is run by each mobile node as a topology control mechanism to decide a corresponding node's next speed and movement direction so that it guides an autonomous mobile node over an unknown geographical area to obtain a uniform node distribution while only using local information. The stochastic behavior of fga, like all ga-based approaches, makes it difficult to analyze the effects that various manet characteristics have on its convergence speed. Metrically transitive homogeneous Markov chains have been used to analyze the convergence of our fga with respect to various communication ranges of mobile nodes and also the number of nodes in various scenarios. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for Markov chain model of our fga. Two different testbed platforms are presented to illustrate effectiveness of our bio-inspired algorithm in terms of area coverage.