Оptimization of data transmission routing in wireless sensor networks using a genetic algorithm and the ant colony optimization algorithm
DOI:
https://doi.org/10.33216/1998-7927-2026-304-6-143-152Keywords:
wireless sensor network, routing, genetic algorithm, Ant Colony Optimization, metaheuristic optimizationAbstract
This paper addresses the problem of determining a data transmission route between sensor nodes in a wireless sensor network (WSN) using two metaheuristic approaches: a genetic algorithm (GA) and the Ant Colony Optimization (ACO) algorithm. The relevance of this study stems from the fact that the selection of intermediate relay nodes directly affects the route length, the number of packet reception and retransmission operations, transmission delay, and overall network performance. The aim of the work is a methodologically consistent comparison of the genetic algorithm and the ant colony optimisation algorithm under a shared topology, an identical communication radius (30 m), a fixed source-destination node pair, and a single criterion of minimising the total route length, in order to evaluate the impact of the search mechanism on the selection of the optimal route.
In the GA, each candidate solution is encoded as an ordered sequence of sensor nodes connecting the source and destination nodes. The population is evolved using tournament selection, route crossover based on a common intermediate node, and sub-route mutation. The fitness of each chromosome is evaluated according to the total length of the edges comprising the corresponding route. Analysis of the best and average fitness values over 200 generations demonstrated rapid improvement during the initial evolutionary stage, followed by a gradual slowdown in the search process. After approximately 100 generations, the fitness values stabilized, indicating that the evolutionary process had converged toward the vicinity of the best solution found.
The ACO algorithm constructs routes incrementally using artificial agents. The probability of selecting the next sensor node is determined by combining pheromone intensity with the heuristic desirability of each feasible transition. After all ants complete their routes, the pheromone levels are updated through evaporation and reinforcement of the edges belonging to shorter paths. Through repeated updates of the pheromone matrix, the colony progressively accumulates knowledge about promising data transmission routes.
Based on the simulation results, within the investigated scenario, the application of the genetic algorithm ensured the formation of a route whose total length was shorter compared to the route obtained by the ACO method: by 28.39% for the 25-node topology and by 30.75% for the 100-node topology. These findings indicate that, for the given network topology, the evolutionary optimization of complete route chromosomes produced a more compact data transmission path, whereas the stepwise pheromone-guided search strategy resulted in a longer route.
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