Abstract
The objective of this paper is focuses on route optimization, for a given wireless sensor network. We detail the significance of route optimization problem and the corresponding mathematical model. After analyzing the complex multi-objective optimization problem, Ant Colony Optimization (ACO) algorithm was introduced to search the best route. Inspired by Genetic Algorithm (GA), we embed two operations into ACO to refine it. First, every ant after achieving sink will be regarded as an individual such as that in GA. The crossover operation will be applied and then, the generated new ants will replace the weaker parents. Second, we designed a mutation operation for ants selecting next nodes to visit. Experimental results demonstrate that the proposed combination algorithm has significant enhancements than both GA and ACO. The lifetime of WSN can be extended and the coverage speed can be accelerated.
Highlights
Wireless Sensor Network (WSN) is a synthesized problem which combining embedding technology, sensor communication technology and distributed information processing technology [1]
Ant colony optimization algorithm finds the optimal solution by accumulating the pheromone, which is an excellent algorithm with global parallel and distributed search capabilities [13][14]
The parameters are same in both Ant Colony Optimization (ACO) and Genetic Algorithm (GA)
Summary
Wireless Sensor Network (WSN) is a synthesized problem which combining embedding technology, sensor communication technology and distributed information processing technology [1]. Because of the limited energy of sensor nodes, route optimization design considers the length of the data transmission path, and considers saving energy and network energy balance and other issues. The route optimization of wireless sensor network is to find the shortest data transmission path between a source node and sink node with least energy consumption and the longest life cycle, which is a complex multi-objective optimization problem. Optimization algorithms based on artificial intelligence shows excellent performances on solving combination optimization problems such as Genetic Algorithm (GA) [10], Ant Colony Optimization (ACO) [9], Simulate Annealing (SA) algorithm [11] and Particle Swarm Optimization (PSO) [12]. We combine GA and ACO design two operations to form a superior optimization algorithm, which is a powerful wireless sensor network path optimization tool with less time consumption and higher precision.
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