An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing nodes with complex parameter coordination both sides of the road use self-organizing capabilities each node to automatically establish entire network. In large-scale self-organization process, importance tasks undertaken by is different. It not difficult prove that task allocation sensors NP-hard problem, efficient strategy necessary for ITWSNs. This paper proposes improved adaptive clone genetic algorithm (IACGA) solve problem The uses clonal expansion operator speed up convergence rate improve global search capability. To verify performance IACGA optimization ITWSNs, compared elite (EGA), simulated annealing (SA), shuffled frog leaping (SFLA). simulation results show execution higher than EGA, SA, SFLA. Moreover, faster. addition, revenue using those Therefore, proposed can effectively ITWSN