Earticle

Optimal sensor placement (OSP) techniques are of vital importance for the monitoring of large-scale structures or complex mechanical systems. In order to overcome the defects of slow convergence speed and easily falling into local optimum in monkey algorithm (MA), a new MA, called chaotic monkey algorithm (CMA), is proposed by introducing chaos search strategy to implement the OSP. In this algorithm, the initial monkeys are generated by using chaos variable and binary coding to enhance the global search capability, and a greedy strategy is adopted to improve the efficiency of local search. Numerical experiments are conducted on the sensor placement of a suspension bridge. The results verify that the new CMA can solve the OSP problem well and has better search capability than MA.

The vehicle routing problem (VRP) is one of the most challenging problems in the op-timization of distribution. Genetic algorithm (GA) has been proved capable of solving VRPs. However, the resolution effectiveness of GA decreases with the increase of nodes within VRPs. It often appears premature convergence or poor ability of local search. In this paper, a hybrid genetic-monkey algorithm (HGMA), integrating monkey algorithm (MA) into genetic algorithm framework, is proposed to overcome the shortcomings above. Monkey climbing (A novel climb process is designed for VRP with discrete variables.) and somersault processes improve ability of local and global search. Furthermore, flexi-ble fitness function, through which infeasible solutions are allowed, are developed to ex-pand the search space. The experiment results indicate efficiency of the proposed algorithm.

Placing sensors at appropriate locations is an important task in the design of an efficient structural health monitoring (SHM) system for a large-scale civil structure. In this paper, a hybrid optimization algorithm called virus monkey algorithm (VMA) based on the virus theory of evolution is proposed to seek the optimal placement of sensors. Firstly, the dual-structure coding method is adopted instead of binary coding method to code the solution. Then, the VMA is designed to incorporate two populations, a monkey population and a virus population, enabling the horizontal propagation between the monkey and virus individuals and the vertical inheritance of monkey's position information from the previous to following position. Correspondingly, the monkey population in this paper is divided into the superior and inferior monkey populations, and the virus population is divided into the serious and slight virus populations. The serious virus is used to infect the inferior monkey to make it escape from the local optima, while the slight virus is adopted to infect the superior monkey to let it find a better result in the nearby area. This kind of novel virus infection operator enables the coevolution of monkey and virus populations. Finally, the effectiveness of the proposed VMA is demonstrated by designing the sensor network of the Canton Tower, the tallest TV Tower in China. Results show that innovations in the VMA proposed in this paper can improve the convergence of algorithm compared with the original monkey algorithm (MA).