THE NICHING MONKEY ALGORITHM FOR OPTIMAL SENSOR PLACEMENT

The Niching monkey algorithm (NMA) for optimal sensor placement (OSP) is proposed by introducing the Niche mechanism into the monkey algorithm (MA). The dual-structure coding method is used to overcome the problem as the original MA can only deal with the optimization of continuous variables. A chaos-based approach is employed for a uniform distribution of the initial positions of the monkeys, and the monkeys are separated into Niching monkey subsystems to form individual searching spaces. In addition, the inferior eliminated mechanism is introduced into every Niching monkey subsystem based on the sharing fitness, and new monkeys are generated by random initialization, which enhances the diversity of the monkey population. Moreover, the average fitness replacement mechanism is used to share information about the best positions of monkeys among Niching monkey subsystems, which improves the global searching performance and the convergence efficiency of the NMA. Finally, a case study is conducted in terms of parametric sensitivity analysis and OSP on the Dalian international trade mansion. Results show that the searching efficiency of NMA has been greatly improved compared with the original MA, which is beneficial to OSP problems.