An improved clone cuckoo search algorithm for solving the multi-constrained QoS routing problem in self-organizing wireless sensor network

Recent advances in data gathering, micro manufacturing and wireless communications pave the way for the applications of low computational complexity, numerous, autonomous, and intelligent sensing units. Self-organizing wireless sensor network (SOWSN) has a variety of abilities such as information acquisition, wireless communication, computation and free-infrastructure capabilities. SOWSN is intensively studied and used in homelands security, military affairs, environmental, industrial process control, advanced health care delivery and many other areas. How to minimize path energy consumption in SOWSN is a main issue in solving the multi-constrained quality of services (QoS) routing problem. It affects the energy cost optimization as well as the energy cost optimization greatly. Aiming to solve the QoS routing problem in SOWSN, we propose an improved clone cuckoo search algorithm (ICCSA). It is a randomized swarm optimization method to achieve lower energy cost optimization. The proposed algorithm is motivated by improved theory and clone theory and based on an efficient cuckoo search algorithm. It has many advantages by using the improved operator as well as clone operator. Computer simulations are given to compare the overall performance of ICCSA with artificial fish swarm algorithm (AFSA), bat algorithm (BA) and ant colony algorithm (ACO), which demonstrates the validity and efficacy of the designed algorithm. After the simulation insection IV, the proposed ICCSA method has a better performance in reducing the energy cost when compares to the current AFSA, BA and ACO methods.