For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.

For the last two decades, nature inspired metaheuristic algorithms have shown their ubiquitous nature in almost every aspect, where computational intelligence is used. This paper intends to focus on the comparative study of two popular and robust bio mimic strategies used in computer engineering, namely Particle Swarm Optimization (PSO) and Cuckoo Search (CS). According to the results, CS outperforms PSO. The performance comparison of both algorithms is implemented in the form of problem specific distance functions rather than an algorithmic distance function. Also an attempt is taken to examine the claim that CS has the same effectiveness of finding the true global optimal solution as the PSO but with significantly better computational efficiency, which means less function evaluations.