## 1 Introduction
Highlights of the previous surveys/reviews on the topic are shown in Table 1. It can be inferred that the existing surveys do not encompass the subject completely. For example in [23] the authors have focussed the problem of node placement and surveyed different solution techniques to enhance the performance of the WSNs. The authors categorized the existing literature into dynamic and static node placement strategies. They argued that neither of the two techniques in isolation can provide the desired result. Therefore, they suggested to use a mix of static and dynamic schemes. Particle swarm optimization (PSO) techniques have been reviewed in [24] for the optimal deployment, node localization, clustering and data aggregation in wireless sensor networks (WSNs). The authors investigated PSO based techniques with respect to their suitability for WSNs and suggested how to tailor them according to the peculiar characteristics of sensor nodes. In [25], the authors have categorized various WSNs applications and reviewed different energy conservation schemes specifically, their impact on the overall performance of the specific application. They also surveyed some existing techniques based on evolutionary algorithm to achieve various trade-offs between multiple conflicting requirements for prolonging the lifetime of the WSNs. Metaheuristic algorithms are getting popular due to their better performance in terms of convergence to the optimality and avoidance from being trapped in local optima [28]. A review is presented in [29] which elaborates application of metaheuristic algorithms to solve multi-objective optimization problems relating to data clustering in wireless sensor networks. The paper elaborates some nomenclature to highlight the aspects of clustering and depicts some important challenges to implement the technique. Biologically inspired computing for the optimization of WSNs have been reviewed in [30]. The authors have shown how the metaphoric relationship can be developed between the two systems namely, biological and non-biological. They have also shown the three stage process of ensembles design for an artificial system inspired from biological system. Therefore, the aforementioned surveys are either objective function specific or they are centered about some specific algorithms to tackle the problems relating to multi-objective optimization in WSNs. Multi-objective deployment of wireless sensor nodes has been surveyed in [31] to achieve pareto optimal front while considering multiple conflicting objectives namely, coverage, energy efficiency, lifetime and the number of sensors. For the sake of completeness and clarity we have included some surveys relating to different multi-objective optimization algorithms and their general application in various fields. For example [2] presents an overview of methods and theory of evolutionary multi-objective optimization. Specifically, the tutorial presents basic principles of multi-objective optimization and evolutionary algorithms, and various algorithmic concepts namely, fitness assignment, diversity assurances and eliticism. The tutorial also elaborates performance of multi-objective evolutionary algorithms and highlights some issues relating to its simplified implementation. A survey of evolutionary multi-objective algorithms applied to different engineering application is provided in [3]. The authors have classified different algorithms into three different categories, namely the mehtods with a priori articulation of preferences, methods with posteriori articulation of preferences and methods with no articulation of preferences. A comprehensive survey of evolutionary based multi-objective optimization techniques is presented in [6] in a way to motivate the implementation of various techniques in the emerging technological fields. The paper elaborates each technique while focusing the relative advantages and disadvantages and the feasibility of implementation in specific application.