Significant research interest has been shown in wireless sensor networks (WSNs), particularly in the context of internet of things (IoT) technologies. However, obtaining the optimal WSN lifespan network is a challenging issue that needs to be analyzed critically prior to any network setup. In recent years, new bio-inspired algorithms have been developed drawing their inspiration from biological and natural phenomena. Bio-inspired optimization algorithms have been compared with the traditional optimization algorithms and are showing promise as a solution to complex real-life problems. This chapter provides a survey and tutorial of recent research trends and development efforts addressing WSN issues by using bio-inspired optimization algorithms. The key intention of this work is to serve as a foundation for analysis of the emerging area of bio-inspired algorithms and multi-objective bio-inspired optimization algorithms for solving the underlying issues in wireless sensor networks.
Top2. Optimization And Bio-Inspired Algorithms
Every aspect of nature and existence, aims to be at its optimum. All optimal seeking involves achieving goals or objectives and satisfying the constraints wherein the optimum must be reached (Chow, 2014) (Chen et al., 2014). This optimal searching can be expressed as an optimization problem (Peng & Ouyang, 2014) (Eid & Abraham, 2018). Based on the produced solutions, optimization algorithms are classified into two categories: deterministic and stochastic algorithms (Yang, 2010). Deterministic algorithms operate without the use of randomness, whereby, the same path is repeatedly taken by the algorithm, and it consistently produces the same result across different runs. In contrast, stochastic algorithms display some randomness and provide varying results between runs.
Stochastic algorithms have the benefit of simultaneously exploring several areas of the search space, being able to escape the local optima, and being able to achieve the global optimum (Li & Grossmann, 2021). Stochastic optimization algorithms fall into one of two categories: heuristics and meta-heuristics. Heuristic refers to a method of finding or discovering the optimum via trial and error. While, the meta-heuristic algorithm iteratively directs a subordinated heuristic by fusing some specified rules for exploring and exploiting the search space. Meta-heuristic algorithms have been categorized in a variety of ways in the literature. They may be divided into two categories: trajectory based and population based (I et al., 2013), as shown in Figure 1. While population-based algorithms utilize several agents (multiple solutions), which will interact and move towards objectives simultaneously, trajectory-based algorithms only use one agent (single solution) at a time.