Optimization Techniques for Intelligent IoT Applications

In the current time, engineering problems are associated with multiple objectives. Based on the number of objectives an optimization problem can be classified as single‐, multi‐, and many‐objective optimization. In the case of a single‐objective optimization it is easy to find a single solution, however it becomes very difficult to get a single solution in the cases of multi‐ and many‐objective optimization as the objectives are quite contradictory. Hence, evolutionary and swarm‐based algorithms are widely used to address such problems where the search space is very large and the problem is associated with multiple contradictory objectives. Today optimization is a powerful tool of trade for the engineer in virtually every discipline. It provides them with a rigorous, systematic method for rapidly zeroing in on the most innovative, cost‐effective solutions to some of today's most challenging engineering design problems. The Internet of Things (IoT) is the concept of connecting everyday devices to the Internet allowing the devices to send and receive data. With the IoT, devices can constantly report their status to a receiving computer that uses the information to optimize decision making. The IoT network optimization offers a lot of benefits for improving traffic management, operating efficiency, energy conservation, reduction in latency, higher throughput and faster rate in scaling up or deploying IoT services and devices in the network. This chapter presents an overall and in depth study of different optimization algorithms inspired from the behaviour of nature.