Introduction to Intelligent Systems, Control, and Machine Learning using MATLAB

In Chapter 5, we look at approaches that belong to heuristic algorithms. These methods are derived from observations nature provide. In our argumentation for specific heuristic optimization algorithms, we discuss the local search and the hill climbing problem. One of the outcomes of this discussion is the argument for attempting to avoid cycling during a search. Tabu search optimization is built on this premise where we avoid cycling. An entirely different class of heuristic optimization algorithms are given by Particle Swarm optimization and Ant Colony optimization algorithms. In contrast to Tabu search and local search, the PSO and AC optimization algorithms utilize a number of agents in order to search for optimality. Another multi agent based algorithm is the Genetic algorithm. GA’s are inspired by Darwin’s survival of the fittest principle and use the terminology found in the field of genetics. Additionally, in this chapter we use heuristic optimization to formulate optimum control concepts, including hybrid control using fuzzy logic-based controllers and Matlab scripts to realize each of the heuristic optimization algorithms.