Abstract
This chapter provides with basic knowledge of recent intelligent optimization and control techniques, and how they are combined with knowledge elements in computational intelligence systems. It is devoted to the application of modern heuristic optimization techniques to power systems. The chapter is composed of various optimization techniques applied power systems: evolutionary algorithms (EAs), genetic algorithm (GA), particle swarm optimization (PSO), ant colony search algorithm, immune algorithm (IA), simulated annealing (SA), and the tabu search (TS). It shows that these heuristic techniques can solve very complex large‐scale nonlinear optimization problems, which cannot be handled by any analytic approaches. Mutation randomly perturbs a candidate solution; recombination randomly mixes their parts to form a novel solution; reproduction replicates the most successful solutions found in a population; whereas, selection purges poor solutions from a population. This process produces advanced generations with candidates that are successively better suited to their environment.
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