Because resources, time and money are always limited in real world applications, we have to find solutions to optimally use these valuable resources. To solve most real world
optimization problems we need sophisticated optimization tools. Nature inspired meta-heuristic algorithms are among the most widely used algorithms for optimization. Firefly algorithm is one of these algorithms.
In this work optimization algorithms are analyzed from traditional methods to modern meta-heuristic algorithms, with an emphasis on nature inspired algorithms. This work is attempts to present the history and applications of these algorithms.
The first chapter introduces algorithms and analyzes the essence of the algorithm. Then the general formulation of an optimization problem is discussed and modern approaches in terms of swarm intelligence. A brief history of nature inspired algorithms is reviewed. The second chapter analyzes the key components nature inspired algorithms in terms of their evolutionary operators and functionalities. The main aim is to provide an overview of these algorithms. In the third chapter the standard firefly algorithm is introduced and then the variants are briefly reviewed. The characteristics of firefly algorithm are also analyzed. The forth chapter presents the implementation of firefly algorithm in solving the economic dispatch problem by minimizing the fuel cost and considering the generator limits and transmission losses. This is followed by a short review of applications nature inspired algorithms in power systems.