Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Firefly optimization (FFO) <s> Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Firefly optimization (FFO) <s> Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed. <s> BIB002 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Firefly optimization (FFO) <s> Efficient and reliable power production is necessary to meet both the profitability of power systems operations and the electricity demand, taking also into account the environmental concerns about the emissions produced by fossil-fuelled power plants. The economic emission load dispatch problem has been defined and applied in order to deal with the optimization of these two conflicting objectives, that is, the minimization of both fuel cost and emission of generating units. This paper introduces and describes a solution to this famous problem using a new metaheuristic nature-inspired algorithm, called firefly algorithm, which was developed by Dr. Xin-She Yang at Cambridge University in 2007. A general formulation of this algorithm is presented together with an analytical mathematical modeling to solve this problem by a single equivalent objective function. The results are compared with those obtained by alternative techniques proposed by the literature in order to show that it is capable of yielding good optimal solutions with proper selection of control parameters. <s> BIB003 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Firefly optimization (FFO) <s> Proposing an extension of firefly algorithm.Employment of picewise chaos, for an further enhanced diversity.Making use of a simple but effective constraint handling method.Making use of an improved local search procedure. Firefly algorithm (FA) is a newer member of bio-inspired meta-heuristics, which was originally proposed to find solutions to continuous optimization problems. Popularity of FA has increased recently due to its effectiveness in handling various optimization problems. To enhance the performance of the FA even further, an adaptive FA is proposed in this paper to solve mechanical design optimization problems, and the adaptivity is focused on the search mechanism and adaptive parameter settings. Moreover, chaotic maps are also embedded into AFA for performance improvement. It is shown through experimental tests that some of the best known results are improved by the proposed algorithm. <s> BIB004
Firefly optimization (FFO) BIB001 is a population-based, metaheuristic algorithm inspired by the social (flashing) and communication behaviour of fireflies. Brighter firefly attracts other fireflies (i.e. the brightness of a firefly indicates the goodness a solution). The attractiveness exponentially decreases based on the distance between them BIB002 BIB004 . The main advantage of FFO is the fact that it uses real random numbers, and it depends on the global communication among the swarming fireflies BIB003 . FA, on the hand, performs a full pair-wise comparison, which might be considerably time consuming BIB004 . In addition to the fact that the light absorption coefficient is widely assumed to be equal to unity. However, as the distance between fireflies increases, fireflies might get trapped in their positions during the repeated evaluation of the algorithm BIB004 .