Development of firefly algorithm via chaotic sequence and population diversity to enhance the image contrast

Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

“…The metaheuristic bioinspired algorithms have proved their proficiency in solving complex real world application problems in versatile fields of Engineering like language recognition using firefly algorithm [3], in speech recognition where the parameters of a fuzzy neural network are optimized using Firefly algorithm [13] in electronics circuit design, in problems of traffic optimization using popular metaheuristics like GA, DE, ACO, GP(genetic programming), ABC etc. [36], in enhancing the image contrast using FA with chaotic sequence and data classification using ACO [8,52], in healthcare the firefly model is used for Parkinson's disease diagnosis and classification [5][6][7], in Robotics, where swarm based Glow worm optimization algorithm with multimodal functions was used for collective robotics applications similarly GA and PSO Algorithms were used for Intelligent Robot Path Optimization [16,59]. Gradually with wider use of metaheuristics it was observed that these algorithms are not suitable for solving all kinds of problems and a specific metaheuristic algorithm shows excellent performance in solving a particular problem whereas the same algorithm shows worst performance in solving another type of problem, therefore one or a few metaheuristic algorithms cannot be standardized to get optimized solutions for all types of problems.…”

“…This algorithm is inspired by the swarming and flashing light characteristics of the fireflies. In the summer night group of fireflies in the sky produce flashing light for two fundamental reasons, to attract their partners for mating and to protect themselves from potential predators [8]. However, the flashing lights follow two physical laws: first, the light intensity (I) is inversely proportional to the distance (r) in the form of Iα1/r2 light intensity deceases as the distance increases and second, the intensity of light exponentially decreases due to absorption of light in the air.…”

“…The binomial crossover is defined as in equation (8), Where k is a randomly generated natural number in the range {1, 2, ⋯ , d}, rand i,j is a randomly generated real number that varies in between [0, 1]. Next, the exponential crossover, we first need to choose a random integer number (n) between {1, 2, ⋯ , d} .…”

“…[11,16]. Many versions of Firefly algorithms are applied for solving various problems in the fields like cryptanalysis [18], graph colouring [17], speech reorganization [3], for improving the speed of convergence, for diagnosing Parkinson's disease, for feature selection [6][7][8] and to provide microarray data for cancer prediction [1,3,7]. Similarly, the Differential Evolution (DE) algorithm is based on the evolutionary principle of survival of the fittest; it's a very popular algorithm, proving its excellence in global optimization, at the same time hybridization of DE with several other algorithms have provided excellent results [29].…”

A Firefly Algorithm With Self-Adaptive Population Size for Global Path Planning of Mobile Robot

“…Some scholars [19][20][21] have explored the influence of population size on firefly algorithm through comparative experiments of multiple groups of different population sizes in specific optimization problems. It is worth noting that the population size in these literatures did not change adaptively during the operation of the algorithm.…”