Abstract
Nature-inspired algorithms are a set of novel problem-solving methodologies and approaches and have been attracting considerable attention for their good performance. Representative examples of nature-inspired algorithms include artificial neural networks (ANN), fuzzy systems (FS), evolutionary computing (EC), and swarm intelligence (SI), and they have been applied to solve many real-world problems. Despite the popularity of nature-inspired algorithms, many challenges remain which require further research efforts. The contributions presented in this special issue include some latest developments of nature-inspired algorithms, such as genetic algorithm, particle swarm optimization, ant colony optimization, migrating birds optimization, neural networks, gravitational search algorithm, and their applications. Several real-world optimization problems have been studied by several nature-inspired algorithms. In this paper, we are going to see Firefly and Particle swarm optimization.
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