Optimization is an age-old problem in mathematics and applied mathematics. During the last decade, due to the convergence of bio, nano, and information technology, an appreciable amount of research has been done on nature-inspired computing for solving various optimization problems. Initially, nature-inspired computing included small number of tools like Evolutionary Computing (EC), Neural Networks (NN), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Gradually, other forms of intelligence were added to nature-inspired computing, such as artificial bee colony optimization, cuckoo search, firefly algorithm, bacteria foraging optimization algorithm, intelligent water drop optimization, gravitational search, and harmony search along with many more. These computational techniques use biological or evolutionary processes as the main component for the efficient design and implementation of the intelligent optimization algorithms.

Nature-inspired computing can be applied to a variety of problems, ranging from engineering and scientific research to manufacturing, industry and business as they provide robust and flexible solutions along with learning and adaptability compared to traditional optimization techniques. It can be applied to solve optimization issues in any domain and may be unified as Nature-Inspired Computing and Optimization (NICO).

This Special Issue welcomes original research and review articles looking at the emergence of various techniques based on ants, bees, flocks of birds, frogs, cuckoos, fireflies, bats, crocodiles and so on. We are confident that the field of nature-inspired computing and optimization can solve many complex real-world problems, arising from different fields such as operations research, management science, computer science, engineering design, financial engineering, production planning, economics, and biological science etc.

Potential topics include but are not limited to the following:

• Theory & algorithm of optimization
• Portfolio, fuzzy, ant colony or bee colony optimization
• Multi-objective programming
• Bacterial foraging optimization
• Particle swarm optimization
• Artificial immune systems
• Human brain function inspired algorithms
• Iterative Local Search (ILS)
• Guided Local Search (GLS)
• Genetic Algorithms (GA)
• Scatter Search (SS)
• Variable Neighborhood Search (VNS)
• Greedy Randomized Adaptive Search Procedure (GRASP)