Special issue on modern search heuristics and applications

Since its inception in the early 1980s, we have seen a lot of exciting developments in the field of metaheuristics. The complexity of many real-world problems, which are often associated with large search spaces, real-time performance demands and dynamic environments, has made exact solution methods impractical to solve them within a reasonable amount of time. This gives rise to various types of non-exact metaheuristic approaches, including the nature-inspired and non nature-inspired ones (see [1,2,3,4]). In general, metaheuristics can be viewed as higher level frameworks aimed at efficiently and effectively exploring a search space [5]. Unlike conventional methods which assume that the objective functions can be solved mathematically, metaheuristics typically do not make much assumption about the problem to be solved or the underlying search space. This makes them applicable to a wide domain of tasks where little information is known about the characteristics of the utility measure. Among the most wellknown metaheuristic approaches are those based on the process of natural selection, such as Genetic Algorithms (GA), Genetic Programming (GP), Evolution Strategies (ES), Evolutionary Programming (EP) and Differential Evolution (DE). Other popular metaheuristics include Simulated Annealing that takes inspiration from physics and Swarm Intelligence algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) that imitate the social behaviour of ants or birds. Scatter Search and Tabu Search are examples of non nature-inspired metaheuristics. These metaheuristics have been applied to areas as diverse as chemistry, computer graphics and visual arts, computer security, data mining, distributed systems, learning and teaching, economics and finance, engineering, health care, telecommunication net-