Fuzziness in Ant Colony Optimization and Their Applications

2. Metaheuristics

The metaheuristics are by far the most popular and define mechanisms for developing an evolution in the search space of the sets of solutions in order to come close to the ideal solution with elements which will survive in successive generations of populations. In the context of soft computing, the hybridizations (Dubois et al, 1990) which take these metaheuristics as a reference are fundamental. A very active area of research is the design of nature-inspired metaheuristics. Many recent metaheuristics, especially evolutionary computation-based algorithms, are inspired by natural systems. Such metaheuristics include Ant colony optimization, particle swarm optimization, cuckoo search, and artificial bee colony to cite a few. Metaheuristics are used for combinatorial optimization in which an optimal solution is sought over a discrete search-space. An example problem is the travelling salesman problem where the search-space of candidate solutions grows faster than exponentially as the size of the problem increases, which makes an exhaustive search for the optimal solution infeasible. Additionally, multidimensional combinatorial problems, including most design problems in engineering (Kirkpatrick, 1983; Holland, 1976; Glover 1977) such as form-finding and behavior-finding, suffer from the curse of dimensionality, which also makes them infeasible for exhaustive search or analytical methods. Popular metaheuristics for combinatorial problems includesimulated annealing by Kirkpatrick et al., genetic algorithms by Holland et al., scatter search (Glover, 1986) and tabu search (Robbins, 1951) by Glover. Literature review on metaheuristic optimization, (Barricelli, 1954) suggested that it was Fred Glover who coined the word metaheuristics (Rastrigin, 1963).