Fuzziness in Ant Colony Optimization and Their Applications

Fuzziness in Ant Colony Optimization and Their Applications

Deepthi P. Hudedagaddi (VIT University, India) and B. K. Tripathy (VIT University, India)
DOI: 10.4018/978-1-5225-2857-9.ch018
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Abstract

Nature-inspired algorithms are still at a very early stage with a relatively short history, comparing with many traditional, well-established methods. Metaheuristics, in their original definition, are solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of a solution space. One major algorithm is Ant Colony Optimization which has been applied in varied domains to better the performance. Fuzzy Linear Programming models and methods has been one of the most and well-studied topics inside the broad area of Soft Computing. Its applications as well as practical realizations can be found in all the real-world areas. Here we wish to introduce how fuzziness can be included in a nature inspired algorithm like ant colony optimization and thereby enhance its functionality. Several applications of ACO with fuzzy concepts will be introduced in the chapter.
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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).

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