Different Approaches for Cooperation with Metaheuristics

Working on artificial intelligence, one of the tasks we can carry on is optimization of the possible solutions of a problem. Optimization problems appear. In optimization problems we search for the best solution, or one good enough, to a problem among a lot of alternatives. Problems we try to solve are usual in daily living. Every person constantly works out optimization problems, e.g. finding the quickest way from home to work taking into account traffic restrictions. Humans can find efficiently solutions to these problems because these are easy enough. Nevertheless, problems can be more complex, for example reducing fuel consumption of a fleet of plains. Computational algorithms are required to tackle this kind of problems. A first approach to solve them is using an exhaustive search. Theoretically, this method always finds the solution, but is not efficient as its execution time grows exponentially. In order to improve this method heuristics were proposed. Heuristics are intelligent techniques, methods or procedures that use expert knowledge to solve tasks; they try to obtain a high performance referring to solution quality and used resources. Metaheuristics, term first used by Fred Glover in 1986 (Glover, 1986), arise to improve heuristics, and can be defined as (Melián, Moreno & Moreno, 2003) ‘intelligent strategies for designing and improving very general heuristic procedures with a high performance’. Since Glover the field has been extensively developed. The current trend is designing new metaheuristics that improve the solution to given problems. However, another line, very interesting, is reuse existing metaheuristics in a coordinated system. In this article we present two different methods following this line.