Title :
Oppositional biogeography-based optimization for combinatorial problems
Author :
Ergezer, Mehmet ; Simon, Dan
Author_Institution :
Cleveland State Univ., Cleveland, OH, USA
Abstract :
In this paper, we propose a framework for employing opposition-based learning to assist evolutionary algorithms in solving discrete and combinatorial optimization problems. To our knowledge, this is the first attempt to apply opposition to combinatorics. We introduce two different methods of opposition to solve two different type of combinatorial optimization problems. The first technique, open-path opposition, is suited for combinatorial problems where the final node in the graph does not have be connected to the first node, such as the graph coloring problem. The latter technique, circular opposition, can be employed for problems where the endpoints of a graph are linked, such as the well-known traveling salesman problem (TSP). Both discrete opposition methods have been hybridized with biogeography-based optimization (BBO). Simulations on TSP benchmarks illustrate that incorporating opposition into BBO improves its performance.
Keywords :
evolutionary computation; graph colouring; learning (artificial intelligence); problem solving; travelling salesman problems; TSP; circular opposition; combinatorial problems; discrete opposition methods; evolutionary algorithms; graph coloring problem; open-path opposition; opposition-based learning; oppositional biogeography; optimization; problem solving; traveling salesman problem; Benchmark testing; Biogeography; Cities and towns; Evolutionary computation; Greedy algorithms; Optimization; Traveling salesman problems; Biogeography-based optimization; combinatorics; discrete optimization; evolutionary algorithms; graph-coloring problem; opposition; traveling salesman problem;
Conference_Titel :
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location :
New Orleans, LA
Print_ISBN :
978-1-4244-7834-7
DOI :
10.1109/CEC.2011.5949792