Biogeography based optimization bbo is an evolutionary algorithm ea that optimizes a function by stochastically and iteratively improving candidate solutions with regard to a given measure of quality or fitness functionbbo belongs to the class of metaheuristics since it includes many variations and since it does not make any assumptions about the problem and can therefore be applied to . This web site is dedicated to biogeography based optimization bbo and related material if you have any papers or software that you would like to be included on this web page please email the information to dan simon at d dot j dot simon at csuohio dot edu. Evolutionary computation algorithms are employed to minimize functions with large number of variables biogeography based optimization bbo is an optimization algorithm that is based on the science of biogeography which researches the migration patterns of species. This dissertation outlines a novel variation of biogeography based optimization bbo which is an evolutionary algorithm ea developed for global optimization the new algorithm employs opposition based learning obl alongside bbo migration to create oppositional bbo ob bo additionally a new opposition method named quasi reflection is introduced. 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
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