Using Different Approaches to Approximate a Pareto Front for a Multiobjective Evolutionary Algorithm: Optimal Thinning Regimes forEucalyptus fastigata | Zendy

Oliver Chikumbo | Zendy

Open Access

Using Different Approaches to Approximate a Pareto Front for a Multiobjective Evolutionary Algorithm: Optimal Thinning Regimes forEucalyptus fastigata

Author(s) -

Oliver Chikumbo

Publication year - 2012

Publication title -

international journal of forestry research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.314

H-Index - 8

eISSN - 1687-9376

pISSN - 1687-9368

DOI - 10.1155/2012/189081

Subject(s) - multi objective optimization , sorting , thinning , pareto principle , mathematical optimization , ranking (information retrieval) , mathematics , genetic algorithm , evolutionary algorithm , set (abstract data type) , solution set , algorithm , computer science , artificial intelligence , biology , ecology , programming language

A stand-level, multiobjective evolutionary algorithm (MOEA) for determining a set of efficient thinning regimes satisfying two objectives, that is, value production for sawlog harvesting and volume production for a pulpwood market, was successfully demonstrated for a Eucalyptus fastigata trial in Kaingaroa Forest, New Zealand. The MOEA approximated the set of efficient thinning regimes (with a discontinuous Pareto front) by employing a ranking scheme developed by Fonseca and Fleming (1993), which was a Pareto-based ranking (a.k.a Multiobjective Genetic Algorithm—MOGA). In this paper we solve the same problem using an improved version of a fitness sharing Pareto ranking algorithm (a.k.a Nondominated Sorting Genetic Algorithm—NSGA II) originally developed by Srinivas and Deb (1994) and examine the results. Our findings indicate that NSGA II approximates the entire Pareto front whereas MOGA only determines a subdomain of the Pareto points

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research