Multiobjective Evolutionary Algorithm Based on Nondominated Sorting and Bidirectional Local Search for Big Data

2017 ◽  
Vol 13 (4) ◽  
pp. 1979-1988 ◽  
Author(s):  
Fan Lin ◽  
Jiasong Zeng ◽  
Jianbing Xiahou ◽  
Beizhan Wang ◽  
Wenhua Zeng ◽  
...  
Keyword(s):  
Big Data ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Multiobjective Evolutionary Algorithm ◽  
Nondominated Sorting

scholarly journals Local Search-Inspired Rough Sets for Improving Multiobjective Evolutionary Algorithm

2014 ◽  
Vol 05 (13) ◽  
pp. 1993-2007 ◽  
Author(s):  
Ahmed A. EL-Sawy ◽  
Mohamed A. Hussein ◽  
El-Sayed Mohamed Zaki ◽  
Abd Allah A. Mousa
Keyword(s):  
Local Search ◽  
Evolutionary Algorithm ◽  
Rough Sets ◽  
Multiobjective Evolutionary Algorithm

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

2012 ◽  
Vol 2012 ◽  
pp. 1-27 ◽  
Author(s):  
Oliver Chikumbo
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Nsga Ii ◽  
Multiobjective Evolutionary Algorithm ◽  
Fitness Sharing ◽  
Pareto Ranking ◽  
Ranking Scheme ◽  
Volume Production ◽  
Nondominated Sorting

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 aEucalyptus fastigatatrial 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.

scholarly journals A hybrid multiobjective evolutionary algorithm: Striking a balance with local search

2010 ◽  
Vol 52 (11-12) ◽  
pp. 2048-2059 ◽  
Author(s):  
Chang Wook Ahn ◽  
Eungyeong Kim ◽  
Hyun-Tae Kim ◽  
Dong-Hyun Lim ◽  
Jinung An
Keyword(s):  
Local Search ◽  
Evolutionary Algorithm ◽  
Multiobjective Evolutionary Algorithm
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