Hybrid Directional-Biased Evolutionary Algorithm for Multi-Objective Optimization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Tomohiro Shimada , Masayuki Otani , Hiroyasu Matsushima , Hiroyuki Sato , Kiyohiko Hattori , Keiki Takadama

ABSTRACT

This paper proposes the hybrid Indicator-based Directionalbiased Evolutionary Algorithm (hIDEA) and verifies its effectiveness through the simulations of the multi-objective 0/1 knapsack problem. Although the conventional Multi-objective Optimization Evolutionary Algorithms (MOEAs) regard the weights of all objective functions as equally, hIDEA biases the weights of the objective functions in order to search not only the center of true Pareto optimal solutions but also near the edges of them. Intensive simulations have revealed that hIDEA is able to search the Pareto optimal solutions widely and accurately including the edge of true ones in comparison with the conventional methods. More... »

PAGES

121-130

Book

TITLE

Parallel Problem Solving from Nature, PPSN XI

ISBN

978-3-642-15870-4
978-3-642-15871-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-15871-1_13

DOI

http://dx.doi.org/10.1007/978-3-642-15871-1_13

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033866015


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