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KU Battle of Metaheuristic Optimization Algorithms 2: Performance Test

  • Joong Hoon Kim
  • Young Hwan Choi
  • Thi Thuy Ngo
  • Jiho Choi
  • Ho Min Lee
  • Yeon Moon Choo
  • Eui Hoon Lee
  • Do Guen Yoo
  • Ali Sadollah
  • Donghwi Jung
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 382)

Abstract

In the previous companion paper, six new/improved metaheuristic optimization algorithms developed by members of Hydrosystem laboratory in Korea University (KU) are introduced. The six algorithms are Cancer Treatment Algorithm (CTA), Extraordinary Particle Swarm Optimization (EPSO), Improved Cluster HS (ICHS), Multi-Layered HS (MLHS), Sheep Shepherding Algorithm (SSA), and Vision Correction Algorithm (VCA). The six algorithms are tested and compared through six well-known unconstrained benchmark functions and a pipe sizing problem of water distribution network. Performance measures such as mean, best, and worst solutions (under given maximum number of function evaluations) are used for the comparison. Optimization results are obtained from thirty independent optimization trials. Obtained Results show that some of the newly developed/improved algorithms show superior performance with respect to mean, best, and worst solutions when compared to other existing algorithms.

Keywords

Cancer treatment algorithm Extraordinary particle swarm optimization Improved cluster HS Multi-Layered HS Sheep shepherding algorithm Vision correction algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Joong Hoon Kim
    • 1
  • Young Hwan Choi
    • 1
  • Thi Thuy Ngo
    • 1
  • Jiho Choi
    • 1
  • Ho Min Lee
    • 1
  • Yeon Moon Choo
    • 1
  • Eui Hoon Lee
    • 1
  • Do Guen Yoo
    • 2
  • Ali Sadollah
    • 2
  • Donghwi Jung
    • 2
  1. 1.School of Civil, Environmental and Architectural EngineeringKorea UniversitySeoulSouth Korea
  2. 2.Research Center for Disaster Prevention Science and TechnologyKorea UniversitySeoulSouth Korea

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