The Initialization of Evolutionary Multi-objective Optimization Algorithms

Mohammad Hamdan, Osamah Qudah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Evolutionary algorithms are the most widely used meta-heuristics for solving multi objective optimization problems, and since all of these algorithms are population based, such as NSGAII, there are a set of factors that affect the final outcomes of these algorithms such as selection criteria, crossover, mutation and fitness evaluation. Unfortunately, little research sheds light at how to generate the initial population. The common method is to generate the initial population randomly. In this work, a set of initialization methods were examined such as, Latin hypercube sampling (LHS), Quasi-Random sampling and stratified sampling. Nonetheless. We also propose a modified version of Latin Hypercube sampling method called (Quasi_LHS) that uses Quasi random numbers as a backbone in its body. Furthermore, we propose a modified version of Stratified sampling method that uses Quasi-Random numbers to represent the intervals. For our research, a set of well known multi objective optimization problems were used in order to evaluate our initial population strategies using NSGAII algorithm. The results show that the proposed initialization methods (Quasi_LHS) and Quasi-based Stratified improved to some extent the quality of final results of the experiments.

Original languageEnglish
Title of host publicationAdvances in Swarm and Computational Intelligence
EditorsYing Tan, Yuhui Shi, Fernando Buarque, Alexander Gelbukh, Swagatam Das, Andries Engelbrecht
PublisherSpringer International Publishing
Pages495-504
Number of pages10
ISBN (Electronic)9783319204666
ISBN (Print)9783319204659
DOIs
Publication statusPublished - 2015
Event6th International Conference on Swarm Intelligence 2015 - Beijing, China
Duration: 25 Jun 201528 Jun 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
Volume9140
ISSN (Print)0302-9743

Conference

Conference6th International Conference on Swarm Intelligence 2015
Abbreviated titleICSI 2015
CountryChina
CityBeijing
Period25/06/1528/06/15

Keywords

  • Evolutionary algorithms
  • Initial population
  • Latin hypercube sampling
  • NSGAII algorithm
  • Pareto set
  • Quality indicators
  • Quasi random numbers
  • Random numbers
  • Stratified sampling

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Hamdan, M., & Qudah, O. (2015). The Initialization of Evolutionary Multi-objective Optimization Algorithms. In Y. Tan, Y. Shi, F. Buarque, A. Gelbukh, S. Das, & A. Engelbrecht (Eds.), Advances in Swarm and Computational Intelligence (pp. 495-504). (Lecture Notes in Computer Science; Vol. 9140). Springer International Publishing. https://doi.org/10.1007/978-3-319-20466-6_52