A Multi-Objective Self-Adaptive Differential Evolution Algorithm for Conceptual High-Rise Building Design

Berk Ekici, Ioannis Chatzikonstantinou, Sevil Sariyildiz, Mehmet Fatih Tasgetiren, Quan Ke Pan

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

9 Citations (Scopus)


This paper presents a multi-objective self-adaptive differential evolution algorithm to solve the form-finding problem of high-rise building design in the conceptual phase. The aim of the research is to reach suitable high-rise design alternatives for hard and soft objectives, which are construction cost per square meter, structural displacement, and visual perception of the spaces from the inside out subject to several constraints that are related with both high-rise construction regulations, and profitability of the spaces. We formulate the problem as a multi-objective realparameter constrained optimization problem for three objectives that are inherently conflicting. To tackle this problem, we developed two different optimization algorithms, namely, a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and a Self-Adaptive Differential Evolution Algorithm (jDE) in order to obtain Pareto fronts with diversified non-dominated solutions. The extensive computational results show that the jDE algorithm yields much more desirable Pareto front than the NSGA-II algorithm.

Original languageEnglish
Title of host publication2016 IEEE Congress on Evolutionary Computation, CEC 2016
Place of PublicationPiscataway
Number of pages8
ISBN (Electronic)9781509006229
Publication statusPublished - 2016
Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016


Conference2016 IEEE Congress on Evolutionary Computation, CEC 2016
Abbreviated titleCEC 2016


  • Computational design
  • Evolutionary computation
  • High-rise optimization
  • Multi-objective optimization
  • Performance-based design

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