Comparison of Constraint Handling Approaches in Multi-objective Optimization

  • Rohan Hemansu Chhipa
  • Mardé HelbigEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)


When considering real-world optimization problems the possibility of encountering problems having constraints is quite high. Constraint handling approaches such as the penalty function and others have been researched and developed to incorporate an optimization problem’s constraints into the optimization process. With regards to multi-objective optimization, in this paper the two main approaches of incorporating constraints are explored, namely: Penalty functions and dominance based selection operators. This paper aims to measure the effectiveness of these two approaches by comparing the empirical results produced by each approach. Each approach is tested using a set of ten benchmark problems, where each problem has certain constraints. The analysis of the results in this paper showed no overall statistical difference between the effectiveness of penalty functions and dominance based selection operators. However, significant statistical differences between the constraint handling approaches were found with regards to specific performance indicators.


Constrained multi-objective optimization Pareto dominance Penalty functions 



This work is based on the research supported by the National Research Foundation (NRF) of South Africa (Grant Number 46712). The opinions, findings and conclusions or recommendations expressed in this article is that of the author(s) alone, and not that of the NRF. The NRF accepts no liability whatsoever in this regard.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of PretoriaPretoriaSouth Africa

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