Landscape-Based Differential Evolution for Constrained Optimization Problems

Karam Sallam, Saber Elsayed, Ruhul Sarker, Daryl Essam

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

6 Citations (Scopus)


Over the last two decades, many different differential evolution (DE) variants have been developed for solving constrained optimization problems. However, none of them performs consistently when solving different types of problems. To deal with this drawback, multiple search operators are used under a single DE algorithm structure where a higher selection pressure is placed on the best performing operator during the evolutionary process. In this paper, we propose to use the landscape information of the problem in the design of the selection mechanism. The performance of this algorithm with the proposed selection mechanism is analysed by solving 10 real-world constrained optimization problems. The experimental results revealed that the proposed algorithm is capable of producing high quality solutions compared to those of state-of-the-art algorithms.
Original languageEnglish
Title of host publication2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
EditorsFernando Von Zuben, Gary Yen, Hisao Ishibuchi
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781509060177
ISBN (Print)9781509060184
Publication statusPublished - Jul 2018
Externally publishedYes
Event2018 IEEE Congress on Evolutionary Computation (CEC) - Rio de Janeiro
Duration: 8 Jul 201813 Jul 2018


Conference2018 IEEE Congress on Evolutionary Computation (CEC)


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