Solving Dynamic Constraint Optimization Problems Using ICHEA

Anurag Sharma, Dharmendra Sharma

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

5 Citations (Scopus)


Many real-world constrained problems have a set of predefined static constraints that can be solved by evolutionary algorithms (EAs) whereas some problems have dynamic constraints that may change over time or may be received by the problem solver at run time. Recently there has been some interest in academic research for solving continuous dynamic constraint optimization problems (DCOPs) where some new benchmark problems have been proposed. Intelligent constraint handling evolutionary algorithm (ICHEA) is demonstrated to be a versatile constraints guided EA for continuous constrained problems which efficiently solves constraint satisfaction problems (CSPs) in [22], constraint optimization problems (COPs) in [23] and dynamic constraint satisfaction problems (DCSPs) in [24]. We investigate efficiency of ICHEA in solving benchmark DCOPs and compare and contrast its performance with other well-known EAs.
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2012)
Subtitle of host publicationLecture Notes in Computer Science
EditorsTingwen Huang, Zhigang Zeng, Chuandong Li, C.S. Leung
Place of PublicationBerlin
Number of pages11
ISBN (Print)9783642344862
Publication statusPublished - 2012
Event19th International Conference on Neural Information Processing 2012 - Doha, Doha, Qatar
Duration: 12 Nov 201215 Nov 2012


Conference19th International Conference on Neural Information Processing 2012


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