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<ArticleSet>
<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume>26</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2019</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>The application of multi-objective charged system search algorithm for optimization problems</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1249</FirstPage>
			<LastPage>1265</LastPage>
			<ELocationID EIdType="pii">20184</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2018.20184</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>A.</FirstName>
					<LastName>Ranjbar</LastName>
<Affiliation>Department of GIS Engineering, Faculty of Surveying Engineering, Tehran University, Tehran, Iran.</Affiliation>

</Author>
<Author>
					<FirstName>S.</FirstName>
					<LastName>Talatahari</LastName>
<Affiliation>Department of Civil Engineering, University of Tabriz, Tabriz, Iran</Affiliation>

</Author>
<Author>
					<FirstName>F.</FirstName>
					<LastName>Hakimpour</LastName>
<Affiliation>Department of GIS Engineering, Faculty of Surveying Engineering, Tehran University, Tehran, Iran.</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2017</Year>
					<Month>07</Month>
					<Day>11</Day>
				</PubDate>
			</History>
		<Abstract>The charged system search algorithm is a relatively new optimization algorithm developed based on some principles from physics and mechanics. This paper presents an approach in which Pareto dominance is incorporated into the charged system search in order to allow this algorithm to handle problems with some multi-objective functions; the proposed algorithm will be called Multi-Objective Charged System Search (MOCSS). Well-known mathematical and engineering benchmarks are used to evaluate the proposed algorithm and the results have been compared with other new approaches. The results of implementing the new algorithm on some test problems show that the proposed algorithm outperforms the other algorithms in terms of Generational Distance, Maximum Spread, Spacing, Coverage of two Set and Hypervolume Indicator. Results of well-known mathematical examples indicate that the new approach is highly competitive and can be considered as a viable alternative to solve multi-objective optimization problems. These results encourage the application of the proposed method to more complex and real-world multi-objective optimization problems. The proposed method can deal with highly nonlinear problems with complex constraints and diverse Pareto optimal sets.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Charged System Search</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Meta-heuristics</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">multi-objective optimization</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Pareto optimal</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">and Multi-Objective Charged System Search</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_20184_cf1ae2b8cd5546d758974b62edfd8d9c.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
