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                <identifier>ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:5670</identifier>
                <datestamp>2018-04-16T12:21:01Z</datestamp>
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                    <dim:field mdschema="dc" element="title" lang="en">Hybridized Monarch Butterfly Algorithm for Global Optimization Problems</dim:field>
                    <dim:field mdschema="dc" element="date" qualifier="issued">2018</dim:field>
                    <dim:field mdschema="dc" element="identifier" qualifier="uri">http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/5670</dim:field>
                    <dim:field mdschema="dc" element="identifier" qualifier="uri">http://www.iaras.org/iaras/filedownloads/ijc/2018/006-0011(2018).pdf</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="orcid::0000-0002-1154-6696" confidence="-1">I. Strumberger</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="orcid::0000-0001-8241-2778" confidence="-1">M. Sarac</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="orcid::0000-0002-7865-2135" confidence="-1">D. Markovic</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="orcid::0000-0002-2062-924X" confidence="-1">N. Bacanin</dim:field>
                    <dim:field mdschema="dc" element="description" qualifier="abstract">This paper introduces hybridized monarch butterfly optimization algorithm for solving global optimization
problems. Despite of the fact that the monarch butterfly optimization algorithm is relatively new approach,
it has already showed great potential when tackling NP-hard optimization tasks. However, by analyzing original
monarch butterfly algorithm, we noticed some deficiencies in the butterfly adjusting operator that in early iterations
exceedingly directs the search process towards the current best solution. To overcome this deficiency, we
incorporated firefly’s algorithm search mechanism into the original monarch optimization approach. We tested
our algorithm on six standard global optimization benchamarks, and performed comparative analysis with original
monarch butterfly optimization, as well as with other five state-of-the-art metaheuristics. Experimental results are
promising.</dim:field>
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                    <dim:field mdschema="dc" element="citation" qualifier="volume">3</dim:field>
                    <dim:field mdschema="dc" element="citation" qualifier="spage">63</dim:field>
                    <dim:field mdschema="dc" element="citation" qualifier="epage">68</dim:field>
                    <dim:field mdschema="dc" element="identifier" qualifier="issn">2367-8895</dim:field>
                    <dim:field mdschema="dc" element="source">International Journal of Computers </dim:field>
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