2015-07-15
AUTHORSOlympia Roeva , Stefka Fidanova , Marcin Paprzycki
ABSTRACTIn this paper, the recently proposed approach for multicriteria decision making—InterCriteria Analysis (ICA)—is presented. The approach is based on the apparatus of the index matrices and the intuitionistic fuzzy sets. The idea of InterCriteria Analysis is applied to establish the relations and dependencies of considered parameters based on different criteria referred to various metaheuristic algorithms. A hybrid scheme using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) is used for parameter identification of E. coli MC4110 fed-batch cultivation process model. In the hybrid GA-ACO, the GA is used to find feasible solutions to the considered optimization problem. Further ACO exploits the information gathered by GA. This process obtains a solution, which is at least as good as—but usually better than—the best solution devised by GA. Moreover, a comparison with both the conventional GA and ACO identification results is presented. Based on ICA the obtained results are examined and conclusions about existing relations and dependencies between model parameters of the E. coli process and algorithms parameters and outcomes, such as number of individuals, number of generations, value of the objective function and computational time, are discussed. More... »
PAGES107-126
Recent Advances in Computational Optimization
ISBN
978-3-319-21132-9
978-3-319-21133-6
http://scigraph.springernature.com/pub.10.1007/978-3-319-21133-6_7
DOIhttp://dx.doi.org/10.1007/978-3-319-21133-6_7
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1017250254
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Mathematical Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0103",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Numerical and Computational Mathematics",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Science, Sofia, Bulgaria",
"id": "http://www.grid.ac/institutes/grid.493309.4",
"name": [
"Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Science, Sofia, Bulgaria"
],
"type": "Organization"
},
"familyName": "Roeva",
"givenName": "Olympia",
"id": "sg:person.015745057111.08",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015745057111.08"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Institute of Information and Communication Technology, Bulgarian Academy of Science, Sofia, Bulgaria",
"id": "http://www.grid.ac/institutes/grid.410344.6",
"name": [
"Institute of Information and Communication Technology, Bulgarian Academy of Science, Sofia, Bulgaria"
],
"type": "Organization"
},
"familyName": "Fidanova",
"givenName": "Stefka",
"id": "sg:person.011173106320.18",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011173106320.18"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Systems Research Institute, Polish Academy of Sciences, Warsaw and Management Academy, Warsaw, Poland",
"id": "http://www.grid.ac/institutes/grid.465202.7",
"name": [
"Systems Research Institute, Polish Academy of Sciences, Warsaw and Management Academy, Warsaw, Poland"
],
"type": "Organization"
},
"familyName": "Paprzycki",
"givenName": "Marcin",
"id": "sg:person.014761523751.31",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014761523751.31"
],
"type": "Person"
}
],
"datePublished": "2015-07-15",
"datePublishedReg": "2015-07-15",
"description": "In this paper, the recently proposed approach for multicriteria decision making\u2014InterCriteria Analysis (ICA)\u2014is presented. The approach is based on the apparatus of the index matrices and the intuitionistic fuzzy sets. The idea of InterCriteria Analysis is applied to establish the relations and dependencies of considered parameters based on different criteria referred to various metaheuristic algorithms. A hybrid scheme using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) is used for parameter identification of E. coli MC4110 fed-batch cultivation process model. In the hybrid GA-ACO, the GA is used to find feasible solutions to the considered optimization problem. Further ACO exploits the information gathered by GA. This process obtains a solution, which is at least as good as\u2014but usually better than\u2014the best solution devised by GA. Moreover, a comparison with both the conventional GA and ACO identification results is presented. Based on ICA the obtained results are examined and conclusions about existing relations and dependencies between model parameters of the E. coli process and algorithms parameters and outcomes, such as number of individuals, number of generations, value of the objective function and computational time, are discussed.",
"editor": [
{
"familyName": "Fidanova",
"givenName": "Stefka",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-319-21133-6_7",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-3-319-21132-9",
"978-3-319-21133-6"
],
"name": "Recent Advances in Computational Optimization",
"type": "Book"
},
"keywords": [
"Ant Colony Optimization",
"genetic algorithm",
"considered optimization problem",
"InterCriteria Analysis",
"optimization problem",
"conventional genetic algorithm",
"parameter identification",
"metaheuristic algorithms",
"objective function",
"computational time",
"colony optimization",
"model parameters",
"feasible solution",
"intuitionistic fuzzy sets",
"hybrid scheme",
"hybrid algorithm",
"GA-ACO",
"fuzzy sets",
"best solution",
"process model",
"algorithm",
"number of generations",
"multicriteria decision",
"identification results",
"index matrix",
"solution",
"parameters",
"optimization",
"different criteria",
"scheme",
"problem",
"dependency",
"approach",
"matrix",
"set",
"number",
"number of individuals",
"information",
"model",
"decisions",
"results",
"function",
"idea",
"analysis",
"relation",
"process",
"criteria",
"generation",
"comparison",
"coli process",
"ICA",
"values",
"time",
"identification",
"apparatus",
"conclusion",
"individuals",
"outcomes",
"paper"
],
"name": "InterCriteria Analysis of ACO and GA Hybrid Algorithms",
"pagination": "107-126",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1017250254"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-319-21133-6_7"
]
}
],
"publisher": {
"name": "Springer Nature",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-319-21133-6_7",
"https://app.dimensions.ai/details/publication/pub.1017250254"
],
"sdDataset": "chapters",
"sdDatePublished": "2022-05-10T10:53",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/chapter/chapter_429.jsonl",
"type": "Chapter",
"url": "https://doi.org/10.1007/978-3-319-21133-6_7"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-21133-6_7'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-21133-6_7'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-21133-6_7'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-21133-6_7'
This table displays all metadata directly associated to this object as RDF triples.
139 TRIPLES
23 PREDICATES
84 URIs
77 LITERALS
7 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/978-3-319-21133-6_7 | schema:about | anzsrc-for:01 |
2 | ″ | ″ | anzsrc-for:0103 |
3 | ″ | schema:author | N94860c0830164645b59d295d14774bb1 |
4 | ″ | schema:datePublished | 2015-07-15 |
5 | ″ | schema:datePublishedReg | 2015-07-15 |
6 | ″ | schema:description | In this paper, the recently proposed approach for multicriteria decision making—InterCriteria Analysis (ICA)—is presented. The approach is based on the apparatus of the index matrices and the intuitionistic fuzzy sets. The idea of InterCriteria Analysis is applied to establish the relations and dependencies of considered parameters based on different criteria referred to various metaheuristic algorithms. A hybrid scheme using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) is used for parameter identification of E. coli MC4110 fed-batch cultivation process model. In the hybrid GA-ACO, the GA is used to find feasible solutions to the considered optimization problem. Further ACO exploits the information gathered by GA. This process obtains a solution, which is at least as good as—but usually better than—the best solution devised by GA. Moreover, a comparison with both the conventional GA and ACO identification results is presented. Based on ICA the obtained results are examined and conclusions about existing relations and dependencies between model parameters of the E. coli process and algorithms parameters and outcomes, such as number of individuals, number of generations, value of the objective function and computational time, are discussed. |
7 | ″ | schema:editor | N2c6d9e795d744bb18af61223cdf05b4c |
8 | ″ | schema:genre | chapter |
9 | ″ | schema:inLanguage | en |
10 | ″ | schema:isAccessibleForFree | false |
11 | ″ | schema:isPartOf | N7327a9df6cf54ba5850db7bfad7129ad |
12 | ″ | schema:keywords | Ant Colony Optimization |
13 | ″ | ″ | GA-ACO |
14 | ″ | ″ | ICA |
15 | ″ | ″ | InterCriteria Analysis |
16 | ″ | ″ | algorithm |
17 | ″ | ″ | analysis |
18 | ″ | ″ | apparatus |
19 | ″ | ″ | approach |
20 | ″ | ″ | best solution |
21 | ″ | ″ | coli process |
22 | ″ | ″ | colony optimization |
23 | ″ | ″ | comparison |
24 | ″ | ″ | computational time |
25 | ″ | ″ | conclusion |
26 | ″ | ″ | considered optimization problem |
27 | ″ | ″ | conventional genetic algorithm |
28 | ″ | ″ | criteria |
29 | ″ | ″ | decisions |
30 | ″ | ″ | dependency |
31 | ″ | ″ | different criteria |
32 | ″ | ″ | feasible solution |
33 | ″ | ″ | function |
34 | ″ | ″ | fuzzy sets |
35 | ″ | ″ | generation |
36 | ″ | ″ | genetic algorithm |
37 | ″ | ″ | hybrid algorithm |
38 | ″ | ″ | hybrid scheme |
39 | ″ | ″ | idea |
40 | ″ | ″ | identification |
41 | ″ | ″ | identification results |
42 | ″ | ″ | index matrix |
43 | ″ | ″ | individuals |
44 | ″ | ″ | information |
45 | ″ | ″ | intuitionistic fuzzy sets |
46 | ″ | ″ | matrix |
47 | ″ | ″ | metaheuristic algorithms |
48 | ″ | ″ | model |
49 | ″ | ″ | model parameters |
50 | ″ | ″ | multicriteria decision |
51 | ″ | ″ | number |
52 | ″ | ″ | number of generations |
53 | ″ | ″ | number of individuals |
54 | ″ | ″ | objective function |
55 | ″ | ″ | optimization |
56 | ″ | ″ | optimization problem |
57 | ″ | ″ | outcomes |
58 | ″ | ″ | paper |
59 | ″ | ″ | parameter identification |
60 | ″ | ″ | parameters |
61 | ″ | ″ | problem |
62 | ″ | ″ | process |
63 | ″ | ″ | process model |
64 | ″ | ″ | relation |
65 | ″ | ″ | results |
66 | ″ | ″ | scheme |
67 | ″ | ″ | set |
68 | ″ | ″ | solution |
69 | ″ | ″ | time |
70 | ″ | ″ | values |
71 | ″ | schema:name | InterCriteria Analysis of ACO and GA Hybrid Algorithms |
72 | ″ | schema:pagination | 107-126 |
73 | ″ | schema:productId | N28cd3ad8adbf48449398a9c94f9d0c2a |
74 | ″ | ″ | Nba8a5b7b17264fe28ceda0e749edeeaa |
75 | ″ | schema:publisher | N4d166c4b4384405d82931888cdc7d7b8 |
76 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1017250254 |
77 | ″ | ″ | https://doi.org/10.1007/978-3-319-21133-6_7 |
78 | ″ | schema:sdDatePublished | 2022-05-10T10:53 |
79 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
80 | ″ | schema:sdPublisher | N6014154c25ef4f0bb62a9e40a27e0c0a |
81 | ″ | schema:url | https://doi.org/10.1007/978-3-319-21133-6_7 |
82 | ″ | sgo:license | sg:explorer/license/ |
83 | ″ | sgo:sdDataset | chapters |
84 | ″ | rdf:type | schema:Chapter |
85 | N28cd3ad8adbf48449398a9c94f9d0c2a | schema:name | doi |
86 | ″ | schema:value | 10.1007/978-3-319-21133-6_7 |
87 | ″ | rdf:type | schema:PropertyValue |
88 | N2c6d9e795d744bb18af61223cdf05b4c | rdf:first | Ne5f23ab19d744533bca016942576bd41 |
89 | ″ | rdf:rest | rdf:nil |
90 | N4d166c4b4384405d82931888cdc7d7b8 | schema:name | Springer Nature |
91 | ″ | rdf:type | schema:Organisation |
92 | N6014154c25ef4f0bb62a9e40a27e0c0a | schema:name | Springer Nature - SN SciGraph project |
93 | ″ | rdf:type | schema:Organization |
94 | N6f09df2d529d43be8f89b02e9b40ec94 | rdf:first | sg:person.014761523751.31 |
95 | ″ | rdf:rest | rdf:nil |
96 | N7327a9df6cf54ba5850db7bfad7129ad | schema:isbn | 978-3-319-21132-9 |
97 | ″ | ″ | 978-3-319-21133-6 |
98 | ″ | schema:name | Recent Advances in Computational Optimization |
99 | ″ | rdf:type | schema:Book |
100 | N90fb4bee4d8e4481b5d90185b0a657f0 | rdf:first | sg:person.011173106320.18 |
101 | ″ | rdf:rest | N6f09df2d529d43be8f89b02e9b40ec94 |
102 | N94860c0830164645b59d295d14774bb1 | rdf:first | sg:person.015745057111.08 |
103 | ″ | rdf:rest | N90fb4bee4d8e4481b5d90185b0a657f0 |
104 | Nba8a5b7b17264fe28ceda0e749edeeaa | schema:name | dimensions_id |
105 | ″ | schema:value | pub.1017250254 |
106 | ″ | rdf:type | schema:PropertyValue |
107 | Ne5f23ab19d744533bca016942576bd41 | schema:familyName | Fidanova |
108 | ″ | schema:givenName | Stefka |
109 | ″ | rdf:type | schema:Person |
110 | anzsrc-for:01 | schema:inDefinedTermSet | anzsrc-for: |
111 | ″ | schema:name | Mathematical Sciences |
112 | ″ | rdf:type | schema:DefinedTerm |
113 | anzsrc-for:0103 | schema:inDefinedTermSet | anzsrc-for: |
114 | ″ | schema:name | Numerical and Computational Mathematics |
115 | ″ | rdf:type | schema:DefinedTerm |
116 | sg:person.011173106320.18 | schema:affiliation | grid-institutes:grid.410344.6 |
117 | ″ | schema:familyName | Fidanova |
118 | ″ | schema:givenName | Stefka |
119 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011173106320.18 |
120 | ″ | rdf:type | schema:Person |
121 | sg:person.014761523751.31 | schema:affiliation | grid-institutes:grid.465202.7 |
122 | ″ | schema:familyName | Paprzycki |
123 | ″ | schema:givenName | Marcin |
124 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014761523751.31 |
125 | ″ | rdf:type | schema:Person |
126 | sg:person.015745057111.08 | schema:affiliation | grid-institutes:grid.493309.4 |
127 | ″ | schema:familyName | Roeva |
128 | ″ | schema:givenName | Olympia |
129 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015745057111.08 |
130 | ″ | rdf:type | schema:Person |
131 | grid-institutes:grid.410344.6 | schema:alternateName | Institute of Information and Communication Technology, Bulgarian Academy of Science, Sofia, Bulgaria |
132 | ″ | schema:name | Institute of Information and Communication Technology, Bulgarian Academy of Science, Sofia, Bulgaria |
133 | ″ | rdf:type | schema:Organization |
134 | grid-institutes:grid.465202.7 | schema:alternateName | Systems Research Institute, Polish Academy of Sciences, Warsaw and Management Academy, Warsaw, Poland |
135 | ″ | schema:name | Systems Research Institute, Polish Academy of Sciences, Warsaw and Management Academy, Warsaw, Poland |
136 | ″ | rdf:type | schema:Organization |
137 | grid-institutes:grid.493309.4 | schema:alternateName | Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Science, Sofia, Bulgaria |
138 | ″ | schema:name | Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Science, Sofia, Bulgaria |
139 | ″ | rdf:type | schema:Organization |