Hybrid ACO-GA for Parameter Identification of an E. coli Cultivation Process Model View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014-06-26

AUTHORS

Olympia Roeva , Stefka Fidanova , Vassia Atanassova

ABSTRACT

The present work offers a novel approach to parameter identification of an E. coli cultivation process model, using a hybrid of two metaheuristics, namely Ant Colony Optimization (ACO) and Genetic Algorithms (GAs). Our basic idea is to generate initial solutions by the ACO method, and then serve these solutions to the GA as its initial population of individuals. Thus, the GA will start with a population, which is not randomly generated, as in the general case, but one rather closer to an optimal solution. The motivation behind this hybridization is to combine the benefits of both approaches, aimed at achieving commensurate calculations precision with less computation resources, in terms of time and memory. The proposed method is approbated with the estimation of the parameters of a real E. coli fed-batch cultivation process model. The presented results are affirmative of our goal to yield better performance of the hybrid algorithm: almost twice less computational time and approximately five times smaller populations needed, compared to both ACO and GAs, as taken separately. More... »

PAGES

313-320

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-662-43880-0_35

DOI

http://dx.doi.org/10.1007/978-3-662-43880-0_35

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003490467


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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/2008/0102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Applied Mathematics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Institute of Biophysics and Biomedical Engineering, BAS, Acad. G. Bonchev Str., bl. 105, 1113, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.493309.4", 
          "name": [
            "Institute of Biophysics and Biomedical Engineering, BAS, Acad. G. Bonchev Str., bl. 105, 1113, 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 Technologies, BAS, Acad. G. Bonchev Str., bl. 25A, 1113, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.424988.b", 
          "name": [
            "Institute of Information and Communication Technologies, BAS, Acad. G. Bonchev Str., bl. 25A, 1113, 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": "Institute of Information and Communication Technologies, BAS, Acad. G. Bonchev Str., bl. 25A, 1113, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.424988.b", 
          "name": [
            "Institute of Biophysics and Biomedical Engineering, BAS, Acad. G. Bonchev Str., bl. 105, 1113, Sofia, Bulgaria", 
            "Institute of Information and Communication Technologies, BAS, Acad. G. Bonchev Str., bl. 25A, 1113, Sofia, Bulgaria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Atanassova", 
        "givenName": "Vassia", 
        "id": "sg:person.013076544445.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013076544445.17"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2014-06-26", 
    "datePublishedReg": "2014-06-26", 
    "description": "The present work offers a novel approach to parameter identification of an E. coli cultivation process model, using a hybrid of two metaheuristics, namely Ant Colony Optimization (ACO) and Genetic Algorithms (GAs). Our basic idea is to generate initial solutions by the ACO method, and then serve these solutions to the GA as its initial population of individuals. Thus, the GA will start with a population, which is not randomly generated, as in the general case, but one rather closer to an optimal solution. The motivation behind this hybridization is to combine the benefits of both approaches, aimed at achieving commensurate calculations precision with less computation resources, in terms of time and memory. The proposed method is approbated with the estimation of the parameters of a real E. coli fed-batch cultivation process model. The presented results are affirmative of our goal to yield better performance of the hybrid algorithm: almost twice less computational time and approximately five times smaller populations needed, compared to both ACO and GAs, as taken separately.", 
    "editor": [
      {
        "familyName": "Lirkov", 
        "givenName": "Ivan", 
        "type": "Person"
      }, 
      {
        "familyName": "Margenov", 
        "givenName": "Svetozar", 
        "type": "Person"
      }, 
      {
        "familyName": "Wa\u015bniewski", 
        "givenName": "Jerzy", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-662-43880-0_35", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-662-43879-4", 
        "978-3-662-43880-0"
      ], 
      "name": "Large-Scale Scientific Computing", 
      "type": "Book"
    }, 
    "keywords": [
      "Ant Colony Optimization", 
      "parameter identification", 
      "genetic algorithm", 
      "optimal solution", 
      "general case", 
      "computational time", 
      "ACO method", 
      "colony optimization", 
      "ACO-GA", 
      "process model", 
      "less computation resources", 
      "hybrid algorithm", 
      "basic idea", 
      "initial solution", 
      "initial population", 
      "computation resources", 
      "calculation precision", 
      "presented results", 
      "solution", 
      "algorithm", 
      "metaheuristics", 
      "model", 
      "better performance", 
      "optimization", 
      "terms of time", 
      "estimation", 
      "present work", 
      "novel approach", 
      "approach", 
      "parameters", 
      "terms", 
      "idea", 
      "performance", 
      "time", 
      "cases", 
      "work", 
      "results", 
      "precision", 
      "resources", 
      "identification", 
      "method", 
      "memory", 
      "small population", 
      "goal", 
      "motivation", 
      "benefits", 
      "hybrids", 
      "population", 
      "hybridization", 
      "individuals"
    ], 
    "name": "Hybrid ACO-GA for Parameter Identification of an E. coli Cultivation Process Model", 
    "pagination": "313-320", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1003490467"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-662-43880-0_35"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-662-43880-0_35", 
      "https://app.dimensions.ai/details/publication/pub.1003490467"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-05-20T07:42", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/chapter/chapter_170.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-662-43880-0_35"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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.10.1007/978-3-662-43880-0_35'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-662-43880-0_35'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-662-43880-0_35'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-662-43880-0_35'


 

This table displays all metadata directly associated to this object as RDF triples.

138 TRIPLES      23 PREDICATES      75 URIs      68 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-662-43880-0_35 schema:about anzsrc-for:01
2 anzsrc-for:0102
3 schema:author Nc3c3ab44dd104d309fe13e43bd3fe372
4 schema:datePublished 2014-06-26
5 schema:datePublishedReg 2014-06-26
6 schema:description The present work offers a novel approach to parameter identification of an E. coli cultivation process model, using a hybrid of two metaheuristics, namely Ant Colony Optimization (ACO) and Genetic Algorithms (GAs). Our basic idea is to generate initial solutions by the ACO method, and then serve these solutions to the GA as its initial population of individuals. Thus, the GA will start with a population, which is not randomly generated, as in the general case, but one rather closer to an optimal solution. The motivation behind this hybridization is to combine the benefits of both approaches, aimed at achieving commensurate calculations precision with less computation resources, in terms of time and memory. The proposed method is approbated with the estimation of the parameters of a real E. coli fed-batch cultivation process model. The presented results are affirmative of our goal to yield better performance of the hybrid algorithm: almost twice less computational time and approximately five times smaller populations needed, compared to both ACO and GAs, as taken separately.
7 schema:editor Nf982e24f6224461bb8e71fe2b4f75294
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N1e066227d52447d195087a8876cd69ee
12 schema:keywords ACO method
13 ACO-GA
14 Ant Colony Optimization
15 algorithm
16 approach
17 basic idea
18 benefits
19 better performance
20 calculation precision
21 cases
22 colony optimization
23 computation resources
24 computational time
25 estimation
26 general case
27 genetic algorithm
28 goal
29 hybrid algorithm
30 hybridization
31 hybrids
32 idea
33 identification
34 individuals
35 initial population
36 initial solution
37 less computation resources
38 memory
39 metaheuristics
40 method
41 model
42 motivation
43 novel approach
44 optimal solution
45 optimization
46 parameter identification
47 parameters
48 performance
49 population
50 precision
51 present work
52 presented results
53 process model
54 resources
55 results
56 small population
57 solution
58 terms
59 terms of time
60 time
61 work
62 schema:name Hybrid ACO-GA for Parameter Identification of an E. coli Cultivation Process Model
63 schema:pagination 313-320
64 schema:productId N410ead5be35749e6bbfca902c6a3a62c
65 Nb03a6c38043d483797d14e76d3dcd6ba
66 schema:publisher N6799238a26d7415c99f89ea8ffe00c79
67 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003490467
68 https://doi.org/10.1007/978-3-662-43880-0_35
69 schema:sdDatePublished 2022-05-20T07:42
70 schema:sdLicense https://scigraph.springernature.com/explorer/license/
71 schema:sdPublisher N8772ad88399c453eb43f28f71ca9cd63
72 schema:url https://doi.org/10.1007/978-3-662-43880-0_35
73 sgo:license sg:explorer/license/
74 sgo:sdDataset chapters
75 rdf:type schema:Chapter
76 N05dc15bb3e664e2ea2e55322e8ac541f schema:familyName Lirkov
77 schema:givenName Ivan
78 rdf:type schema:Person
79 N13584d1248b94cc8b32e1edce3644abc schema:familyName Margenov
80 schema:givenName Svetozar
81 rdf:type schema:Person
82 N1e066227d52447d195087a8876cd69ee schema:isbn 978-3-662-43879-4
83 978-3-662-43880-0
84 schema:name Large-Scale Scientific Computing
85 rdf:type schema:Book
86 N362fbddb24d74338b94badff698303c1 rdf:first sg:person.011173106320.18
87 rdf:rest Nd855e0b44d9c47edac45944772b26228
88 N3c3d6298ea404cbeb87cb6b782b99a5d schema:familyName Waśniewski
89 schema:givenName Jerzy
90 rdf:type schema:Person
91 N410ead5be35749e6bbfca902c6a3a62c schema:name dimensions_id
92 schema:value pub.1003490467
93 rdf:type schema:PropertyValue
94 N6799238a26d7415c99f89ea8ffe00c79 schema:name Springer Nature
95 rdf:type schema:Organisation
96 N8772ad88399c453eb43f28f71ca9cd63 schema:name Springer Nature - SN SciGraph project
97 rdf:type schema:Organization
98 Nad9347c101284767b250684d360920c3 rdf:first N13584d1248b94cc8b32e1edce3644abc
99 rdf:rest Ne7d663f1df204cef857ccef0c9a4728b
100 Nb03a6c38043d483797d14e76d3dcd6ba schema:name doi
101 schema:value 10.1007/978-3-662-43880-0_35
102 rdf:type schema:PropertyValue
103 Nc3c3ab44dd104d309fe13e43bd3fe372 rdf:first sg:person.015745057111.08
104 rdf:rest N362fbddb24d74338b94badff698303c1
105 Nd855e0b44d9c47edac45944772b26228 rdf:first sg:person.013076544445.17
106 rdf:rest rdf:nil
107 Ne7d663f1df204cef857ccef0c9a4728b rdf:first N3c3d6298ea404cbeb87cb6b782b99a5d
108 rdf:rest rdf:nil
109 Nf982e24f6224461bb8e71fe2b4f75294 rdf:first N05dc15bb3e664e2ea2e55322e8ac541f
110 rdf:rest Nad9347c101284767b250684d360920c3
111 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
112 schema:name Mathematical Sciences
113 rdf:type schema:DefinedTerm
114 anzsrc-for:0102 schema:inDefinedTermSet anzsrc-for:
115 schema:name Applied Mathematics
116 rdf:type schema:DefinedTerm
117 sg:person.011173106320.18 schema:affiliation grid-institutes:grid.424988.b
118 schema:familyName Fidanova
119 schema:givenName Stefka
120 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011173106320.18
121 rdf:type schema:Person
122 sg:person.013076544445.17 schema:affiliation grid-institutes:grid.424988.b
123 schema:familyName Atanassova
124 schema:givenName Vassia
125 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013076544445.17
126 rdf:type schema:Person
127 sg:person.015745057111.08 schema:affiliation grid-institutes:grid.493309.4
128 schema:familyName Roeva
129 schema:givenName Olympia
130 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015745057111.08
131 rdf:type schema:Person
132 grid-institutes:grid.424988.b schema:alternateName Institute of Information and Communication Technologies, BAS, Acad. G. Bonchev Str., bl. 25A, 1113, Sofia, Bulgaria
133 schema:name Institute of Biophysics and Biomedical Engineering, BAS, Acad. G. Bonchev Str., bl. 105, 1113, Sofia, Bulgaria
134 Institute of Information and Communication Technologies, BAS, Acad. G. Bonchev Str., bl. 25A, 1113, Sofia, Bulgaria
135 rdf:type schema:Organization
136 grid-institutes:grid.493309.4 schema:alternateName Institute of Biophysics and Biomedical Engineering, BAS, Acad. G. Bonchev Str., bl. 105, 1113, Sofia, Bulgaria
137 schema:name Institute of Biophysics and Biomedical Engineering, BAS, Acad. G. Bonchev Str., bl. 105, 1113, Sofia, Bulgaria
138 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...