Energy Optimized Workflow Scheduling in IaaS Cloud: A Flower Pollination based Approach Article Swipe
Sahani Pooja Jaiprakash
,
Harsh Kumar Arya
,
Indrajeet Gupta
,
Tapas Badal
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22541/au.168594506.69867134/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22541/au.168594506.69867134/v1
The energy consumption of cloud data centers is a critical concern that could affect both the environment and the availability of energy resources. For this, the global community and industries are taking measures to address this issue that is caused by the high electricity consumption of servers, Heating, Ventilation, and Air Conditioning (HVAC) in the data centers. With this context, this paper presents a novel approach for scheduling energy-efficient workflows (EEWS) in cloud computing using the MaxUtil model. The proposed approach incorporates the flower pollination algorithm (FPA), a popular meta-heuristic algorithm inspired by nature. The primary objectives of the proposed scheduling scheme are to minimize energy consumption and workflow processing time (makespan). The proposed algorithm involves two key phases: (i) assigning tasks to available virtual machines (VMs) and (ii) scheduling the tasks based on optimal criteria. As per our knowledge, this is the first study that focuses on optimizing energy consumption and makespan in cloud computing workflow scheduling using FPA. The proposed approch employs an effective representation of pollen and dynamic fitness function with multi-objective. The advantage of FPA lies in its speed of convergence and providing feasible solutions. Extensive studies have been conducted across five different scientific workflows from various fields. The proposed algorithm outperforms traditional workflow scheduling algorithms based on particle swarm optimization (PSO), gravitational search algorithms (GSA) and genetic algorithm (GA). The proposed algorithm outperforms GA, PSO, and GSA in the majority of cases, according to simulation findings. In addition, a well-known statistical test known as variance analysis (ANOVA) is used to validate the experimental results of the suggested algorithm. Based on the result’s of ANOVA test, the article claims that the suggested algorithm is superior to existing methods.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.22541/au.168594506.69867134/v1
- https://www.authorea.com/doi/pdf/10.22541/au.168594506.69867134
- OA Status
- gold
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379387443
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4379387443Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.22541/au.168594506.69867134/v1Digital Object Identifier
- Title
-
Energy Optimized Workflow Scheduling in IaaS Cloud: A Flower Pollination based ApproachWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-05Full publication date if available
- Authors
-
Sahani Pooja Jaiprakash, Harsh Kumar Arya, Indrajeet Gupta, Tapas BadalList of authors in order
- Landing page
-
https://doi.org/10.22541/au.168594506.69867134/v1Publisher landing page
- PDF URL
-
https://www.authorea.com/doi/pdf/10.22541/au.168594506.69867134Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.authorea.com/doi/pdf/10.22541/au.168594506.69867134Direct OA link when available
- Concepts
-
Computer science, Cloud computing, Workflow, Job shop scheduling, Energy consumption, Particle swarm optimization, Scheduling (production processes), Distributed computing, Mathematical optimization, Algorithm, Database, Engineering, Mathematics, Operating system, Schedule, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4379387443 |
|---|---|
| doi | https://doi.org/10.22541/au.168594506.69867134/v1 |
| ids.doi | https://doi.org/10.22541/au.168594506.69867134/v1 |
| ids.openalex | https://openalex.org/W4379387443 |
| fwci | 0.0 |
| type | preprint |
| title | Energy Optimized Workflow Scheduling in IaaS Cloud: A Flower Pollination based Approach |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10101 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1710 |
| topics[0].subfield.display_name | Information Systems |
| topics[0].display_name | Cloud Computing and Resource Management |
| topics[1].id | https://openalex.org/T10273 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9987000226974487 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1705 |
| topics[1].subfield.display_name | Computer Networks and Communications |
| topics[1].display_name | IoT and Edge/Fog Computing |
| topics[2].id | https://openalex.org/T10715 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9952999949455261 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1705 |
| topics[2].subfield.display_name | Computer Networks and Communications |
| topics[2].display_name | Distributed and Parallel Computing Systems |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7646768093109131 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C79974875 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6689045429229736 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q483639 |
| concepts[1].display_name | Cloud computing |
| concepts[2].id | https://openalex.org/C177212765 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6208571195602417 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q627335 |
| concepts[2].display_name | Workflow |
| concepts[3].id | https://openalex.org/C55416958 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5736674070358276 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q6206757 |
| concepts[3].display_name | Job shop scheduling |
| concepts[4].id | https://openalex.org/C2780165032 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5612173676490784 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q16869822 |
| concepts[4].display_name | Energy consumption |
| concepts[5].id | https://openalex.org/C85617194 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5394379496574402 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2072794 |
| concepts[5].display_name | Particle swarm optimization |
| concepts[6].id | https://openalex.org/C206729178 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5170300602912903 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2271896 |
| concepts[6].display_name | Scheduling (production processes) |
| concepts[7].id | https://openalex.org/C120314980 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4673754572868347 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q180634 |
| concepts[7].display_name | Distributed computing |
| concepts[8].id | https://openalex.org/C126255220 |
| concepts[8].level | 1 |
| concepts[8].score | 0.38939914107322693 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[8].display_name | Mathematical optimization |
| concepts[9].id | https://openalex.org/C11413529 |
| concepts[9].level | 1 |
| concepts[9].score | 0.37674474716186523 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[9].display_name | Algorithm |
| concepts[10].id | https://openalex.org/C77088390 |
| concepts[10].level | 1 |
| concepts[10].score | 0.18113446235656738 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8513 |
| concepts[10].display_name | Database |
| concepts[11].id | https://openalex.org/C127413603 |
| concepts[11].level | 0 |
| concepts[11].score | 0.1055261492729187 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[11].display_name | Engineering |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.07881715893745422 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C111919701 |
| concepts[13].level | 1 |
| concepts[13].score | 0.07869547605514526 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[13].display_name | Operating system |
| concepts[14].id | https://openalex.org/C68387754 |
| concepts[14].level | 2 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7271585 |
| concepts[14].display_name | Schedule |
| concepts[15].id | https://openalex.org/C119599485 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q43035 |
| concepts[15].display_name | Electrical engineering |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7646768093109131 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/cloud-computing |
| keywords[1].score | 0.6689045429229736 |
| keywords[1].display_name | Cloud computing |
| keywords[2].id | https://openalex.org/keywords/workflow |
| keywords[2].score | 0.6208571195602417 |
| keywords[2].display_name | Workflow |
| keywords[3].id | https://openalex.org/keywords/job-shop-scheduling |
| keywords[3].score | 0.5736674070358276 |
| keywords[3].display_name | Job shop scheduling |
| keywords[4].id | https://openalex.org/keywords/energy-consumption |
| keywords[4].score | 0.5612173676490784 |
| keywords[4].display_name | Energy consumption |
| keywords[5].id | https://openalex.org/keywords/particle-swarm-optimization |
| keywords[5].score | 0.5394379496574402 |
| keywords[5].display_name | Particle swarm optimization |
| keywords[6].id | https://openalex.org/keywords/scheduling |
| keywords[6].score | 0.5170300602912903 |
| keywords[6].display_name | Scheduling (production processes) |
| keywords[7].id | https://openalex.org/keywords/distributed-computing |
| keywords[7].score | 0.4673754572868347 |
| keywords[7].display_name | Distributed computing |
| keywords[8].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[8].score | 0.38939914107322693 |
| keywords[8].display_name | Mathematical optimization |
| keywords[9].id | https://openalex.org/keywords/algorithm |
| keywords[9].score | 0.37674474716186523 |
| keywords[9].display_name | Algorithm |
| keywords[10].id | https://openalex.org/keywords/database |
| keywords[10].score | 0.18113446235656738 |
| keywords[10].display_name | Database |
| keywords[11].id | https://openalex.org/keywords/engineering |
| keywords[11].score | 0.1055261492729187 |
| keywords[11].display_name | Engineering |
| keywords[12].id | https://openalex.org/keywords/mathematics |
| keywords[12].score | 0.07881715893745422 |
| keywords[12].display_name | Mathematics |
| keywords[13].id | https://openalex.org/keywords/operating-system |
| keywords[13].score | 0.07869547605514526 |
| keywords[13].display_name | Operating system |
| language | en |
| locations[0].id | doi:10.22541/au.168594506.69867134/v1 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | |
| locations[0].pdf_url | https://www.authorea.com/doi/pdf/10.22541/au.168594506.69867134 |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.22541/au.168594506.69867134/v1 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5085735300 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-2857-9555 |
| authorships[0].author.display_name | Sahani Pooja Jaiprakash |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I193073490 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of CSE, Institute of Technical Education Research (ITER), Siksha 'O' Anusandhan (Deemed to be University), Odisha , India |
| authorships[0].institutions[0].id | https://openalex.org/I193073490 |
| authorships[0].institutions[0].ror | https://ror.org/056ep7w45 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I193073490 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Siksha O Anusandhan University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Sahani Pooja Jaiprakash |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of CSE, Institute of Technical Education Research (ITER), Siksha 'O' Anusandhan (Deemed to be University), Odisha , India |
| authorships[1].author.id | https://openalex.org/A5092089713 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Harsh Kumar Arya |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I3129773123 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Computer Science Engineering and Technology , Bennett Univarsity, Greater Noida in Uttar Pradesh, India |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I3129773123 |
| authorships[1].affiliations[1].raw_affiliation_string | Bennett University |
| authorships[1].institutions[0].id | https://openalex.org/I3129773123 |
| authorships[1].institutions[0].ror | https://ror.org/00an5hx75 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I3129773123 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Bennett University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Harsh Kumar Arya |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Bennett University, School of Computer Science Engineering and Technology , Bennett Univarsity, Greater Noida in Uttar Pradesh, India |
| authorships[2].author.id | https://openalex.org/A5012338428 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-1262-899X |
| authorships[2].author.display_name | Indrajeet Gupta |
| authorships[2].countries | IN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I3129773123 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Computer Science Engineering and Technology , Bennett Univarsity, Greater Noida in Uttar Pradesh, India |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I3129773123 |
| authorships[2].affiliations[1].raw_affiliation_string | Bennett University |
| authorships[2].institutions[0].id | https://openalex.org/I3129773123 |
| authorships[2].institutions[0].ror | https://ror.org/00an5hx75 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I3129773123 |
| authorships[2].institutions[0].country_code | IN |
| authorships[2].institutions[0].display_name | Bennett University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Indrajeet Gupta |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Bennett University, School of Computer Science Engineering and Technology , Bennett Univarsity, Greater Noida in Uttar Pradesh, India |
| authorships[3].author.id | https://openalex.org/A5070228614 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-2717-9069 |
| authorships[3].author.display_name | Tapas Badal |
| authorships[3].countries | IN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I3129773123 |
| authorships[3].affiliations[0].raw_affiliation_string | Bennett University |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I3129773123 |
| authorships[3].affiliations[1].raw_affiliation_string | School of Computer Science Engineering and Technology , Bennett Univarsity, Greater Noida in Uttar Pradesh, India |
| authorships[3].institutions[0].id | https://openalex.org/I3129773123 |
| authorships[3].institutions[0].ror | https://ror.org/00an5hx75 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I3129773123 |
| authorships[3].institutions[0].country_code | IN |
| authorships[3].institutions[0].display_name | Bennett University |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Tapas Badal |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Bennett University, School of Computer Science Engineering and Technology , Bennett Univarsity, Greater Noida in Uttar Pradesh, India |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.authorea.com/doi/pdf/10.22541/au.168594506.69867134 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Energy Optimized Workflow Scheduling in IaaS Cloud: A Flower Pollination based Approach |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10101 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1710 |
| primary_topic.subfield.display_name | Information Systems |
| primary_topic.display_name | Cloud Computing and Resource Management |
| related_works | https://openalex.org/W1981780420, https://openalex.org/W2182707996, https://openalex.org/W45233828, https://openalex.org/W2964988449, https://openalex.org/W2397952901, https://openalex.org/W2029380707, https://openalex.org/W188202134, https://openalex.org/W4255934811, https://openalex.org/W3172150420, https://openalex.org/W3204654320 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.22541/au.168594506.69867134/v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://www.authorea.com/doi/pdf/10.22541/au.168594506.69867134 |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.22541/au.168594506.69867134/v1 |
| primary_location.id | doi:10.22541/au.168594506.69867134/v1 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | https://www.authorea.com/doi/pdf/10.22541/au.168594506.69867134 |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.22541/au.168594506.69867134/v1 |
| publication_date | 2023-06-05 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3039016531, https://openalex.org/W3144050156, https://openalex.org/W3044118070, https://openalex.org/W4291952042, https://openalex.org/W4234488063, https://openalex.org/W1750422355, https://openalex.org/W4318240938, https://openalex.org/W2074070386, https://openalex.org/W2160183388, https://openalex.org/W125869453, https://openalex.org/W2758953455, https://openalex.org/W1981202677, https://openalex.org/W2930521045, https://openalex.org/W4285275275, https://openalex.org/W3002068112, https://openalex.org/W4200389202, https://openalex.org/W4240429504, https://openalex.org/W2181767695, https://openalex.org/W1520679476, https://openalex.org/W3048247980, https://openalex.org/W2537420066, https://openalex.org/W3207442676, https://openalex.org/W3122916864, https://openalex.org/W3096308392, https://openalex.org/W3003976313, https://openalex.org/W3011208829, https://openalex.org/W2561389184, https://openalex.org/W3100933494, https://openalex.org/W2623397635, https://openalex.org/W1977367431, https://openalex.org/W2095829438, https://openalex.org/W4310215787, https://openalex.org/W2037420005, https://openalex.org/W3133522574, https://openalex.org/W4293770403, https://openalex.org/W2154513453, https://openalex.org/W2088723799, https://openalex.org/W4200468938, https://openalex.org/W2914250519, https://openalex.org/W4293106306, https://openalex.org/W1120107613, https://openalex.org/W2056640105, https://openalex.org/W2913578938 |
| referenced_works_count | 43 |
| abstract_inverted_index.a | 8, 63, 87, 243 |
| abstract_inverted_index.As | 136 |
| abstract_inverted_index.In | 241 |
| abstract_inverted_index.an | 164 |
| abstract_inverted_index.as | 248 |
| abstract_inverted_index.by | 40, 92 |
| abstract_inverted_index.in | 53, 71, 153, 180, 232 |
| abstract_inverted_index.is | 7, 38, 141, 252, 277 |
| abstract_inverted_index.of | 3, 20, 45, 97, 167, 177, 183, 235, 259, 267 |
| abstract_inverted_index.on | 133, 147, 211, 264 |
| abstract_inverted_index.to | 33, 103, 122, 238, 254, 279 |
| abstract_inverted_index.(i) | 119 |
| abstract_inverted_index.Air | 50 |
| abstract_inverted_index.FPA | 178 |
| abstract_inverted_index.For | 23 |
| abstract_inverted_index.GA, | 228 |
| abstract_inverted_index.GSA | 231 |
| abstract_inverted_index.The | 0, 78, 94, 112, 160, 175, 202, 224 |
| abstract_inverted_index.and | 17, 28, 49, 107, 127, 151, 169, 185, 220, 230 |
| abstract_inverted_index.are | 30, 102 |
| abstract_inverted_index.for | 66 |
| abstract_inverted_index.its | 181 |
| abstract_inverted_index.key | 117 |
| abstract_inverted_index.our | 138 |
| abstract_inverted_index.per | 137 |
| abstract_inverted_index.the | 15, 18, 25, 41, 54, 75, 82, 98, 130, 142, 233, 256, 260, 265, 270, 274 |
| abstract_inverted_index.two | 116 |
| abstract_inverted_index.(ii) | 128 |
| abstract_inverted_index.FPA. | 159 |
| abstract_inverted_index.PSO, | 229 |
| abstract_inverted_index.With | 57 |
| abstract_inverted_index.been | 192 |
| abstract_inverted_index.both | 14 |
| abstract_inverted_index.data | 5, 55 |
| abstract_inverted_index.five | 195 |
| abstract_inverted_index.from | 199 |
| abstract_inverted_index.have | 191 |
| abstract_inverted_index.high | 42 |
| abstract_inverted_index.lies | 179 |
| abstract_inverted_index.test | 246 |
| abstract_inverted_index.that | 11, 37, 145, 273 |
| abstract_inverted_index.this | 35, 58, 60, 140 |
| abstract_inverted_index.time | 110 |
| abstract_inverted_index.used | 253 |
| abstract_inverted_index.with | 173 |
| abstract_inverted_index.(GA). | 223 |
| abstract_inverted_index.(GSA) | 219 |
| abstract_inverted_index.(VMs) | 126 |
| abstract_inverted_index.ANOVA | 268 |
| abstract_inverted_index.Based | 263 |
| abstract_inverted_index.based | 132, 210 |
| abstract_inverted_index.cloud | 4, 72, 154 |
| abstract_inverted_index.could | 12 |
| abstract_inverted_index.first | 143 |
| abstract_inverted_index.issue | 36 |
| abstract_inverted_index.known | 247 |
| abstract_inverted_index.novel | 64 |
| abstract_inverted_index.paper | 61 |
| abstract_inverted_index.speed | 182 |
| abstract_inverted_index.study | 144 |
| abstract_inverted_index.swarm | 213 |
| abstract_inverted_index.tasks | 121, 131 |
| abstract_inverted_index.test, | 269 |
| abstract_inverted_index.this, | 24 |
| abstract_inverted_index.using | 74, 158 |
| abstract_inverted_index.(EEWS) | 70 |
| abstract_inverted_index.(FPA), | 86 |
| abstract_inverted_index.(HVAC) | 52 |
| abstract_inverted_index.(PSO), | 215 |
| abstract_inverted_index.across | 194 |
| abstract_inverted_index.affect | 13 |
| abstract_inverted_index.cases, | 236 |
| abstract_inverted_index.caused | 39 |
| abstract_inverted_index.claims | 272 |
| abstract_inverted_index.energy | 1, 21, 105, 149 |
| abstract_inverted_index.flower | 83 |
| abstract_inverted_index.global | 26 |
| abstract_inverted_index.model. | 77 |
| abstract_inverted_index.pollen | 168 |
| abstract_inverted_index.scheme | 101 |
| abstract_inverted_index.search | 217 |
| abstract_inverted_index.taking | 31 |
| abstract_inverted_index.(ANOVA) | 251 |
| abstract_inverted_index.MaxUtil | 76 |
| abstract_inverted_index.address | 34 |
| abstract_inverted_index.approch | 162 |
| abstract_inverted_index.article | 271 |
| abstract_inverted_index.centers | 6 |
| abstract_inverted_index.concern | 10 |
| abstract_inverted_index.dynamic | 170 |
| abstract_inverted_index.employs | 163 |
| abstract_inverted_index.fields. | 201 |
| abstract_inverted_index.fitness | 171 |
| abstract_inverted_index.focuses | 146 |
| abstract_inverted_index.genetic | 221 |
| abstract_inverted_index.nature. | 93 |
| abstract_inverted_index.optimal | 134 |
| abstract_inverted_index.phases: | 118 |
| abstract_inverted_index.popular | 88 |
| abstract_inverted_index.primary | 95 |
| abstract_inverted_index.results | 258 |
| abstract_inverted_index.studies | 190 |
| abstract_inverted_index.various | 200 |
| abstract_inverted_index.virtual | 124 |
| abstract_inverted_index.Heating, | 47 |
| abstract_inverted_index.analysis | 250 |
| abstract_inverted_index.approach | 65, 80 |
| abstract_inverted_index.centers. | 56 |
| abstract_inverted_index.context, | 59 |
| abstract_inverted_index.critical | 9 |
| abstract_inverted_index.existing | 280 |
| abstract_inverted_index.feasible | 187 |
| abstract_inverted_index.function | 172 |
| abstract_inverted_index.inspired | 91 |
| abstract_inverted_index.involves | 115 |
| abstract_inverted_index.machines | 125 |
| abstract_inverted_index.majority | 234 |
| abstract_inverted_index.makespan | 152 |
| abstract_inverted_index.measures | 32 |
| abstract_inverted_index.methods. | 281 |
| abstract_inverted_index.minimize | 104 |
| abstract_inverted_index.particle | 212 |
| abstract_inverted_index.presents | 62 |
| abstract_inverted_index.proposed | 79, 99, 113, 161, 203, 225 |
| abstract_inverted_index.servers, | 46 |
| abstract_inverted_index.superior | 278 |
| abstract_inverted_index.validate | 255 |
| abstract_inverted_index.variance | 249 |
| abstract_inverted_index.workflow | 108, 156, 207 |
| abstract_inverted_index.Extensive | 189 |
| abstract_inverted_index.according | 237 |
| abstract_inverted_index.addition, | 242 |
| abstract_inverted_index.advantage | 176 |
| abstract_inverted_index.algorithm | 85, 90, 114, 204, 222, 226, 276 |
| abstract_inverted_index.assigning | 120 |
| abstract_inverted_index.available | 123 |
| abstract_inverted_index.community | 27 |
| abstract_inverted_index.computing | 73, 155 |
| abstract_inverted_index.conducted | 193 |
| abstract_inverted_index.criteria. | 135 |
| abstract_inverted_index.different | 196 |
| abstract_inverted_index.effective | 165 |
| abstract_inverted_index.findings. | 240 |
| abstract_inverted_index.providing | 186 |
| abstract_inverted_index.suggested | 261, 275 |
| abstract_inverted_index.workflows | 69, 198 |
| abstract_inverted_index.algorithm. | 262 |
| abstract_inverted_index.algorithms | 209, 218 |
| abstract_inverted_index.industries | 29 |
| abstract_inverted_index.knowledge, | 139 |
| abstract_inverted_index.objectives | 96 |
| abstract_inverted_index.optimizing | 148 |
| abstract_inverted_index.processing | 109 |
| abstract_inverted_index.resources. | 22 |
| abstract_inverted_index.result’s | 266 |
| abstract_inverted_index.scheduling | 67, 100, 129, 157, 208 |
| abstract_inverted_index.scientific | 197 |
| abstract_inverted_index.simulation | 239 |
| abstract_inverted_index.solutions. | 188 |
| abstract_inverted_index.well-known | 244 |
| abstract_inverted_index.(makespan). | 111 |
| abstract_inverted_index.consumption | 2, 44, 106, 150 |
| abstract_inverted_index.convergence | 184 |
| abstract_inverted_index.electricity | 43 |
| abstract_inverted_index.environment | 16 |
| abstract_inverted_index.outperforms | 205, 227 |
| abstract_inverted_index.pollination | 84 |
| abstract_inverted_index.statistical | 245 |
| abstract_inverted_index.traditional | 206 |
| abstract_inverted_index.Conditioning | 51 |
| abstract_inverted_index.Ventilation, | 48 |
| abstract_inverted_index.availability | 19 |
| abstract_inverted_index.experimental | 257 |
| abstract_inverted_index.incorporates | 81 |
| abstract_inverted_index.optimization | 214 |
| abstract_inverted_index.gravitational | 216 |
| abstract_inverted_index.meta-heuristic | 89 |
| abstract_inverted_index.representation | 166 |
| abstract_inverted_index.energy-efficient | 68 |
| abstract_inverted_index.multi-objective. | 174 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8999999761581421 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.09580179 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |