Accelerating Evolution: Integrating PSO Principles into Real-Coded Genetic Algorithm Crossover Article Swipe
Xiaobo Jin
,
Jiajing Tu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2505.03217
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2505.03217
This study introduces an innovative crossover operator named Particle Swarm Optimization-inspired Crossover (PSOX), which is specifically developed for real-coded genetic algorithms. Departing from conventional crossover approaches that only exchange information between individuals within the same generation, PSOX uniquely incorporates guidance from both the current global best solution and historical optimal solutions across multiple generations. This novel mechanism enables the algorithm to maintain population diversity while simultaneously accelerating convergence toward promising regions of the search space. The effectiveness of PSOX is rigorously evaluated through comprehensive experiments on 15 benchmark test functions with diverse characteristics, including unimodal, multimodal, and highly complex landscapes. Comparative analysis against five state-of-the-art crossover operators reveals that PSOX consistently delivers superior performance in terms of solution accuracy, algorithmic stability, and convergence speed, especially when combined with an appropriate mutation strategy. Furthermore, the study provides an in-depth investigation of how different mutation rates influence PSOX's performance, yielding practical guidelines for parameter tuning when addressing optimization problems with varying landscape properties.
Related Topics
Concepts
No concepts available.
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2505.03217
- https://arxiv.org/pdf/2505.03217
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4415247663
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4415247663Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2505.03217Digital Object Identifier
- Title
-
Accelerating Evolution: Integrating PSO Principles into Real-Coded Genetic Algorithm CrossoverWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-06Full publication date if available
- Authors
-
Xiaobo Jin, Jiajing TuList of authors in order
- Landing page
-
https://arxiv.org/abs/2505.03217Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2505.03217Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2505.03217Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
Full payload
| id | https://openalex.org/W4415247663 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2505.03217 |
| ids.doi | https://doi.org/10.48550/arxiv.2505.03217 |
| ids.openalex | https://openalex.org/W4415247663 |
| fwci | |
| type | preprint |
| title | Accelerating Evolution: Integrating PSO Principles into Real-Coded Genetic Algorithm Crossover |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11975 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9283000230789185 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Evolutionary Algorithms and Applications |
| topics[1].id | https://openalex.org/T10100 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9182000160217285 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Metaheuristic Optimization Algorithms Research |
| topics[2].id | https://openalex.org/T10551 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9140999913215637 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2209 |
| topics[2].subfield.display_name | Industrial and Manufacturing Engineering |
| topics[2].display_name | Scheduling and Optimization Algorithms |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2505.03217 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2505.03217 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2505.03217 |
| locations[1].id | doi:10.48550/arxiv.2505.03217 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2505.03217 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5011077051 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1671-1379 |
| authorships[0].author.display_name | Xiaobo Jin |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jin, Xiaobo |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5069964168 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-1702-3615 |
| authorships[1].author.display_name | Jiajing Tu |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Tu, JiaShu |
| authorships[1].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2505.03217 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-16T00:00:00 |
| display_name | Accelerating Evolution: Integrating PSO Principles into Real-Coded Genetic Algorithm Crossover |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11975 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9283000230789185 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Evolutionary Algorithms and Applications |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2505.03217 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2505.03217 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2505.03217 |
| primary_location.id | pmh:oai:arXiv.org:2505.03217 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2505.03217 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2505.03217 |
| publication_date | 2025-05-06 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.15 | 86 |
| abstract_inverted_index.an | 3, 128, 136 |
| abstract_inverted_index.in | 114 |
| abstract_inverted_index.is | 14, 79 |
| abstract_inverted_index.of | 71, 77, 116, 139 |
| abstract_inverted_index.on | 85 |
| abstract_inverted_index.to | 60 |
| abstract_inverted_index.The | 75 |
| abstract_inverted_index.and | 47, 96, 121 |
| abstract_inverted_index.for | 17, 150 |
| abstract_inverted_index.how | 140 |
| abstract_inverted_index.the | 33, 42, 58, 72, 133 |
| abstract_inverted_index.PSOX | 36, 78, 109 |
| abstract_inverted_index.This | 0, 54 |
| abstract_inverted_index.best | 45 |
| abstract_inverted_index.both | 41 |
| abstract_inverted_index.five | 103 |
| abstract_inverted_index.from | 22, 40 |
| abstract_inverted_index.only | 27 |
| abstract_inverted_index.same | 34 |
| abstract_inverted_index.test | 88 |
| abstract_inverted_index.that | 26, 108 |
| abstract_inverted_index.when | 125, 153 |
| abstract_inverted_index.with | 90, 127, 157 |
| abstract_inverted_index.Swarm | 9 |
| abstract_inverted_index.named | 7 |
| abstract_inverted_index.novel | 55 |
| abstract_inverted_index.rates | 143 |
| abstract_inverted_index.study | 1, 134 |
| abstract_inverted_index.terms | 115 |
| abstract_inverted_index.which | 13 |
| abstract_inverted_index.while | 64 |
| abstract_inverted_index.PSOX's | 145 |
| abstract_inverted_index.across | 51 |
| abstract_inverted_index.global | 44 |
| abstract_inverted_index.highly | 97 |
| abstract_inverted_index.search | 73 |
| abstract_inverted_index.space. | 74 |
| abstract_inverted_index.speed, | 123 |
| abstract_inverted_index.toward | 68 |
| abstract_inverted_index.tuning | 152 |
| abstract_inverted_index.within | 32 |
| abstract_inverted_index.(PSOX), | 12 |
| abstract_inverted_index.against | 102 |
| abstract_inverted_index.between | 30 |
| abstract_inverted_index.complex | 98 |
| abstract_inverted_index.current | 43 |
| abstract_inverted_index.diverse | 91 |
| abstract_inverted_index.enables | 57 |
| abstract_inverted_index.genetic | 19 |
| abstract_inverted_index.optimal | 49 |
| abstract_inverted_index.regions | 70 |
| abstract_inverted_index.reveals | 107 |
| abstract_inverted_index.through | 82 |
| abstract_inverted_index.varying | 158 |
| abstract_inverted_index.Particle | 8 |
| abstract_inverted_index.analysis | 101 |
| abstract_inverted_index.combined | 126 |
| abstract_inverted_index.delivers | 111 |
| abstract_inverted_index.exchange | 28 |
| abstract_inverted_index.guidance | 39 |
| abstract_inverted_index.in-depth | 137 |
| abstract_inverted_index.maintain | 61 |
| abstract_inverted_index.multiple | 52 |
| abstract_inverted_index.mutation | 130, 142 |
| abstract_inverted_index.operator | 6 |
| abstract_inverted_index.problems | 156 |
| abstract_inverted_index.provides | 135 |
| abstract_inverted_index.solution | 46, 117 |
| abstract_inverted_index.superior | 112 |
| abstract_inverted_index.uniquely | 37 |
| abstract_inverted_index.yielding | 147 |
| abstract_inverted_index.Crossover | 11 |
| abstract_inverted_index.Departing | 21 |
| abstract_inverted_index.accuracy, | 118 |
| abstract_inverted_index.algorithm | 59 |
| abstract_inverted_index.benchmark | 87 |
| abstract_inverted_index.crossover | 5, 24, 105 |
| abstract_inverted_index.developed | 16 |
| abstract_inverted_index.different | 141 |
| abstract_inverted_index.diversity | 63 |
| abstract_inverted_index.evaluated | 81 |
| abstract_inverted_index.functions | 89 |
| abstract_inverted_index.including | 93 |
| abstract_inverted_index.influence | 144 |
| abstract_inverted_index.landscape | 159 |
| abstract_inverted_index.mechanism | 56 |
| abstract_inverted_index.operators | 106 |
| abstract_inverted_index.parameter | 151 |
| abstract_inverted_index.practical | 148 |
| abstract_inverted_index.promising | 69 |
| abstract_inverted_index.solutions | 50 |
| abstract_inverted_index.strategy. | 131 |
| abstract_inverted_index.unimodal, | 94 |
| abstract_inverted_index.addressing | 154 |
| abstract_inverted_index.approaches | 25 |
| abstract_inverted_index.especially | 124 |
| abstract_inverted_index.guidelines | 149 |
| abstract_inverted_index.historical | 48 |
| abstract_inverted_index.innovative | 4 |
| abstract_inverted_index.introduces | 2 |
| abstract_inverted_index.population | 62 |
| abstract_inverted_index.real-coded | 18 |
| abstract_inverted_index.rigorously | 80 |
| abstract_inverted_index.stability, | 120 |
| abstract_inverted_index.Comparative | 100 |
| abstract_inverted_index.algorithmic | 119 |
| abstract_inverted_index.algorithms. | 20 |
| abstract_inverted_index.appropriate | 129 |
| abstract_inverted_index.convergence | 67, 122 |
| abstract_inverted_index.experiments | 84 |
| abstract_inverted_index.generation, | 35 |
| abstract_inverted_index.individuals | 31 |
| abstract_inverted_index.information | 29 |
| abstract_inverted_index.landscapes. | 99 |
| abstract_inverted_index.multimodal, | 95 |
| abstract_inverted_index.performance | 113 |
| abstract_inverted_index.properties. | 160 |
| abstract_inverted_index.Furthermore, | 132 |
| abstract_inverted_index.accelerating | 66 |
| abstract_inverted_index.consistently | 110 |
| abstract_inverted_index.conventional | 23 |
| abstract_inverted_index.generations. | 53 |
| abstract_inverted_index.incorporates | 38 |
| abstract_inverted_index.optimization | 155 |
| abstract_inverted_index.performance, | 146 |
| abstract_inverted_index.specifically | 15 |
| abstract_inverted_index.comprehensive | 83 |
| abstract_inverted_index.effectiveness | 76 |
| abstract_inverted_index.investigation | 138 |
| abstract_inverted_index.simultaneously | 65 |
| abstract_inverted_index.characteristics, | 92 |
| abstract_inverted_index.state-of-the-art | 104 |
| abstract_inverted_index.Optimization-inspired | 10 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |