Home ›
Entities
› academia
› Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem
Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem
Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem
Summary
Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem is a scholarly article[1].
Key Facts
Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem. Retrieved May 24, 2026, from https://4ort.xyz/entity/multi-objective-coyote-optimization-algorithm-based-on-hybrid-elite-framework-and-meta-lamarckian-learning-strategy-for-
MLA“Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/multi-objective-coyote-optimization-algorithm-based-on-hybrid-elite-framework-and-meta-lamarckian-learning-strategy-for-.
BibTeX@misc{4ortxyz_multi-objective-coyote-optimization-algorithm-based-on-hybrid-elite-framework-and-meta-lamarckian-learning-strategy-for-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem}}, year = {2026}, url = {https://4ort.xyz/entity/multi-objective-coyote-optimization-algorithm-based-on-hybrid-elite-framework-and-meta-lamarckian-learning-strategy-for-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem — https://4ort.xyz/entity/multi-objective-coyote-optimization-algorithm-based-on-hybrid-elite-framework-and-meta-lamarckian-learning-strategy-for- (retrieved 2026-05-24)
