Swarm Intelligence and Evolutionary Algorithms for Real World Applications
This reprint showcases recent advances in swarm intelligence (SI) and evolutionary computation (EC), emphasising their capacity to address complex, data-intensive, and noise-prone real-world problems where conventional methods often fall short. The collected works highlight how the self-organising a...
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| Ձևաչափ: | Online |
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| Լեզու: | անգլերեն |
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MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Առցանց հասանելիություն: | ONIX_20260416T142754_9783725864164_38 |
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| _version_ | 1869524197141643264 |
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| collection | Directory of Open Access Books |
| description | This reprint showcases recent advances in swarm intelligence (SI) and evolutionary computation (EC), emphasising their capacity to address complex, data-intensive, and noise-prone real-world problems where conventional methods often fall short. The collected works highlight how the self-organising and adaptive nature of SI and EC enables robust search, optimisation, and modeling across diverse domains. The contributions span finance, healthcare, hardware design, energy systems, molecular biology, and intelligent environments. They include hybrid neural–evolutionary strategies for portfolio optimisation, evolutionary approaches for identifying circadian-modulating molecules, and grammatical evolution for automatic generation of synthesizable hardware code. Bio-inspired multi-objective optimisation is applied to early voice-disorder detection, while particle swarm optimisation supports optimal placement of electric-vehicle parking infrastructure. New algorithmic developments—such as a swarm optimiser and symbiotic organism search-based unsupervised feature selection—advance global optimisation and data analytics. Finally, a QPSO-based hybrid technique enhances indoor positioning by fusing WLAN and WSN data. Together, these papers demonstrate the versatility and growing impact of SI and EC techniques, fostering dialogue among emerging and established researchers and advancing their application to pressing real-world challenges. |
| format | Online |
| id | doab-20.500.12854ir-175283 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1752832026-04-16T20:01:49Z Swarm Intelligence and Evolutionary Algorithms for Real World Applications al-Rifaie, Mohammad Majid Swarm intelligence Evolutionary computation Multi-objective optimisation thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities This reprint showcases recent advances in swarm intelligence (SI) and evolutionary computation (EC), emphasising their capacity to address complex, data-intensive, and noise-prone real-world problems where conventional methods often fall short. The collected works highlight how the self-organising and adaptive nature of SI and EC enables robust search, optimisation, and modeling across diverse domains. The contributions span finance, healthcare, hardware design, energy systems, molecular biology, and intelligent environments. They include hybrid neural–evolutionary strategies for portfolio optimisation, evolutionary approaches for identifying circadian-modulating molecules, and grammatical evolution for automatic generation of synthesizable hardware code. Bio-inspired multi-objective optimisation is applied to early voice-disorder detection, while particle swarm optimisation supports optimal placement of electric-vehicle parking infrastructure. New algorithmic developments—such as a swarm optimiser and symbiotic organism search-based unsupervised feature selection—advance global optimisation and data analytics. Finally, a QPSO-based hybrid technique enhances indoor positioning by fusing WLAN and WSN data. Together, these papers demonstrate the versatility and growing impact of SI and EC techniques, fostering dialogue among emerging and established researchers and advancing their application to pressing real-world challenges. 2026-04-16T20:01:44Z 2026-04-16T20:01:44Z 2026 book ONIX_20260416T142754_9783725864164_38 9783725864164 9783725864171 https://directory.doabooks.org/handle/20.500.12854/175283 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/12195 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-6417-1 10.3390/books978-3-7258-6417-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725864164 9783725864171 188 CH open access |
| spellingShingle | Swarm intelligence Evolutionary computation Multi-objective optimisation thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities Swarm Intelligence and Evolutionary Algorithms for Real World Applications |
| title | Swarm Intelligence and Evolutionary Algorithms for Real World Applications |
| title_full | Swarm Intelligence and Evolutionary Algorithms for Real World Applications |
| title_fullStr | Swarm Intelligence and Evolutionary Algorithms for Real World Applications |
| title_full_unstemmed | Swarm Intelligence and Evolutionary Algorithms for Real World Applications |
| title_short | Swarm Intelligence and Evolutionary Algorithms for Real World Applications |
| title_sort | swarm intelligence and evolutionary algorithms for real world applications |
| topic | Swarm intelligence Evolutionary computation Multi-objective optimisation thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities |
| topic_facet | Swarm intelligence Evolutionary computation Multi-objective optimisation thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities |
| url | ONIX_20260416T142754_9783725864164_38 |