Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry
Computational intelligence is an important branch of artificial intelligence. Nowadays, evolutionary computation as a part of computational intelligence is widely used to solve various numerical problems and real-world engineering problems. Its application and development bring a great contribution...
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| Format: | Online |
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| Langue: | anglais |
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MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Accès en ligne: | ONIX_20260416T142754_9783725858330_33 |
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| _version_ | 1869528627911065600 |
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| collection | Directory of Open Access Books |
| description | Computational intelligence is an important branch of artificial intelligence. Nowadays, evolutionary computation as a part of computational intelligence is widely used to solve various numerical problems and real-world engineering problems. Its application and development bring a great contribution to the optimization domain. Thus, it is of great interest to investigate the role and significance of evolutionary computation, metaheuristics, and nature-inspired algorithms in optimizing distinctive problems. This Reprint aims to bring together both experts and newcomers from either academia or industry to discuss new and existing issues concerning evolutionary computation and optimization. The research topics include cloud-edge-end collaborative task offloading, time-series analysis of brent oil price, high-efficiency and ultrawideband polarization conversion metasurface, many-objective optimization, imperative programs, maize water and fertilizer irrigation simulation, multi-objective vehicle routing problems with time windows, and short-term electrical load forecasting. Various optimization algorithms such as particle swarm optimization, genetic programming, and non-dominated sorting genetic algorithm are successfully applied to these topics and show superior performance. These algorithms not only greatly promote the development of evolutionary computation and community, but also effectively address some existing limitation of optimization problems. |
| format | Online |
| id | doab-20.500.12854ir-174928 |
| 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-1749282026-04-16T17:24:17Z Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry Wang, Yirui Gao, Shangce Yu, Yang Electrical load forecasting Machine learning Extreme learning machine Dynamic backward learning Madness factor operator Multiobjective vehicle routing problem with time windows Deep reinforcement learning Evolutionary multi-task optimization Knowledge transfer NSGA-II DSSAT model Local search Optimization of irrigation and fertilization Evolutionary algorithms Tree genetic programming Linear genetic programming Imperative programming Many-objective optimization Penalty-based boundary intersection Comprehensive adaptive penalty scheme NSGA-III Metasurface Polarization conversion Ultrawideband Topology optimization Shape optimization Genetic algorithm Artificial intelligence ANFIS Brent oil Metaheuristic optimization Methodological symmetry Time-series analysis Swarm intelligence Optimization algorithms Nature-inspired algorithms Cloud–edge–end collaborative network Task offloading Method symmetry Particle swarm optimization Metaheuristic algorithm thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Computational intelligence is an important branch of artificial intelligence. Nowadays, evolutionary computation as a part of computational intelligence is widely used to solve various numerical problems and real-world engineering problems. Its application and development bring a great contribution to the optimization domain. Thus, it is of great interest to investigate the role and significance of evolutionary computation, metaheuristics, and nature-inspired algorithms in optimizing distinctive problems. This Reprint aims to bring together both experts and newcomers from either academia or industry to discuss new and existing issues concerning evolutionary computation and optimization. The research topics include cloud-edge-end collaborative task offloading, time-series analysis of brent oil price, high-efficiency and ultrawideband polarization conversion metasurface, many-objective optimization, imperative programs, maize water and fertilizer irrigation simulation, multi-objective vehicle routing problems with time windows, and short-term electrical load forecasting. Various optimization algorithms such as particle swarm optimization, genetic programming, and non-dominated sorting genetic algorithm are successfully applied to these topics and show superior performance. These algorithms not only greatly promote the development of evolutionary computation and community, but also effectively address some existing limitation of optimization problems. 2026-04-16T17:24:10Z 2026-04-16T17:24:10Z 2025 book ONIX_20260416T142754_9783725858330_33 9783725858330 9783725858347 https://directory.doabooks.org/handle/20.500.12854/174928 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/11827 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-5834-7 10.3390/books978-3-7258-5834-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725858330 9783725858347 166 CH open access |
| spellingShingle | Electrical load forecasting Machine learning Extreme learning machine Dynamic backward learning Madness factor operator Multiobjective vehicle routing problem with time windows Deep reinforcement learning Evolutionary multi-task optimization Knowledge transfer NSGA-II DSSAT model Local search Optimization of irrigation and fertilization Evolutionary algorithms Tree genetic programming Linear genetic programming Imperative programming Many-objective optimization Penalty-based boundary intersection Comprehensive adaptive penalty scheme NSGA-III Metasurface Polarization conversion Ultrawideband Topology optimization Shape optimization Genetic algorithm Artificial intelligence ANFIS Brent oil Metaheuristic optimization Methodological symmetry Time-series analysis Swarm intelligence Optimization algorithms Nature-inspired algorithms Cloud–edge–end collaborative network Task offloading Method symmetry Particle swarm optimization Metaheuristic algorithm thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry |
| title | Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry |
| title_full | Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry |
| title_fullStr | Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry |
| title_full_unstemmed | Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry |
| title_short | Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry |
| title_sort | evolutionary computation metaheuristics nature inspired algorithms and symmetry |
| topic | Electrical load forecasting Machine learning Extreme learning machine Dynamic backward learning Madness factor operator Multiobjective vehicle routing problem with time windows Deep reinforcement learning Evolutionary multi-task optimization Knowledge transfer NSGA-II DSSAT model Local search Optimization of irrigation and fertilization Evolutionary algorithms Tree genetic programming Linear genetic programming Imperative programming Many-objective optimization Penalty-based boundary intersection Comprehensive adaptive penalty scheme NSGA-III Metasurface Polarization conversion Ultrawideband Topology optimization Shape optimization Genetic algorithm Artificial intelligence ANFIS Brent oil Metaheuristic optimization Methodological symmetry Time-series analysis Swarm intelligence Optimization algorithms Nature-inspired algorithms Cloud–edge–end collaborative network Task offloading Method symmetry Particle swarm optimization Metaheuristic algorithm thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | Electrical load forecasting Machine learning Extreme learning machine Dynamic backward learning Madness factor operator Multiobjective vehicle routing problem with time windows Deep reinforcement learning Evolutionary multi-task optimization Knowledge transfer NSGA-II DSSAT model Local search Optimization of irrigation and fertilization Evolutionary algorithms Tree genetic programming Linear genetic programming Imperative programming Many-objective optimization Penalty-based boundary intersection Comprehensive adaptive penalty scheme NSGA-III Metasurface Polarization conversion Ultrawideband Topology optimization Shape optimization Genetic algorithm Artificial intelligence ANFIS Brent oil Metaheuristic optimization Methodological symmetry Time-series analysis Swarm intelligence Optimization algorithms Nature-inspired algorithms Cloud–edge–end collaborative network Task offloading Method symmetry Particle swarm optimization Metaheuristic algorithm thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20260416T142754_9783725858330_33 |