Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimiza...
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| 格式: | Online |
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| 語言: | 英语 |
| 出版: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| 主題: | |
| 在線閱讀: | ONIX_20220111_9783036523941_868 |
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| _version_ | 1869518016355500032 |
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| collection | Directory of Open Access Books |
| description | Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms. |
| format | Online |
| id | doab-20.500.12854ir-77036 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-770362024-04-09T23:16:30Z Evolutionary Computation 2020 Wang, Gai-Ge Alavi, Amir global optimization cuckoo search algorithm Q-learning mutation self-adaptive step size evolutionary computation playtesting game feature game simulation game trees playtesting metric validation Pareto optimality h-index ranking dominance Pareto-front multi-indicators multi-metric multi-resources citation universities ranking swarm intelligence simulated annealing krill herd particle swarm optimization quantum elephant herding optimization engineering optimization metaheuristic constrained optimization multi-objective optimization single objective optimization differential evolution success-history premature convergence turning-based mutation opposition-based learning ant colony optimization opposite path traveling salesman problems whale optimization algorithm WOA binary whale optimization algorithm bWOA-S bWOA-V feature selection classification dimensionality reduction menu planning problem evolutionary algorithm decomposition-based multi-objective optimisation memetic algorithm iterated local search diversity preservation single-objective optimization knapsack problem travelling salesman problem seed schedule many-objective optimization fuzzing bug detection path discovery evolutionary algorithms (EAs) coevolution dynamic learning performance indicators magnetotelluric one-dimensional inversions geoelectric model optimization problem multi-task optimization multi-task evolutionary computation knowledge transfer assortative mating unified search space quantum computing grey wolf optimizer 0-1 knapsack problem green shop scheduling fuzzy hybrid flow shop scheduling discrete artificial bee colony algorithm minimize makespan minimize total energy consumption thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms. 2022-01-11T13:50:00Z 2022-01-11T13:50:00Z 2021 book ONIX_20220111_9783036523941_868 9783036523941 9783036523958 https://directory.doabooks.org/handle/20.500.12854/77036 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/4646 https://mdpi.com/books/pdfview/book/4646 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-2395-8 10.3390/books978-3-0365-2395-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036523941 9783036523958 442 Basel, Switzerland open access |
| spellingShingle | global optimization cuckoo search algorithm Q-learning mutation self-adaptive step size evolutionary computation playtesting game feature game simulation game trees playtesting metric validation Pareto optimality h-index ranking dominance Pareto-front multi-indicators multi-metric multi-resources citation universities ranking swarm intelligence simulated annealing krill herd particle swarm optimization quantum elephant herding optimization engineering optimization metaheuristic constrained optimization multi-objective optimization single objective optimization differential evolution success-history premature convergence turning-based mutation opposition-based learning ant colony optimization opposite path traveling salesman problems whale optimization algorithm WOA binary whale optimization algorithm bWOA-S bWOA-V feature selection classification dimensionality reduction menu planning problem evolutionary algorithm decomposition-based multi-objective optimisation memetic algorithm iterated local search diversity preservation single-objective optimization knapsack problem travelling salesman problem seed schedule many-objective optimization fuzzing bug detection path discovery evolutionary algorithms (EAs) coevolution dynamic learning performance indicators magnetotelluric one-dimensional inversions geoelectric model optimization problem multi-task optimization multi-task evolutionary computation knowledge transfer assortative mating unified search space quantum computing grey wolf optimizer 0-1 knapsack problem green shop scheduling fuzzy hybrid flow shop scheduling discrete artificial bee colony algorithm minimize makespan minimize total energy consumption thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Evolutionary Computation 2020 |
| title | Evolutionary Computation 2020 |
| title_full | Evolutionary Computation 2020 |
| title_fullStr | Evolutionary Computation 2020 |
| title_full_unstemmed | Evolutionary Computation 2020 |
| title_short | Evolutionary Computation 2020 |
| title_sort | evolutionary computation 2020 |
| topic | global optimization cuckoo search algorithm Q-learning mutation self-adaptive step size evolutionary computation playtesting game feature game simulation game trees playtesting metric validation Pareto optimality h-index ranking dominance Pareto-front multi-indicators multi-metric multi-resources citation universities ranking swarm intelligence simulated annealing krill herd particle swarm optimization quantum elephant herding optimization engineering optimization metaheuristic constrained optimization multi-objective optimization single objective optimization differential evolution success-history premature convergence turning-based mutation opposition-based learning ant colony optimization opposite path traveling salesman problems whale optimization algorithm WOA binary whale optimization algorithm bWOA-S bWOA-V feature selection classification dimensionality reduction menu planning problem evolutionary algorithm decomposition-based multi-objective optimisation memetic algorithm iterated local search diversity preservation single-objective optimization knapsack problem travelling salesman problem seed schedule many-objective optimization fuzzing bug detection path discovery evolutionary algorithms (EAs) coevolution dynamic learning performance indicators magnetotelluric one-dimensional inversions geoelectric model optimization problem multi-task optimization multi-task evolutionary computation knowledge transfer assortative mating unified search space quantum computing grey wolf optimizer 0-1 knapsack problem green shop scheduling fuzzy hybrid flow shop scheduling discrete artificial bee colony algorithm minimize makespan minimize total energy consumption thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| topic_facet | global optimization cuckoo search algorithm Q-learning mutation self-adaptive step size evolutionary computation playtesting game feature game simulation game trees playtesting metric validation Pareto optimality h-index ranking dominance Pareto-front multi-indicators multi-metric multi-resources citation universities ranking swarm intelligence simulated annealing krill herd particle swarm optimization quantum elephant herding optimization engineering optimization metaheuristic constrained optimization multi-objective optimization single objective optimization differential evolution success-history premature convergence turning-based mutation opposition-based learning ant colony optimization opposite path traveling salesman problems whale optimization algorithm WOA binary whale optimization algorithm bWOA-S bWOA-V feature selection classification dimensionality reduction menu planning problem evolutionary algorithm decomposition-based multi-objective optimisation memetic algorithm iterated local search diversity preservation single-objective optimization knapsack problem travelling salesman problem seed schedule many-objective optimization fuzzing bug detection path discovery evolutionary algorithms (EAs) coevolution dynamic learning performance indicators magnetotelluric one-dimensional inversions geoelectric model optimization problem multi-task optimization multi-task evolutionary computation knowledge transfer assortative mating unified search space quantum computing grey wolf optimizer 0-1 knapsack problem green shop scheduling fuzzy hybrid flow shop scheduling discrete artificial bee colony algorithm minimize makespan minimize total energy consumption thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| url | ONIX_20220111_9783036523941_868 |