围裙
发表于 2025-3-25 03:48:42
Implementation of Flower Pollination Algorithm to the Design Optimization of Planar Antennas,built on pollination nature of the flowers, classifying into two categories: biotic and abiotic pollinations. It is observed that the performance of FPA has been well demonstrated through diverse engineering design problems, whereas its efficacy in the design optimization of planar antennas, which a
拥护
发表于 2025-3-25 08:40:18
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ADOPT
发表于 2025-3-25 13:54:05
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即席
发表于 2025-3-25 17:17:53
Optimizing Reinforced Cantilever Retaining Walls Under Dynamic Loading Using Improved Flower Pollinonstruction. Even better savings are possible when they are constructed in earthquake-prone regions. In this study, an improved flower pollination algorithm (IFPA) is used to optimize the design of the reinforced concrete cantilever retaining wall subjected to dynamic loadings. The mathematical mode
伟大
发表于 2025-3-25 23:16:56
Multi-objective Flower Pollination Algorithm and Its Variants to Find Optimal Golomb Rulers for WDMime-consuming. The nature-inspired multi-objective algorithms (MOAs) are the best choice to deal with such design problems. This chapter presents a comparative study of the multi-objective flower pollination algorithm (MOFPA) and its hybrid variants to produce the optimal Golomb ruler (OGR) sequence
仔细阅读
发表于 2025-3-26 00:50:16
Applications of Flower Pollination Algorithm in Wireless Sensor Networking and Image Processing: A been playing an important role in solving these complex problems. Nature simulated optimization strategies are the main motivation for development of quality optimization algorithms which can be achieved through the machines. Based on the characteristics of biological systems occur in the nature, ma
hurricane
发表于 2025-3-26 05:44:51
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善辩
发表于 2025-3-26 09:03:41
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婚姻生活
发表于 2025-3-26 13:54:07
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陪审团每个人
发表于 2025-3-26 17:08:25
Acquired Auditory Processing Disordersgence toward the global optima, fast speed, and better success rate results in the numerous variants of FPA in solving the many NP-complete problems by some modifications or hybridization with other nature-inspired metaheuristic optimization algorithms. The chapter aims to inspire the researchers to