Titlebook: Applications of Flower Pollination Algorithm and its Variants; Nilanjan Dey Book 2021 The Editor(s) (if applicable) and The Author(s), und

围裙

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

ADOPT

即席

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

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

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

善辩

婚姻生活

陪审团每个人

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