Aboul Ella Hassanien (Abo)

The rapid increase in social networks data and users present an urgent need for predicting the performance of posted data over these networks. It helps in many industrial aspects such as election, public opinion detection and advertising or branding over social networks. This paper presents a new posts’ prediction system for Facebook’s branding pages concerning the user’s attention and interaction. CSO is utilized to optimize the ANFIS parameters for accurate prediction. CSO-ANFIS is compared with several methods including ANFIS, particle swarm optimization, genetic algorithm and krill herd optimization.

warm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then: Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possible
Discusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizers, Depicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function design, Details the similarities, differences, weaknesses, and strengths of each swarm optimization method and Draws parallels between the operators and searching manners of the different algorithms

This chapter presents a remote sensing image fusion based on swarm intelligence. Image fusion is combining multi-sensor images in a single image that has most informative. Remote sensing image fusion is an effective way to extract a large volume of data from multisource images. However, traditional image fusion approaches cannot meet the requirements of applications because they can lose spatial information or distort spectral characteristics. The core of the image fusion is image fusion rules. The main challenge is getting suitable weight of fusion rule. This chapter proposes swarm intelligence to optimize the image fusion rule. Swarm intelligence algorithms are a family of global optimizers inspired by swarm phenomena in nature and have shown better performance. In this chapter, two remote sensing image fusion based on swarm intelligence algorithms, Particle Swarm Optimization (PSO) and flower pollination algorithm are presented to get an adaptive image fusion rule and comparative between them.

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