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From Optimization to Clustering: A Swarm Intelligence Approach

From Optimization to Clustering: A Swarm Intelligence Approach

Megha Vora, T. T. Mirnalinee
Copyright: © 2017 |Pages: 26
ISBN13: 9781522507888|ISBN10: 1522507884|EISBN13: 9781522507895
DOI: 10.4018/978-1-5225-0788-8.ch058
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MLA

Vora, Megha, and T. T. Mirnalinee. "From Optimization to Clustering: A Swarm Intelligence Approach." Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 1519-1544. https://doi.org/10.4018/978-1-5225-0788-8.ch058

APA

Vora, M. & Mirnalinee, T. T. (2017). From Optimization to Clustering: A Swarm Intelligence Approach. In I. Management Association (Ed.), Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications (pp. 1519-1544). IGI Global. https://doi.org/10.4018/978-1-5225-0788-8.ch058

Chicago

Vora, Megha, and T. T. Mirnalinee. "From Optimization to Clustering: A Swarm Intelligence Approach." In Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1519-1544. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0788-8.ch058

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Abstract

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

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