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What is Particle

Metaheuristic Approaches to Portfolio Optimization
It is a combination of individuals in the swarm. It is the potential solution in the PSO algorithm.
Published in Chapter:
Particle Swarm Algorithm: An Application on Portfolio Optimization
Burcu Adiguzel Mercangoz (Istanbul University, Turkey)
Copyright: © 2019 |Pages: 33
DOI: 10.4018/978-1-5225-8103-1.ch002
Abstract
Optimization is discovering an alternative with the most cost-effective or highest-achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. Portfolio optimization in finance depends on selecting assets from an opportunity set which yields highest expected return on each level of portfolio risk. Optimization algorithms based on natural events are called heuristic algorithms. The particle swarm optimization (PSO) is a population-based heuristic optimization technique. The technique is inspired by the ability of animals such as birds and fish to adapt to their environment by applying a “sharing of knowledge” approach, to find rich food sources and to avoid hunting. This chapter focuses on portfolio selection problems and shows how to manage financial portfolios using a particle swarm optimization (PSO) technique which is a heuristic algorithm. In order to better understand the subject, the technique has been evaluated in Istanbul Stock Exchange for three transportation sector stocks.
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More Results
Hybrid Intrusion Detection System for Smart Home Applications
In an optimisation problem where there is a population of candidate solutions, the candidate solution that is chosen as the one to optimise over the search space is called a particle.
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2D-PAGE Analysis Using Evolutionary Computation
Each of the elements that explore the search space in a Particle Swarm Optimization algorithm.
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