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Applying Particle Swarm Optimization

New Solutions and Cases for Optimized Portfolios

  • Book
  • © 2021

Overview

Editors:
  1. Burcu Adıgüzel Mercangöz

    1. Faculty of Transportation and Logistics, Istanbul University, Avcılar/Istanbul, Turkey

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  • First book-length treatment of portfolio optimization using particle swarm optimization (PSO)
  • Explains PSO in detail and shows how to apply it
  • Applies PSO to portfolio optimization problems in a range of areas

Part of the book series: International Series in Operations Research & Management Science (ISOR, volume 306)

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xii
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  2. Applying Particle Swarm Optimization to Portfolio Optimization

    1. Front Matter

      Pages 1-1
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    2. Utility: Theories and Models

      • Murat Akkaya
      Pages 3-14

    3. Portfolio Optimization

      • Burcu Adıgüzel Mercangöz
      Pages 15-27

    4. Behavioral Portfolio Theory

      • Murat Akkaya
      Pages 29-48

    5. A Comparative Study on PSO with Other Metaheuristic Methods

      • Serhat Yarat, Sibel Senan, Zeynep Orman
      Pages 49-72

    6. Mathematical Model of Particle Swarm Optimization: Numerical Optimization Problems

      • Ashwin A. Kadkol
      Pages 73-95

    7. Particle Swarm Optimization: The Foundation

      • Dadabada Pradeep Kumar
      Pages 97-110

    8. The PSO Family: Application to the Portfolio Optimization Problem

      • Lucas Fernández-Brillet, Oscar Álvarez, Juan Luis Fernández-Martínez
      Pages 111-132

    9. A Constrained Portfolio Selection Model Solved by Particle Swarm Optimization Under Different Risk Measures

      • Akbar Esfahanipour, Pouya Khodaee
      Pages 133-153

    10. Optimal Portfolio Selection with Particle Swarm Algorithm: An Application on BIST-30

      • Burcu Adıgüzel Mercangöz, Altaf Q. H. Badar
      Pages 155-167

    11. Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization

      • Mulazim-Ali Khokhar, Kris Boudt, Chunlin Wan
      Pages 169-187

  3. Different Applications of PSO

    1. Front Matter

      Pages 189-189
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    2. Different Applications of PSO

      • Altaf Q. H. Badar
      Pages 191-208

    3. Particle Swarm Optimization in Global Path Planning for Swarm of Robots

      • Ritesh Kumar Halder
      Pages 209-232

    4. Training Multi-layer Perceptron Using Hybridization of Chaotic Gravitational Search Algorithm and Particle Swarm Optimization

      • Sajad Ahmad Rather, P. Shanthi Bala, Pillai Lekshmi Ashokan
      Pages 233-262

    5. Solving Optimization Problem with Particle Swarm Optimization: Solving Hybrid Flow Shop Scheduling Problem with Particle Swarm Optimization Algorithm

      • Fatma Selen Madenoğlu
      Pages 263-277

    6. Constriction Coefficient-Based Particle Swarm Optimization and Gravitational Search Algorithm for Image Segmentation

      • Sajad Ahmad Rather, P. Shanthi Bala
      Pages 279-305

    7. An Overview of the Performance of PSO Algorithm in Renewable Energy Systems

      • Omar Hazem Mohammed, Mohammed Kharrich
      Pages 307-320

    8. Application of PSO in Distribution Power Systems: Operation and Planning Optimization

      • Paschalis A. Gkaidatzis, Aggelos S. Bouhouras, Dimitris P. Labridis
      Pages 321-351
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Keywords

About this book

This book explains the theoretical structure of particle swarm optimization (PSO) and focuses on the application of PSO to portfolio optimization problems. The general goal of portfolio optimization is to find a solution that provides the highest expected return at each level of portfolio risk. According to H. Markowitz’s portfolio selection theory, as new assets are added to an investment portfolio, the total risk of the portfolio’s decreases depending on the correlations of asset returns, while the expected return on the portfolio represents the weighted average of the expected returns for each asset.

The book explains PSO in detail and demonstrates how to implement Markowitz’s portfolio optimization approach using PSO. In addition, it expands on the Markowitz model and seeks to improve the solution-finding process with the aid of various algorithms. In short, the book provides researchers, teachers, engineers, managers and practitioners with many tools they need to apply the PSO technique to portfolio optimization.

Editors and Affiliations

  • Faculty of Transportation and Logistics, Istanbul University, Avcılar/Istanbul, Turkey

    Burcu Adıgüzel Mercangöz

About the editor

Burcu Adıgüzel Mercangöz is an Associate Professor of Operations Research at the Faculty of Transportation and Logistics, Istanbul University (Turkey). Her research interests include Quantitative Methods, Multicriteria Decision Analysis, Optimization Techniques, Optimization (Mathematics), Supply Chain Management, Transportation, Logistics, Information Systems, Heuristics, and Meta-Heuristics (Tabu Search, Genetic Algorithms, Simulated Annealing, Ant-Colony, Particle Swarm Optimization).

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