Improved particle swarm optimization and application to portfolio selection

• DOI: 10.1002/ecjc.20263
• Publication Date: 2006-11-17T12:24:09Z
• Authors: Makoto Koshino; Hiroaki Murata; Haruhiko Kimura
• Keywords: Flocking (texture)

Abstract Particle swarm optimization (PSO) is a population‐based stochastic technique, inspired by the social behavior of birds (flocking) or fish (schooling), which applied to various problems in nonlinear systems. The inertia weights approach (IWA) and constriction factor (CFA) are improved methods PSO. IWA searches problem space globally early steps, finally locally near optimal solution. CFA method that introduces new parameter into velocity update equation. This paper proposes combination (the Inertia Weights Constriction Factor Approach: IWCFA), PSO rank , whose objective ranking individuals population. These two proposed function optimizations portfolio selection problem, typical mathematical securities finance. results show original finds better solutions than GA, © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 90(3): 13–25, 2007; Published online InterScience ( www.interscience.wiley.com ). DOI 10.1002/ecjc.20263