Cuckoo search for business optimization applications

Cuckoo search has become a popular and powerful metaheuristic algorithm for global optimization. In business optimization and applications, many studies have focused on support vector machine and neural networks. In this paper, we use cuckoo search to carry out optimization tasks and compare the performance of cuckoo search with support vector machine. By testing benchmarks such as project scheduling and bankruptcy predictions, we conclude that cuckoo search can perform better than support vector machine.

[1]  Yang Xin-She.  マルチモーダル最適化のためのFireflyアルゴリズム | 文献情報 | J-GLOBAL 科学技術総合リンクセンター , 2009 .

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[4]  Kenneth Morgan,et al.  Modified cuckoo search: A new gradient free optimisation algorithm , 2011 .

[5]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[6]  Sönke Hartmann,et al.  A self‐adapting genetic algorithm for project scheduling under resource constraints , 2002 .

[7]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[8]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[9]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[10]  Abdesslem Layeb,et al.  A novel quantum inspired cuckoo search for knapsack problems , 2011, Int. J. Bio Inspired Comput..

[11]  Michael G. Madden,et al.  The Genetic Kernel Support Vector Machine: Description and Evaluation , 2005, Artificial Intelligence Review.

[12]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[13]  R. Mantegna,et al.  Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[14]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[15]  Xin-She Yang,et al.  Optimization and data mining for fracture prediction in geosciences , 2010, ICCS.

[16]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[17]  Francis Eng Hock Tay,et al.  Financial Forecasting Using Support Vector Machines , 2001, Neural Computing & Applications.

[18]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[19]  Rainer Kolisch,et al.  PSPLIB - A project scheduling problem library: OR Software - ORSEP Operations Research Software Exchange Program , 1997 .

[20]  Babita Gupta,et al.  An empirical investigation of online consumer purchasing behavior , 2003, CACM.

[21]  E. Altman,et al.  Managing Credit Risk: The Next Great Financial Challenge , 1998 .

[22]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[23]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

[24]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[25]  Simon Fong,et al.  Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications , 2011, NDT.

[26]  Francisco Ballestín,et al.  A hybrid genetic algorithm for the resource-constrained project scheduling problem , 2008, Eur. J. Oper. Res..