Skip to main content

SIWO: A Hybrid Algorithm Combined with the Conventional SCE and Novel IWO

Buy Article:

$113.00 plus tax (Refund Policy)


This paper develops a novel population-based optimization method, designated as the Shuffled Invasive Weed Optimization (SIWO). As a cooperative search metaphor inspired by natural memetics, it explores a global optimal solution for hard combinatorial optimization problems by combining the benefits of the competitiveness mixing of information of the shuffled complex evolution technique with the natural biological evolution-based Invasive Weed Optimization (IWO) algorithm. The algorithm consists of a set of interacting weed population partitioned into different memeplexes. It performs simultaneously an independent local search, that is completed by using an IWO method in each memeplex, and a shuffling strategy that allows for the exchange of information between local searches to move toward a global optimum in a technique similar to that used in the shuffled complex evolution algorithm. To show the feasibility, the efficiency and the effectiveness of the SIWO method, numerous simulations are conducted to test it using some benchmark problems. The achieved results of the simulation are compared with PSO algorithm and IWO algorithm, suggesting that SIWO has stable robust behavior on explored tests.


Document Type: Research Article


Publication date: 2007-11-01

More about this publication?
  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
  • Editorial Board
  • Information for Authors
  • Submit a Paper
  • Subscribe to this Title
  • Terms & Conditions
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more