9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

Research Article

A Dynamic Step-size Adaptation Roach Infestation Algorithm for Constrained Engineering Optimization Problems

  • @INPROCEEDINGS{10.4108/eai.3-12-2015.2262602,
        author={Ibidun Obagbuwa and Manoj Maharaj},
        title={A Dynamic Step-size Adaptation Roach Infestation Algorithm for Constrained Engineering Optimization Problems},
        proceedings={9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)},
        publisher={ACM},
        proceedings_a={BICT},
        year={2016},
        month={5},
        keywords={swarm intelligence meta-heuristic engineering optimization constrained problem},
        doi={10.4108/eai.3-12-2015.2262602}
    }
    
  • Ibidun Obagbuwa
    Manoj Maharaj
    Year: 2016
    A Dynamic Step-size Adaptation Roach Infestation Algorithm for Constrained Engineering Optimization Problems
    BICT
    EAI
    DOI: 10.4108/eai.3-12-2015.2262602
Ibidun Obagbuwa1,*, Manoj Maharaj1
  • 1: University of KwaZulu-Natal
*Contact email: ibidunobagbuwa@yahoo.com

Abstract

Engineering problems belong to a large and complex category of optimization problems with non-linear and nonconvex functions; conventional methods are no longer sufficient to handle such problems. Meta-heuristic optimization algorithms have been proved in literature for being able to tackle complex problems. A new meta-heuristic algorithm called dynamic step-size roach infestation optimization algorithm based on searching behaviour of cockroaches was published recently. A Simple Euler method was introduced into a roach infestation optimization algorithm for the enhancement of swarm stability and to allow a balance of exploitation and exploration. The results of the experiments, show its superiority over the existing algorithms. In this work the same method was applied; and modified to solve a constrained engineering problem. The results obtained from simulation processes are close to those obtained by other meta-heuristic methods.