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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Peter Bamidele Shola
University of Ilorin, Ilorin, Nigeria
Computer Science & IT Engineering

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The Cheapest Shop Seeker : A New Algorithm For Optimization Problem in a Continous Space Peter Bamidele Shola
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (613.686 KB) | DOI: 10.11591/ijai.v5.i3.pp119-126

Abstract

In this paper a population-based meta-heuristic algorithm for optimization problems in a continous space is presented.The algorithm,here called cheapest shop seeker is modeled after a group of shoppers seeking to identify the cheapest shop (among many available) for shopping. The  algorithm was tested on many benchmark functions with the result  compared with those from some other methods. The algorithm appears to  have a better  success  rate of hitting the global optimum point  of a function  and of the rate of convergence (in terms of the number of iterations required to reach the optimum  value) for some functions  in spite  of its simplicity.
An Algorithm for Continuous Optimization Problems using Hybrid Particle Updating Method Peter Bamidele Shola; L B Asaju
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 1: July 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i1.pp164-173

Abstract

Optimization problem is one such problem commonly encountered in many area of endeavor, obviously due to the need to economize the use of the available resources in many problems. This paper presents a population-based meta-heuristic algorithm   for solving optimization problems in a continous space. The algorithm, combines a form of cross-over technique with a position updating formula based on the instantaneous global best position to update each particle position .The algorithm was tested and compared with the standard particle swarm optimization (PSO)  on many benchmark functions. The result suggests a better performance of the algorithm over the later in terms of reaching (attaining) the global optimum value (at least for those benchmark functions considered) and the rate of convergence in terms of the number of iterations required reaching the optimum values.
Co-Authors L B Asaju