Center-based initialization for large-scale black-box problems

  • Authors:

  • Shahryar Rahnamayan;G. Gary Wang

  • Affiliations:

  • University of Ontario Institute of Technology, Faculty of Engineering and Applied Science, Oshawa, ON, Canada;Simon Fraser University, Mechatronic Systems Engineering, Surrey, BC, Canada

  • Venue:
  • AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
  • Year:
  • 2009

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Abstract

Nowadays, optimization problems with a few thousands of variables become more common. Population-based algorithms, such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Evolutionary Strategies (ES) are commonly used approaches to solve complex large-scale problems from science and engineering. These approaches all work with a population of candidate solutions. On the other hand, for high-dimensional problems, no matter what is the individuals' distribution, the population i? highly sparse. Therefore, intelligent employment of individual candidates can play a crucial role to find optimal solution(s) faster. The most majority of population-based algorithms utilize pseudo-random population initialization when there is no a priori knowledge about the solution. In this paper, a center-based population initialization is proposed and investigated on seven benchmark functions. The obtained results are compared with the results of Normal, Pseudo Random, and Latin Hypercube population initialization schemes. Furthermore, the advantages of the proposed center-based sampling method are investigated by a mathematical proof and also Monte Carlo (simulation) method. The detailed experimental verifications are provided for problems with 50, 500, and 1000 dimensions.