Abstract:
In this paper we propose an optimization
algorithm for global optimization problems. The proposed
algorithm is named (CA-ImLS) and is based on Cultural
Algorithms and an improved local search approach for
optimization over large-scale continuous spaces. In this paper,
Cultural Algorithm and an improved sub-regional local search
method are hybridized to form CA-ImLS. The original
Cultural Algorithm is extended to have five parallel local
searches that are rooted to its knowledge sources in the belief
space component. This directs the search in multi-directions
and improves the capability of its problem solvers in obtaining
better-quality solutions. The distribution of new search agents
is based on the success of the knowledge sources in which each
knowledge source has its own local search for generating new
agents with better fitness values and enhanced diversity to
avoid stagnation. Experimental results are given for a set of
benchmark optimization functions. Results indicate an average
improvement of 2%-83% over the basic Cultural Algorithm
framework.