Balancing search direction in cultural algorithm for enhanced global numerical optimization

Many meta-heuristics methods are applied to guide the exploration and exploitation of the search space for large scale optimization problems. These problems have attracted much attention from researchers who proposed developed a variety of techniques for locating the optimal solutions. Cultural Algorithm has been recently adopted to solve global numerical optimization problems. In this paper, a modified version of Cultural Algorithm (CA) that uses four knowledge sources in order to incorporate the information obtained from the objective function as well as constraint violation into knowledge structure in the belief space is proposed. The archived knowledge in the proposed approach will be used to enhance the way the belief space influences future generations of problem solvers. The first step is to use the four knowledge sources to guide the direction of the search to more promising solutions. The search is balanced between exploration and exploitation by dynamically adjusting the number of evaluations available for each type of knowledge source based on whether is primarily exploratory or exploitative. The second step selects one local search method to find the nearest solutions to those proposed by the knowledge sources. The proposed work is employed to solve seven global optimization problems in 50 and 100 dimensions, and an engineering application problem. Simulation results show how the approach speeds up the convergence process with very competitive results on such complex benchmarks when compared to other state-of-the-art algorithms.