Differential evolution (DE) is a heuristic optimization method with a relatively simple and efficient form of mutation and crossover and it has been applied to solve many real world optimization problems in real-valued search space. Modified binary differential evolution (MBDE) with a simple binary mutation mechanism based on a logical operation is suitable for dealing with binary and continuous optimization problems. In this study, the modified binary differential evolution is enhanced by using adaptive resonance theory (ART) to classify binary image pattern of population into groups for balancing exploration and exploitation in optimization search. The diversity and convergence of search are both enhanced by applying ART clustering strategy. Different types of optimization problems consisting of test function optimization and topology optimization of structure are used to illustrate the high viability of the proposed algorithm in optimization.