Optimization based on dialectics

The importance of fields of knowledge like biology, psychology, and social sciences as sources of inspiration for computational intelligence has been increasing, deeply influencing evolutionary computation and its applications, inspiring the development of algorithms and methodologies like evolutionary programming and particle swarm optimization. However, the proliferation of biologically-inspired algorithms and solutions indicates the actual focus of researchers and, consequently, philosophy is still faced as a sort of obscure and enigmatic knowledge, despite the power of generalization and the systematic nature of philosophical investigative methods like dialectics. This work proposes an evolutionary class of algorithms based on the materialist dialectics, namely the objective dialectical method, to be used in search and optimization problems. To validate our proposal we developed simulations using several benchmarks functions. The generated results were evaluated in minimization problems concerning how near the results are from the minimum value and how many iterations were used until the estimated minimum value reached a specific threshold value set as a determined precision. This work showed that the proposed dialectical algorithm has good performance in global optimization.