Preface: Special issue on "Understanding of evolutionary optimization behavior", Part 1.

Understanding of optimization algorithm's behavior is a vital part that is needed for quality progress in the field of stochastic optimization algorithms. To be able to overcome this deficiency, we need to establish new standards for understanding optimization algorithm behavior, which will provide understanding of the working principles behind the stochastic optimization algorithms. In their paper I Evolutionary algorithms and submodular functions: benefits of heavy-tailed mutations i , Quinzan et al. develop suitable Evolutionary Algorithms (EAs) to tackle submodular optimization problems. The paper I Improving convergence in swarm algorithms by controlling range of random movement i by Chaudhary and Banati studies the applicability of the IS technique over different swarm algorithms employing different random distributions. [Extracted from the article]