iCorr-GAA Algorithm for Solving Complex Optimization Problem

Optimization is widely used to solve problems in many fields. With the development of society, the complexity of optimization problems is also increasing. Genetic algorithm (GA) is one of the most powerful stochastic optimizer. As a well-known GA variant, Correlation-based Genetic Algorithm (Corr-GAA) has been successfully applied to solve these optimization problems. Although highly effective, Corr-GAA tends to converge quickly at early evolution, and may fall into the local optimum in the later evolution stage. Non-uniform mutation operator can effectively improve this situation by adjusting dynamically search step of each iteration. In this paper we present an improved genetic algorithm (iCorr-GAA) that combines Corr-GAA with non-uniform mutation operator to solve complex optimization problems. The performance of the algorithm was evaluated by solving a set of benchmark functions provided for CEC 2014 special session and competition. Experimental results give evidence that iCorr-GAA has good global search capability and fast convergence speed.