Benchmarking a hybrid DE-RHC algorithm on real world problems

Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Through CEC 2005 to CEC 2010 competitions, many different algorithms have been proposed to solve continuous problems. The advances on this type of problems are of capital importance as many real-world problems from very different domains (biology, engineering, data mining, etc.) can be formulated as the optimization of a continuous function. For this reason, we have proposed a hybrid DE-RHC algorithm that combines the search strength of Differential Evolution with the explorative ability of a Random Hill Climber, which can help the Differential Evolution algorithm to reach new promising areas in difficult fitness landscapes, such as those than can be found on real-world problems. To evaluate this approach, the benchmark problems proposed in the "Testing Evolutionary Algorithms on Real-world Numerical Optimization Problems" CEC 2011 special session have been considered.