Length Scale-Based Differential Evolution

Abstract

Differential Evolution (DE) has shown a superior performance for solving global continuous optimization problems. The crucial idea of DE is modifying the population of the candidate solutions toward the weighted differences of randomly selected candidate solutions. In this paper, we propose the length scale-based DE which utilizes the obtained information of a landscape analysis metric, the length scale metric, to enhance its own performance. Landscape analysis methods attempt to gain the properties of optimization problems. For two sample points, length scale metric calculates their objective function changes with respect to the distance between them. By computing length scale values of all possible pairs of candidate solutions, DE can employ the pairs of candidate solutions with the greater length scale values to calculate the difference vector in its mutation operator. The length scale-based DE is evaluated on CEC-2014 benchmark functions. Two dimensions, 50 and 100, are considered for benchmark functions. Simulation results confirm that the proposed algorithm obtains a promising performance on the majority of the benchmark functions on both dimensions.