An Optimization Algorithm Based on Binary Difference and Gravitational Evolution

Universal gravitation is a natural phenomenon. Inspired by Newton's universal gravitation model and based on binary differences strategy, we propose an algorithm for global optimization problems, which is called the binary difference gravitational evolution (BDGE) algorithm. BDGE is a population-based algorithm, and the population is composed of particles. Each particle is treated as a virtual object with two attributes of position and quality. Some of the best objects in the population compose the reference-group and the rest objects compose the floating-group. The BDGE algorithm could find the global optimum solutions through two critical operations: the self-update of reference-group and the interactive-update process between the reference-group and floating-group utilizing the gravitational evolution method. The parameters of BDGE are set by a trial-and-error process and the BDGE is proved that it can converge to the global optimal solution with probability 1. Benchmark functions are used to evaluate the performance of BDGE and to compare it with classic Differential Evolution. The simulation results illustrate the encouraging performance of the BDGE algorithm with regards to computing speed and accuracy.