An Improved Binary Bee Colony Algorithm for Satellite Resource Scheduling Method

Abstract

Aiming at the problem of satellite resource scheduling for multi-space targets, drawn on the experience of encoding in the Particle Swarm Optimization (PSO) algorithm, we designed an encoding style to represent the constraint and the solutions to the problem and introduced binary artificial bee colony (BABC) algorithm based on Pareto multi-objective optimization. Compared with the artificial bee colony (ABC) algorithm, the only difference is that BABC used Logistics function mapping the values to the binary. In this paper we made some improvements including population initialization which use the constraint conditions to randomly generate then modify to a feasible solution and candidate solutions generation in a way of crossover used in the Genetic algorithm. In the optimal solution search process, the Pareto optimal solution of the population is recorded, which means a set of differentiated solutions with different advantages on different indexes is obtained. It is convenient to select the corresponding optimal solution according to the user’s preference and the actual situation. The experimental results show that the improved binary artificial bee colony algorithm could solve the satellite resource scheduling problem, which provides a new idea for multi-space target satellite resource scheduling problem.