Simulation based multi-objective evolutionary algorithm for electronic reconnaissance satellites scheduling problem

A multi-objective chance constrained programming model (MOCCPM) for electronic reconnaissance satellites scheduling problem (ERSSP) is presented. MOCCPM takes the uncertainties in the course of satellite electronic reconnaissance into account, as well as the capabilities and usage restrictions of the electronic reconnaissance satellites. Then a Monte Carlo simulation based multi-objective evolutionary algorithm (MCBMOEA) is proposed. Taking full advantage of the heuristic information related to the target, the MCBMOEA can construct the initial solutions and avoid converging slowly in the process of evolution. Penalty function based fitness assignment and Pareto optimality based selection ensures the efficient optimization effort of the algorithm. Elitism mechanism is adopted to prevent losing non-dominated individuals generated during the evolutionary process and speed up the convergence of the algorithm. Problem specific sequence swap crossover and mutation operator ensures the feasibility and diversity of the offspring so as to prevent the algorithm from falling into local optimum. Monte Carlo sampling is to address the stochastic nature of ERSSP. The experiment results show that MCBMOEA can solve the problem effectively.