With the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in a ship microgrid. In order to balance the two non-compatible objectives, a new differential evolution variant, which is named as SaCIDE-r, was proposed to solve the optimization problem. In this algorithm, a Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-adaptive mechanism which was developed to avoid introducing extra control parameters. Further, to avoid being trapped in local optima, a re-initialization mechanism was developed. Then, we have evaluated the performances of the proposed SaCIDE-r approach by studying some numerical optimization problems of Congress on Evolutionary Computation (CEC) 2013 with D = 30, compared with seven stateof-the-art DE algorithms. Moreover, the proposed SaCIDE-r method was applied for economic scheduling of a shipboard microgrid under different cases compared with other multi-objective optimizing methods, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SaCIDE-r method might be a feasible solution for such a kind of optimization problem. INDEX TERMS Shipboard microgrid, global optimization, collective intelligence (CI), differential evolution (DE).

As an easily used and powerful heuristic search technique based on population, Differential Evolution (DE) algorithm has been widely applied for various global optimization and real engineering problems. Nevertheless, as with other Evolutionary Algorithms (EA), DE could not avoid from premature convergence due to over concentrated population, which could be called losing population diversity. In order to enhance its performance, we propose a Neutral Mutation (NM) operator for DE algorithm. This novel operator is inspired by neutral theory of molecular evolution, which claims that most mutations at the molecular level are neutral. That is to say, most variations observed are with neither advantage nor disadvantage fitness. Thus, they would not affect an organism’s ability to survive and reproduce. The NM operator maintains slightly deleterious trial vectors, which we called neutral or nearly neutral, with a certain probability in the conventional selection operator of DE. Besides, some of these trial vectors have a chance to be neutrally mutated within the search domain randomly. As a result, the population is diversified with costing negligible Function Evaluations (FEs). Comprehensive experimental results demonstrate that the presented NM operator could improve population diversity to some extent, especially when the population is not divergent at all.