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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.

In this paper, a Self-learning Collective Intelligence Differential Evolution (SLCIDE) algorithm was proposed to optimize both the architecture and parameters of a Feedforward Neural Network (FNN). In order to improve the exploration-exploitation capability, a new Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-learning mechanism which was designed to adaptively select m top ranked donor vectors was developed by using a widely used unsupervised learning method, k-means. As a result, the proposed approach can be more adaptive and statistically powerful on versatile problems. Then, we evaluated the performances of the proposed SLCIDE approach by studying some numerical optimization problems of CEC 2013 with D = 30 and D = 50. Further, the proposed SLCIDE method was applied to train a FNN on four most popular datasets, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SLCIDE method obtain better results compared with other state-of-the-art algorithms.

Teaching–learning-based optimization (TLBO) has been widely used to solve global optimization problems. However, the optimization problems in various fields are becoming more and more complex. The canonical TLBO is easy to be trapped in the local optimum when dealing with these problems. In this paper, a new TLBO algorithm with collective intelligence concept introduced is proposed, namely collective information-based TLBO (CIBTLBO). CIBTLBO uses the information from the top learners to form CITeachers and uses the neighborhood information of each learner to form NTeachers, and these teachers help other learners learn in the teacher phase. Furthermore, CITeacher also helps in the learner phase. To demonstrate superiority of the proposed algorithm, experiments on 28 benchmark functions from CEC2013 are carried out, and the benchmark functions are set to 10, 30, 50 and 100 dimensions, respectively. The results show that the proposed CIBTLBO algorithm outperforms the other previous related algorithms. [ABSTRACT FROM AUTHOR]