Memetic Computing最新文献

Grid-connected microgrids comprising renewable energy, energy storage systems and local load, play a vital role in decreasing the energy consumption of fossil diesel and greenhouse gas emissions. A distribution power network connecting several microgrids can promote more potent and reliable operations to enhance the security and privacy of the power system. However, the operation control for a multi-microgrid system is a big challenge. To design a multi-microgrid power system, an intelligent multi-microgrids energy management method is proposed based on the preference-based multi-objective reinforcement learning (PMORL) techniques. The power system model can be divided into three layers: the consumer layer, the independent system operator layer, and the power grid layer. Each layer intends to maximize its benefit. The PMORL is proposed to lead to a Pareto optimal set for each object to achieve these objectives. A non-dominated solution is decided to execute a balanced plan not to favor any particular participant. The preference-based results show that the proposed method can effectively learn different preferences. The simulation outcomes confirm the performance of the PMORL and verify the viability of the proposed method.

Constrained multi-objective problems (CMOPs) require balancing convergence, diversity, and feasibility of solutions. Unfortunately, the existing constrained multi-objective optimization algorithms (CMOEAs) exhibit poor performance when solving the CMOPs with complex feasible regions. To solve this shortcoming, this work proposes an improved algorithm named the CMOEA-TCP, which maintains two populations cooperating to push the solutions to approximate the constrained Pareto front. Specifically, one population is obtained by the Pareto-based method and aims to strengthen the algorithm’s convergence ability. Meanwhile, another population is maintained by decomposition-based method and devoted to improving its diversity. The two populations work cooperatively during the entire evolution process with the constraint-handling technique. The performance of the CMOEA- TCP is verified on three benchmark suites with 34 problems. The experimental results demonstrate that the CMOEA-TCP can achieve performance comparable to or better than the other six state-of-the-art CMOEAs on the majority of considered problems.

Feature selection, as one of the dimension reduction methods, is a crucial processing step in dealing with high-dimensional data. It tries to preserve feature subset representing the whole feature space, which aims to reduce redundancy and increase the classification accuracy. Since the two objectives are usually in conflict with each other, feature selection is modeled as a multi-objective problem. However, the high search space and discrete Pareto front makes it not easy for existing evolutionary multiobjective algorithms. Classic evolutionary computation method, which is often applied to feature selection problem straightforwardly, gradually exposes its inefficiency in searching process. Hence, a particle swarm optimization based multiobjective memetic algorithm for high-dimensional feature selection is designed in this paper to deal with above shortcomings. Its basic idea is to model feature selection as a multiobjective optimization problem by optimizing the number of features and the classification accuracy in supervised condition simultaneously, in which information entropy based initialization and adaptive local search are designed to improve the search efficiency. Moreover, a new particle velocity update rule considering both convergence and diversity of solutions is designed to update particles, and a fast discrete nondominated sorting strategy is designed to rank the Pareto solutions. These strategies enable the proposed algorithm to gain better performance on both the quality and size of feature subset. The experimental results show that the proposed algorithm can improve the quality of Pareto fronts evolved by the state-of-the-art algorithms for feature selection.