Evolutionary algorithms (EAs) have shown their competitiveness in solving feature selection (FS) problem. However, when facing high-dimensional data with a number of instances, there are two challenges for the existing EAs. (1) The increasing number of features causes the search space of EAs to grow exponentially, which is known as the “curse of dimensionality". (2) The increasing number of instances not only increases the evaluation cost of EAs, but also may degrade the quality of obtained feature subsets. To tackle the two challenges simultaneously, this paper proposes an instance selection (IS) assisted evolutionary FS algorithm, named ISA-EFS. In ISA-EFS, a complementary feature grouping strategy is first suggested, with which the search is performed on the feature group level instead of the single feature level, and the “curse of dimensionality" can be solved effectively. Based on the grouping strategy, two new evolutionary (grouping-oriented crossover and mutation) operators are designed, which achieve the feature subsets with good quality. Then, a novel instance selection algorithm is developed to select a small number of “representative" instances and used for high-dimensional feature selection (HDFS). In ISA-EFS, the suggested IS and FS algorithms are carried on alternately. Meanwhile, since IS is designed to assist FS, the computational resources are gradually removed from IS to FS, with which the quality of feature subsets obtained by ISA-EFS is continuously improved. Experimental results on 12 high-dimensional datasets with a number of instances demonstrate the effectiveness and efficiency of the proposed ISA-EFS, when compared with six state-of-the-art FS algorithms.

This article mainly investigates how to reduce the communication cost in multiagent system (MAS) for distributed optimization. First, a continuous-time distributed optimization model based on MAS is proposed for resource allocation (RA) with periodic communication. All agents in the system do not need to be in constant contact with their neighbors, but contact at set intervals. This will greatly reduce the communication consumption of the system. Second, to further reduce the communication cost, MAS with event-triggered communication is proposed based on periodic communication. It is proved that the system is convergent to an optimal solution of the investigated problem subject to bound and equality constraints. Finally, two examples with simulations are given to verify the performance of the proposed system.

In this article, a discrete-time distributed optimization algorithm is proposed for solving the economic dispatch (ED) problem with some groups of generator units to communicate over a connected graph, which is independent of the power system. The ED problem is converted to a distributed optimization problem with an objective of the sum of individual convex functions and constraints of local generators. Based on the optimal conditions, a class of distributed algorithms is designed to find the solution to the ED problem. The distributed algorithm can be realized as a multiagent system with a connected graph, whose convergence can be proved using the dynamic analysis method. Moreover, experiments with simulations are presented to demonstrate the performance of the proposed algorithm.

In this paper, a continuous-time multiagent system is proposed for solving optimal resource allocation problems with local allocation feasible constraints. In the system, all the primal agents are divided into different groups. We use dual variables which describe the dual agents to represent the groups of the original agents. The groups of dual agents are used to communicate with others on behalf of the primal agents to reduce communication costs. That is to say, primal agents aim to seek their own optimal solutions by using local information. And dual agents represent primal agents to communicate with other agents in different groups by using the whole group information. The two kinds of agents cooperate to find the optimal solution of the problem. In this way, we only need to know the connections of dual agents to design the multiagent network, and do not need to consider the connections of the primal agents. So the communication cost and the amount of variables will be largely reduced especially for large-scale problem. Furthermore, it is proved that the multiagent system can reach consensus with respect to the dual variables. At the same time, the primal variables are convergent to the optimal solutions of the optimization problem under some certain assumptions on the communication network. For large-scale problem if we take the groups as areas, then the system is suitable for multiarea problem. Simulation results are presented to demonstrate the performance of the proposed multiagent system.

In this paper, a continuous-time distributed optimization algorithm based on multi-agent system is proposed for solving the distributed least absolute deviation problems subject to hybrid constraints. In the multi-agent network, each of the L1-norm functions is realized using the projection operator. Meanwhile, each agent must be subject to the local hybrid constraints. Then all the agents constitute a network with connected graph to cooperate to seek the optimal solutions with consensus. The performance of the proposed distributed algorithm is illustrated using a numerical example with simulations.