Various types of social relationships, such as friends and foes, can be represented as signed social networks (SNs) that contain both positive and negative links. Although many community detection (CD) algorithms have been proposed, most of them were designed primarily for networks containing only positive links. Thus, it is important to design CD algorithms which can handle large-scale SNs. To this purpose, we first extend the original similarity to the signed similarity based on the social balance theory. Then, based on the signed similarity and the natural contradiction between positive and negative links, two objective functions are designed to model the problem of detecting communities in SNs as a multiobjective problem. Afterward, we propose a multiobjective evolutionary algorithm, called MEAs-SN. In MEAs-SN, to overcome the defects of direct and indirect representations for communities, a direct and indirect combined representation is designed. Attributing to this representation, MEAs-SN can switch between different representations during the evolutionary process. As a result, MEAs-SN can benefit from both representations. Moreover, owing to this representation, MEAs-SN can also detect overlapping communities directly. In the experiments, both benchmark problems and large-scale synthetic networks generated by various parameter settings are used to validate the performance of MEAs-SN. The experimental results show the effectiveness and efficacy of MEAs-SN on networks with 1000, 5000, and 10,000 nodes and also in various noisy situations. A thorough comparison is also made between MEAs-SN and three existing algorithms, and the results show that MEAs-SN outperforms other algorithms.

With the coming of big data age, the data usually present in a huge magnitude such as TB or more. These data contain both useful and useless information. Therefore, techniques which can effectively analyze these data are in urgent demand. In practice, dealing with Electroencephalographic (EEG) signals with Independent Component Analysis (ICA) approximates to a big optimization problem because it requires real-time, or at least automatic in dealing with signals. Thus, in the Optimization of Big Data 2015 Competition, the problem abstracted from dealing with EEG signals through ICA is modeled as a big optimization problem (BigOpt). Evolutionary optimization techniques have been successfully used in solving various optimization problems, and in the age of big data, they have attracted increasing attentions. Since the multi-agent genetic algorithm (MAGA) shows a good performance in solving large-scale problems, in this paper, based on the framework of MAGA, an MAGA is proposed for solving the big optimization problem, which is labeled as MAGA-BigOpt. In MAGA-BigOpt, the competition and self-learning operators are redesigned and combined with crossover and mutation operators to simulate the cooperation, competition, and learning behaviors of agents. Especially, in the self-learning operator, agents quickly find decreasing directions to improve itself with a heuristic strategy. In the experiments, the performance of MAGA-BigOpt is validated on the given benchmark problems from the Optimization of Big Data 2015 Competition, where both the data with and without noise are used. The results show that MAGA-BigOpt outperforms the baseline algorithm provided by the competition in both cases with lower computational costs.

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