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Solving combinatorial optimization problems (COPs) poses a significant challenge in various application domains. The NP-hardness of many COPs necessitates the integration of meta-heuristics to effectively tackle these problems by leveraging the strengths of each meta-heuristic. However, hybrid meta-heuristics lack a mechanism to determine when to activate specific techniques during the search process. To address these limitations and enhance the overall generality, this study proposes a novel agent-based hyper-heuristic framework, referred to as IAFCO, for solving COPs. The proposed framework imbues the search strategy with greater intelligence and information, resulting in improved performance. The proposed framework comprises 4 coalitions that leverage different search perspectives to enhance the search process: a global coalition, a local coalition, a learning coalition, and a perturbation coalition. Additionally, a control agent is introduced to coordinate the coalitions. Using a reinforcement learning mechanism, the control agent autonomously selects the most suitable coalition of agents based on the search state. The set of states employed in this study is problem-independent and encompasses all possible states that may arise during the search process. Furthermore, some agents adopt chaotic numbers instead of random numbers to maintain diversity. To showcase the effectiveness of the proposed framework, its performance is evaluated on the software modularization problem (SMP) -also known as software clustering- and community detection problem (CDP) in complex networks as 2 case studies. The experiments on SMP's software systems, including 10 applications and 10 folders of Mozilla Firefox, and comparison with 21 modularization algorithms and experiments on CDP's networks, including 9 real-world networks, and comparison with 22 state-of-the-art community detection algorithms, indicate that the proposed framework is comparable to other existing methods. The source codes and datasets of this paper are available at https://github.com/mahjoubeh-t/IAFCO. [ABSTRACT FROM AUTHOR]

On the basis of network analysis, and within the context of modeling imprecision or vague information with fuzzy sets, we propose an innovative way to analyze, aggregate and apply this uncertain knowledge into community detection of real-life problems. This work is set on the existence of one (or multiple) soft information sources, independent of the network considered, assuming this extra knowledge is modeled by a vector of fuzzy sets (or a family of vectors). This information may represent, for example, how much some people agree with a specific law, or their position against several politicians. We emphasize the importance of being able to manage the vagueness which usually appears in real life because of the common use of linguistic terms. Then, we propose a constructive method to build fuzzy measures from fuzzy sets. These measures are the basis of a new representation model which combines the information of a network with that of fuzzy sets, specifically when it comes to linguistic terms. We propose a specific application of that model in terms of finding communities in a network with additional soft information. To do so, we propose an efficient algorithm and measure its performance by means of a benchmarking process, obtaining high-quality results. [ABSTRACT FROM AUTHOR]

On the basis of network analysis, and within the context of modeling imprecision or vague information with fuzzy sets, we propose an innovative way to analyze, aggregate and apply this uncertain knowledge into community detection of real-life problems. This work is set on the existence of one (or multiple) soft information sources, independent of the network considered, assuming this extra knowledge is modeled by a vector of fuzzy sets (or a family of vectors). This information may represent, for example, how much some people agree with a specific law, or their position against several politicians. We emphasize the importance of being able to manage the vagueness which usually appears in real life because of the common use of linguistic terms. Then, we propose a constructive method to build fuzzy measures from fuzzy sets. These measures are the basis of a new representation model which combines the information of a network with that of fuzzy sets, specifically when it comes to linguistic terms. We propose a specific application of that model in terms of finding communities in a network with additional soft information. To do so, we propose an efficient algorithm and measure its performance by means of a benchmarking process, obtaining high-quality results.

Many real-world networks have a topological structure characterized by cohesive groups of vertices. To perform the task of identifying such subsets of vertices, community detection in networks has aroused the interest of researchers and practitioners alike. In spite of the existence of various efficient community detection algorithms in the literature, most of them uses global information about the network, not applicable to distributed networks. This paper proposes a genetic-based algorithm to detect communities in directed networks based on local information to generate the offspring. The major difference between the proposed strategy and those found in the literature is the way of exploiting target regions of interest in the solution space. This step is directly influenced by the crossover operator that depends largely on the individual representation. In the introduced strategy, GA-LP, the individual is locally stored in the vertices as labels, what brings more flexibility in the system to be adapted to address applications that involve, for example, dynamic networks. In computational experiments, the proposed strategy showed an outstanding performance, being fast, achieving the best results on average in the networks tested. [ABSTRACT FROM AUTHOR]

Community structure is an important feature in complex networks which has great significant for organization of networks. The community detection is the process of partitioning the network into some communities in such a way that there exist many connections in the communities and few connections between them. In this paper a Michigan memetic algorithm; called MLAMA-Net; is proposed for solving the community detection problem. The proposed algorithm is an evolutionary algorithm in which each chromosome represents a part of the solution and the whole population represents the solution. In the proposed algorithm, the population of chromosomes is a network of chromosomes which is isomorphic to the input network. Each node has a chromosome and a learning automaton (LA). The chromosome represents the community of corresponding node and saves the histories of exploration. The learning automaton represents a meme and saves the histories of the exploitation. The proposed algorithm is a distributed algorithm in which each chromosome locally evolves by evolutionary operators and improves by a local search. By interacting with both the evolutionary operators and local search, our algorithm effectively detects the community structure in complex networks and solves the resolution limit problem of modularity optimization. To show the superiority of our proposed algorithm over the some well-known algorithms, several computer experiments have been conducted. The obtained results show MLAMA-Net is effective and efficient at detecting the community structure in complex networks. [ABSTRACT FROM AUTHOR]