![]() Following a brief review of notation, Part II introduces new dynamic strategies for managing tabu lists, allowing fuller exploitation of underlying evaluation functions. Part II, in this issue, examines refinements and more advanced aspects of tabu search. Part I also reported successful applications from a wide range of settings, in which tabu search frequently made it possible to obtain higher quality solutions than previously obtained with competing strategies, generally with less computational effort. Part I introduced the fundamental ideas of tabu search as an approach for guiding other heuristics to overcome the limitations of local optimality, both in a deterministic and a probabilistic framework. This is the second half of a two part series devoted to the tabu search metastrategy for optimization problems. The distribution of community size in our method is more in line with the real distribution than those of the baselines at the same time. Our method is shown to be more stable without missing communities and more effective than the baselines with competitive performance. ![]() We conduct experiments on simulated data and real social networking data with ground truths (GT) and compare the proposed method with several baselines. In this work, we develop a novel dynamic community detection algorithm by leveraging the encoding–decoding scheme present in a succinct network representation method to reconstruct each snapshot via a common low-dimensional subspace, which can remove non-significant links and highlight the community structures, resulting in the mitigation of community instability to a large degree. on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (IEEE) pp 513–9), resulting in the problem of instability. Conventional solutions based on static community detection approaches treat each snapshot of dynamic networks independently, which may fragment communities in time (Aynaud T and Guillaume J L 2010 8th Int. The results of the performance of the evaluation function show that this proposed algorithm can find an optimal and more convergent solution compared to modern approaches.ĭetecting communities of highly internal and low external interactions in dynamically evolving networks has become increasingly important owing to its wide applications in divers fields. This algorithm does not need to specify the number of communities in advance and meets the time smoothing limit, and this applies to dynamic real-world and synthetic networks. In the proposed algorithm, work is done on dynamic data. The temporal asymptotic surprise is used as an evaluation function of the algorithm. This paper proposes a multiagent optimization memetic algorithm in complex networks to detect dynamic communities and calls it DYNMAMA (dynamic multiagent memetic algorithm). We can use dynamic graphs to model these types of networks. ![]() In the real world, dynamic networks are evolving, and the problem of tracking and detecting communities at different time intervals is raised. Revealing the structure of a community is one of the essential features of a network, during which remote communities are discovered in a complex network. To this end, we introduce four new systematic frameworks integrating both heuristic and metaheuristic algorithms, illustrating the possible issues that would fuel the desire for researchers to direct their future interest towards developing more effective community detection instances from the context of these frameworks.Ĭomplex networks are used in a variety of applications. Then, we introduce two new taxonomies for community detection algorithms: hybrid metaheuristic and hyper heuristic that can serve as common grounds for designing a collection of new and more effective MCD algorithms. Mainly, we review the main heuristic and metaheuristic based community detection algorithms. The review introduced in this paper attempts to address this issue. Moreover, all the published reviews did not make any direct effort to link heuristic and metaheuristic based community detection approaches, rather, they simply state them separately. The design of other operators, however, remains canonical lacking any intense interest to reflect the domain knowledge. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. ![]() Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem.
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