Grey Wolf Optimizer

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

/pdf/community-detection-in-graphs-34rogm3r6p.pdf

Community detection in graphs

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

The Whale Optimization Algorithm

Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.

/pdf/multiobjective-immune-algorithm-with-nondominated-neighbor-3m342iujs2.pdf

Multiobjective immune algorithm with nondominated neighbor-based selection

Community structure is one of the most important properties in networks, and community detection has received an enormous amount of attention in recent years. Modularity is by far the most used and best known quality function for measuring the quality of a partition of a network, and many community detection algorithms are developed to optimize it. However, there is a resolution limit problem in modularity optimization methods. In this study, a memetic algorithm, named Meme-Net, is proposed to optimize another quality function, modularity density, which includes a tunable parameter that allows one to explore the network at different resolutions. Our proposed algorithm is a synergy of a genetic algorithm with a hill-climbing strategy as the local search procedure. Experiments on computer-generated and real-world networks show the effectiveness and the multiresolution ability of the proposed method.

/pdf/memetic-algorithm-for-community-detection-in-networks-1m6kp2nq4c.pdf

Memetic algorithm for community detection in networks.

Self-organizing genetic algorithm based tuning of PID controllers

Inspired by the mechanism of small-world phenomenon, some small-world optimization operators, mainly including the local short-range searching operator and random long-range searching operator, are constructed in this paper. And a new optimization algorithm, Small-World Optimization Algo-rithm (SWOA) is explored. Compared with the corresponding Genetic Algorithms (GAs), the simulation experiment results of some complex functions optimization indicate that SWOA can enhance the diversity of the population, avoid the prematurity and GA deceptive problem to some extent, and have the high convergence speed. SWOA is shown to be an effective strategy to solve complex tasks.

https://link.springer.com/content/pdf/10.1007%2F11881223_33.pdf

Small-World optimization algorithm for function optimization

Using data from a 2005 Survey of rural-urban migrants in Shenzhen, this paper investigates intentions of two groups of migrants. We use the birth years from 1970 to 1980 as a reasonable range of dividing lines to separate the two groups. For each year we divide the sample into those born before that year and those born in or after that year. These are referred to as the old and the new generation, respectively. Three possible development trajectories are considered: settling in cities, returning home to seek a nonagricultural job, and returning home to farm. We find that members of the new generation have stronger desires to do non-farm work, and returning to seek a nonagricultural job has become the most important planned trajectory for this generation. Sharp differences exist between the two generations in the reasons that underlie their intentions. For the old generation, conditions such as age, family responsibility, and type of job are important determinants of intentions, while other conditions such as initial migration motives, social capital, and socioeconomic conditions of origin areas are important for the intentions of the new generation. Thus the new generation is more likely to view migration as a form of investment with the accumulation of human capital and social capital. Those migrants from the old generation who have higher education levels also intend to seek non-farm jobs. However, because of the combined effects of life cycle and the market transition in China, these intentions are not as strong as those of the new generation. We discuss economic and policy implications of our findings.

/pdf/floating-choices-a-generational-perspective-on-intentions-of-c0o2evm3sg.pdf

Haifeng Du

Papers

Multiobjective immune algorithm with nondominated neighbor-based selection

Memetic algorithm for community detection in networks.

Self-organizing genetic algorithm based tuning of PID controllers

Small-World optimization algorithm for function optimization

Floating Choices: A Generational Perspective on Intentions of Rural–Urban Migrants in China