Social Network Analysis

A comparative study of Artificial Bee Colony algorithm

Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Gbest-guided artificial bee colony algorithm for numerical function optimization

https://isiarticles.com/bundles/Article/pre/pdf/7415.pdf

A modified Artificial Bee Colony algorithm for real-parameter optimization

Given an undirected, connected, weighted graph, the leaf-constrained minimum spanning tree (LCMST) problem seeks on this graph a spanning tree of minimum weight among all the spanning trees of the graph that have at least @? leaves. In this paper, we have proposed an artificial bee colony (ABC) algorithm for the LCMST problem. The ABC algorithm is a new metaheuristic approach inspired by intelligent foraging behavior of honeybee swarm. We have compared the performance of our ABC approach against the best approaches reported in the literature. Computational results demonstrate the superiority of the new ABC approach over all the other approaches. The new approach obtained better quality solutions in shorter time.

/pdf/an-artificial-bee-colony-algorithm-for-the-leaf-constrained-4ytcko2q88.pdf

An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem

A swarm intelligence approach to the quadratic minimum spanning tree problem

The multiple traveling salesperson problem (MTSP) is an extension of the well known traveling salesperson problem (TSP). Given m > 1 salespersons and n > m cities to visit, the MTSP seeks a partition of cities into m groups as well as an ordering among cities in each group so that each group of cities is visited by exactly one salesperson in their specified order in such a way that each city is visited exactly once and sum of total distance traveled by all the salespersons is minimized. Apart from the objective of minimizing the total distance traveled by all the salespersons, we have also considered an alternate objective of minimizing the maximum distance traveled by any one salesperson, which is related with balancing the workload among salespersons. In this paper, we have proposed a new grouping genetic algorithm based approach for the MTSP and compared our results with other approaches available in the literature. Our approach outperformed the other approaches on both the objectives.

A new grouping genetic algorithm approach to the multiple traveling salesperson problem

Two metaheuristic approaches for the multiple traveling salesperson problem

Minimum weight dominating set (MWDS) finds many uses in solving problems as varied as clustering in wireless networks, multi-document summarization in information retrieval and so on. It is proven to be NP-hard, even for unit disk graphs. Many centralized and distributed, greedy and approximation algorithms have been proposed for the MWDS problem. However, all the approximation algorithms are limited to unit disk graphs which are primarily used to model wireless networks. This assumption fails when applied to other domains. In this paper, we present two metaheuristic algorithms - a hybrid genetic algorithm and a hybrid ant colony optimization algorithm - for the problem of computing minimum weight dominating set. We compare our results with that of a greedy heuristic as well as the only other metaheuristic algorithm proposed so far in the literature and show that our algorithms are far better than these algorithms.

Alok Singh

Papers

An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem

A swarm intelligence approach to the quadratic minimum spanning tree problem

A new grouping genetic algorithm approach to the multiple traveling salesperson problem

Two metaheuristic approaches for the multiple traveling salesperson problem

Hybrid metaheuristic algorithms for minimum weight dominating set