Improving ant colony optimization algorithm for data clustering

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008

The existing Ant Colony Optimization (ACO) Algorithms for the Quadratic Assignment Problem (QAP) are often combined with two kinds of Stochastic Local Search (SLS) methods: the 2-opt local search and the tabu local search. In this paper, these two SLS methods are respectively improved according to the properties of ACO and QAP. For the 2-opt local search, a new random walk strategy is used to avoid a quick stagnation into local optima. Moreover, a forwardlooking strategy is proposed to explore the neighborhood more thoroughly. In the case of tabu local search, a random walk strategy is also employed to avoid getting stuck at local optima. Experimental evaluation of the ACO algorithms combined with the improved local search proposed in this paper are conducted on problems from the well known QAPLIB library. The results demonstrate that each ACO algorithm, combined with its respective improved local search, has a better performance in terms of the quality of the solution returned than the ACO algorithm with the original local search techniques. Moreover, we also noticed that the improved methods outperform each other for different classes of problems.

The existing Ant Colony Optimization (ACO) Algorithms for the Quadratic Assignment Problem (QAP) are often combined with two kinds of Stochastic Local Search (SLS) methods: the 2-opt local search and the tabu local search. In this paper, these two SLS methods are respectively improved according to the properties of ACO and QAP. For the 2-opt local search, a new random walk strategy is used to avoid a quick stagnation into local optima. Moreover, a forward-looking strategy is proposed to explore the neighborhood more thoroughly. In the case of tabu local search, a random walk strategy is also employed to avoid getting stuck at local optima. Experimental evaluation of the ACO algorithms combined with the improved local search proposed in this paper are conducted on problems from the well known QAPLIB library. The results demonstrate that each ACO algorithm, combined with its respective improved local search, has a better performance, in terms of the quality of the solution returned, than the ACO algorithm with the original local search techniques. Moreover, we also noticed that the improved methods outperform each other for different classes of problems.

Applied Mathematics and Computation, 2006

Ant algorithm is a multi-agent systems inspired by the behaviors of real ant colonies function to solve optimization problems. In this paper an ant colony optimization algorithm is developed to solve the quadratic assignment problem. The local search process of the algorithm is simulated annealing. In the exploration of the search space, the evaluation of pheromones which are laid on the ground by ants is used. In this work, the algorithm is analyzed by using current problems in the literature and is compared with other metaheuristics.

Journal of the operational …, 1999

This paper presents HAS-QAP, a hybrid ant colony system coupled with a local search, applied to the quadratic assignment problem. HAS-QAP uses pheromone trail information to perform modifications on QAP solutions, unlike more traditional ant systems that use pheromone trail information to construct complete solutions. HAS-QAP is analysed and compared with some of the best heuristics available for the QAP: two versions of tabu search, namely, robust and reactive tabu search, hybrid genetic algorithm, and a simulated annealing method. Experimental results show that HAS-QAP and the hybrid genetic algorithm perform best on real world, irregular and structured problems due to their ability to find the structure of good solutions, while HAS-QAP performance is less competitive on random, regular and unstructured problems.

2011

Cluster Analysis is a popular data analysis and data mining technique. High quality and fast clustering algorithms play a vital role for users to navigate, effectively organize the data and summarize data. Ant Colony Optimization (ACO), a Swarm Intelligence technique, integrated with clustering algorithms, is being used by many applications for past few years. In this paper we discuss recent improvements on clustering algorithms like PP (Project Pursuit) based on the ACO algorithm for high dimensional data, recent applications of Data Clustering with ACO, application of Ant-based clustering algorithm for object finding by multiple robots in image processing field and the hybrid PSO/ACO algorithm for better optimized results.

Future Generation Computer Systems, 2001

Ant Colonies optimization take inspiration from the behavior of real ant colonies to solve optimization problems. This paper presents a parallel model for ant colonies to solve the quadratic assignment problem (QAP). The cooperation between simulated ants is provided by a pheromone matrix that plays the role of a global memory. The exploration of the search space is guided by the evolution of pheromones levels, while exploitation has been boosted by a tabu local search heuristic. Special care has also been taken in the design of a diversification phase, based on a frequency matrix. We give results that have been obtained on benchmarks from the QAP library. We show that they compare favorably with other algorithms dedicated for the QAP.

Vidyodaya Journal of Science

The multi-objective quadratic assignment problem (mQAP) is an NP-hard combinatorial optimisation problem. Real world problems are concerned with multi-objective problems which optimise more objective functions simultaneously. Moreover, QAP models many real-world optimisation problems, such as network design problems, communication problems, layout problems, etc. One of its major applications is the facility location, which is to find an assignment of all facilities to all locations in the way their total is minimised. The multi-objective QAP considers multiple types of flows between two facilities. Over the last few decades several meta-heuristic algorithms have been proposed to solve the multi-objective QAP, such as genetic algorithms, Tabu search, simulated annealing, and ant colony optimisation. This paper presents a new ant colony optimisation algorithm for solving multiple objective optimisation problems, and it is named as the random weight-based ant colony optimisation algorithm (RWACO). The proposed algorithm is applied to the bi-objective quadratic assignment problem and evaluates the performance by comparing with some recently developed multiobjective ant colony optimisation algorithms. The experimental results have shown that the proposed algorithm performs better than the other multi-objective ACO algorithms considered in this study.

We use the heuristics known as ant colony optimization in the partitioning problem for improving solutions of k-means method. Each ant in the algorithm is associated to a partition, which is modified by the principles of the heuristics; that is, by the random selection of an element, and the assignment of another element which is chosen according to a probability that depends on the pheromone trail (related to the overall criterion: the maximization of the between-classes variance), and a local criterion (the distance between objects). The pheromone trail is reinforced for those objects that belong to the same class. We present some preliminary results, compared to results of other techniques, such as simulated annealing, genetic algorithm, tabu search and k-means. Results are as good as the best of the above methods.

2006

Abstract In recent decades, many meta-heuristics, including genetic algorithm (GA), ant colony optimization (ACO) and various local search (LS) procedures have been developed for solving a variety of NP-hard combinatorial optimization problems. Depending on the complexity of the optimization problem, a meta-heuristic method that may have proven to be successful in the past might not work as well.

2011

This paper presents a new algorithm for clustering which is called an “efficient ant colony optimization clustering algorithm” (EACOC) based on a classic algorithm “LF algorithm”. We have proved the algorithm efficiency when dealt with a big variety of different data as well as providing high quality and converging speed simultaneously. This is considered as the outcome of many changes we have made including redefining the digital manner of ants, setting new formula to find out the degree of similarity and measuring the distance between objects; as well as creating a process to determine the degree of similarity between the collections resulting from the repeated processes. Experimental results show, by using clustering benchmarks indicate, that this suggested algorithm is the best of (LF) Algorithm, as it could defeat the defects found in (LF) involving; the law converging speed and the big number of repeating processes.