Grey Wolf Optimizer

Data Mining - Concepts and Techniques.

The Whale Optimization Algorithm

This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization. The performance of SCA is benchmarked in three test phases. Firstly, a set of well-known test cases including unimodal, multi-modal, and composite functions are employed to test exploration, exploitation, local optima avoidance, and convergence of SCA. Secondly, several performance metrics (search history, trajectory, average fitness of solutions, and the best solution during optimization) are used to qualitatively observe and confirm the performance of SCA on shifted two-dimensional test functions. Finally, the cross-section of an aircraft's wing is optimized by SCA as a real challenging case study to verify and demonstrate the performance of this algorithm in practice. The results of test functions and performance metrics prove that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively. The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces. Note that the source codes of the SCA algorithm are publicly available at http://www.alimirjalili.com/SCA.html .

SCA: A Sine Cosine Algorithm for solving optimization problems

Salp Swarm Algorithm

Data clustering, the process of grouping similar objects in a set of observations is one of the attractive and main tasks in data mining that is used in many areas and applications such as text clustering and information retrieval, data compaction, fraud detection, biology, computer vision, data summarization, marketing and customer analysis, etc. The well-known k-means algorithm, which widely applied to the clustering problem, has the drawbacks of depending on the initial state of centroids and may converge to the local optima rather than global optima. A data clustering algorithm based on the gravitational search algorithm (GSA) is proposed in this research. In this algorithm, some candidate solutions for clustering problem are created randomly and then interact with one another via Newton's gravity law to search the problem space. The performance of the presented algorithm is compared with three other well-known clustering algorithms, including k-means, genetic algorithm (GA), and particle swarm optimization algorithm (PSO) on four real and standard datasets. Experimental results confirm that the GSA is a robust and viable method for data clustering.

Application of gravitational search algorithm on data clustering

The Big Bang–Big Crunch (BB–BC) algorithm is a new optimization method that is based on one of the theories of the evolution of the universe namely the Big Bang and Big Crunch theory. According to this method, in the Big Bang phase some candidate solutions to the optimization problem are randomly generated and spread all over the search space. In the Big Crunch phase, randomly distributed candidate solutions are drawn into a single representative point via a center of population or minimal cost approach. This paper presents BB-BC based novel approach for data clustering. The simulation results indicate the applicability and potential of this algorithm on data clustering.

Data Clustering Using Big Bang–Big Crunch Algorithm

In search of optimal centroids on data clustering using a binary search algorithm

Over the last few decades, many nature-inspired algorithms have been proposed for solving complex and difficult problems. Each algorithm has its own merits and drawbacks. One of the most recent nature-inspired algorithms, which has been applied successfully in many applications, is black hole (BH) algorithm. BH algorithm is a population-based meta-heuristic algorithm that is inspired by the black hole phenomenon. It starts with a random population of solutions to the given optimization problem. The most excellent solution at each iteration which has the best fitness is chosen to be the black hole and the other form the stars. The black hole pulls the stars towards it and causes them to search the problem space for finding optimal solution. In this paper, the application of the BH algorithm on solving travelling salesman problem (TSP) is investigated. The aim of TSP is to find a tour in a set of cities in such a way, each city is visited exactly once and return to the starting city where the length of the tour is minimized. In order to evaluate the efficiency of the BH algorithm, it has been tested on several benchmark data sets and compared to other well-known algorithms. The experimental results show that the BH algorithm can find high-quality solutions compared to genetic algorithm, ant colony optimization and particle swarms optimization algorithms. Moreover, the BH algorithm is faster than other test algorithms.

Solving travelling salesman problem using black hole algorithm

In this paper, we present an efficient algorithm for cluster analysis, which is based on gravitational search and a heuristic search algorithm. In the proposed algorithm, called GSA-HS, the gravitational search algorithm is used to find a near optimal solution for clustering problem, and then at the next step a heuristic search algorithm is applied to improve the initial solution by searching around it. Four benchmark datasets are used to evaluate and to compare the performance of the presented algorithm with two other famous clustering algorithms, i.e. K-means and particle swarm optimization algorithm. The results show that the proposed algorithm can find high quality clusters in all the tested datasets.

Abdolreza Hatamlou

Papers

Application of gravitational search algorithm on data clustering

Data Clustering Using Big Bang–Big Crunch Algorithm

In search of optimal centroids on data clustering using a binary search algorithm

Solving travelling salesman problem using black hole algorithm

Gravitational search algorithm with heuristic search for clustering problems