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

Wireless Sensor Networks WSNs consist of many sensor nodes, which are used for capturing the essential data from the environment and sending it to the Base Station BS. Most of the research has been focused on energy challenges in WSN. There are many notable studies on minimization of energy consumption during the process of sensing the important data from the environment where nodes are deployed. Clustering-based routing protocols are an energy-efficient protocols that improve the lifetime of a wireless sensor network. The objective of the clustering is to decrease the total transmission power by aggregating into a single path for prolonging the network lifetime. However, the problem of unbalanced energy consumption exists in some cluster nodes in the WSNs. In this paper, a hybrid algorithm is proposed for clustering and cluster head CH election. The proposed routing protocol hybridized Penalized Fuzzy C-Means PFCM and Self Organization Map SOM algorithms with LEACH protocol for the optimum numbers of the CHs and the location of them. Simulation results reveal that the proposed algorithm outperforms other existing protocols in terms of network life, number of dead sensor nodes, energy consumption of the network and convergence rate of the algorithm in comparison to the LEACH algorithm.

Hybridization of the LEACH Protocol with Penalized Fuzzy C-Means PFCM and Self-Organization Map SOM Algorithms for Decreasing Energy in Wireless Sensor Networks

Web usage data usually contain quantitative values, and this implies that fuzzy logic can be used to represent such values. The time spent by users on each web page is a part of web usage data, which can be used to analyze users’ browsing behavior. In existing research on fuzzy web mining, the time duration of web pages is shown as trapezoidal membership functions (TMFs), and the number and parameters of TMFs are already predefined. TMFs of each web page are different from those of other web pages. Therefore, instead of using predefined TMFs, in this study, we proposed a new algorithm called CALA-FOMF to find both the number of TMFs and their optimized parameters to mine fuzzy association rules in web usage data using a team of continuous action-set learning automata (CALA). CALA-FOMF contained two steps. In the first step, using a team of CALA, we introduced a new framework. The proposed framework obtained the number of TMFs as inputs and found their optimized parameters. The proposed framework was able to reduce the search space and eliminate inappropriate membership functions during the learning process. In the second step, we proposed a new algorithm using the proposed framework to find an appropriate number of TMFs and their optimized parameters. The performance of the CALA-FOMF approach was compared with that of the fuzzy web mining algorithm, which used uniform TMFs. Experiments on datasets with different sizes confirmed that the proposed CALA-FOMF increased the efficiency of mining fuzzy association rules by extracting optimized TMFs.

CALA-FOMF: a continuous action-set learning automata-based approach to finding optimized membership functions for fuzzy association rules in web usage data

Transactions in web data are huge amounts of data, often consisting of fuzzy and quantitative values. Mining fuzzy association rules can help discover interesting relationships between web data. Th...

Finding Suitable Membership Functions for Mining Fuzzy Association Rules in Web Data Using Learning Automata

In this paper, we present an efficient algorithm for data clustering, which is based on the gravitational search algorithm and a heuristic search approach. In the proposed algorithm, called GSAHS, the gravitational search algorithm is used to find an initial solution for the clustering problem, and then at the next stage, a heuristic search algorithm is applied to improve the quality of the initial solution by thoroughly exploring the both sides of the initial solution. Four benchmark datasets are used to evaluate the performance of the GSAHS in comparison with K-means, genetic algorithm (GA), tabu search (TS), simulated annealing (SA), ant colony optimization (ACO), honey bee mating optimization (HBMO), particle swarm optimization (PSO) and gravitational search algorithm (GSA). The experimental results confirm that the GSAHS can find high quality clusters in all the test datasets. Keywords: cluster analysis, gravitational search algorithm, heuristic search algorithm.

A Robust Data Clustering Approach Using Gravitational Search Algorithm and a Heuristic Search Approach

In﻿this﻿article﻿the﻿authors﻿investigate﻿the﻿application﻿of﻿the﻿heart﻿algorithm﻿for﻿solving﻿unconstraint﻿ numerical﻿optimization﻿problems.﻿Heart﻿algorithms﻿are﻿a﻿novel﻿optimization﻿algorithm﻿which﻿mimics﻿ the﻿heart﻿function﻿and﻿circulatory﻿system﻿procedure﻿in﻿the﻿human﻿beings.﻿It﻿starts﻿with﻿a﻿number﻿of﻿ candidate﻿solutions﻿for﻿the﻿given﻿problem﻿and﻿utilizes﻿the﻿contraction﻿and﻿expansion﻿actions﻿to﻿move﻿ the﻿candidates﻿in﻿the﻿search﻿space﻿for﻿finding﻿optimal﻿solution.﻿The﻿applicability﻿and﻿performance﻿of﻿ the﻿heart﻿algorithm﻿for﻿solving﻿unconstrained﻿optimization﻿problems﻿has﻿been﻿tested﻿using﻿several﻿ benchmark﻿functions.﻿Experimental﻿results﻿show﻿its﻿potential﻿and﻿superiority. KEywORdS Heart Algorithm, Numerical Optimization, Optimization Algorithms, Unconstrained

Abdolreza Hatamlou

Papers

Hybridization of the LEACH Protocol with Penalized Fuzzy C-Means PFCM and Self-Organization Map SOM Algorithms for Decreasing Energy in Wireless Sensor Networks

CALA-FOMF: a continuous action-set learning automata-based approach to finding optimized membership functions for fuzzy association rules in web usage data

Finding Suitable Membership Functions for Mining Fuzzy Association Rules in Web Data Using Learning Automata

A Robust Data Clustering Approach Using Gravitational Search Algorithm and a Heuristic Search Approach

Numerical Optimization Using the Heart Algorithm