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

Solving﻿hard﻿problems﻿like﻿the﻿Travelling﻿Salesman﻿Problem﻿(TSP)﻿is﻿a﻿major﻿challenge﻿faced﻿by﻿ analysts﻿even﻿though﻿many﻿techniques﻿are﻿available.﻿The﻿main﻿goal﻿of﻿TSP﻿is﻿that﻿a﻿number﻿of﻿cities﻿ should﻿be﻿visited﻿by﻿a﻿salesman﻿and﻿return﻿to﻿the﻿starting﻿city﻿along﻿a﻿number﻿of﻿possible﻿shortest﻿ paths.﻿TSP﻿is﻿although﻿looking﻿a﻿simple﻿problem,﻿but﻿ it﻿ is﻿an﻿important﻿problem﻿of﻿ the﻿classical﻿ optimization﻿problems﻿that﻿are﻿difficult﻿to﻿solve﻿conventionally.﻿It﻿has﻿been﻿proved﻿that﻿solving﻿TSP﻿ by﻿the﻿conventional﻿approaches﻿in﻿a﻿reasonable﻿time﻿is﻿not﻿possible.﻿So,﻿the﻿only﻿feasible﻿option﻿left﻿ is﻿to﻿use﻿heuristic﻿algorithms.﻿In﻿this﻿article,﻿we﻿apply﻿and﻿investigate﻿a﻿new﻿heuristic﻿approach﻿called﻿ Heart﻿algorithm﻿to﻿solve﻿Travelling﻿Salesman﻿Problem.﻿It﻿simulates﻿the﻿heart﻿action﻿and﻿circulatory﻿ system﻿procedure﻿in﻿the﻿human﻿beings﻿for﻿searching﻿the﻿problem﻿space.﻿The﻿Heart﻿algorithm﻿has﻿ the﻿advantages﻿of﻿strong﻿robustness,﻿fast﻿convergence,﻿fewer﻿setting﻿parameters﻿and﻿simplicity.﻿The﻿ results﻿of﻿the﻿Heart﻿algorithm﻿on﻿several﻿standard﻿TSP﻿instances﻿is﻿compared﻿with﻿the﻿results﻿of﻿the﻿ PSO﻿and﻿ACO﻿algorithms﻿and﻿show﻿that﻿the﻿Heart﻿algorithm﻿performs﻿well﻿in﻿finding﻿the﻿shortest﻿ distance﻿within﻿the﻿minimum﻿span﻿of﻿time. KEywoRdS Heart Algorithm, Heuristic Algorithms, Meta-heuristic Approaches, Travelling Salesman Problem

Solving Travelling Salesman Problem Using Heart Algorithm

This study proposes a novel routing algorithm using Q-learning. Q-learning is a machine learning (artificial intelligence) algorithm using the reinforcement learning policy which can be used to solve problems for which there are different ways to reach their goal. The proposed algorithm, the Modified Q-learning routing algorithm (MQRA), has eliminated the episodes of Q-learning required to gradually learn in different stages and this has made it a rapid routing algorithm. MQRA can be used in various types of networks. This study uses MQRA in mobile ad-hoc networks, its generalization to fisheye state routing (FSR) (a routing algorithm) and its performance results are compared with the standard FSR. Experimental results confirm the applicability and potential of the proposed algorithm.

https://cke.um.ac.ir/article_38867_b4b3d0fff1a60160de95413ee0a1179f.pdf

A Novel Routing Algorithm for Mobile ad-hoc Networks Based on Q-learning and its Generalization to FSR Routing Protocol

Variable ordering heuristics are used in constraint satisfaction algorithms. Different characteristics of various variable ordering heuristics are complementary. Therefore we have tried to get the advantages of all heuristics to improve search algorithms performance for solving constraint satisfaction problems. This paper considers combinations based on products and quotients, and then a newer form of combination based on weighted sums of ratings from a set of base heuristics, some of which result in definite improvements in performance.

/pdf/combining-variable-ordering-heuristics-for-improving-search-mftlzma3a8.pdf

Combining Variable Ordering Heuristics for Improving Search Algorithms Performance

Numerical Optimization Using the Heart Algorithm

In﻿this﻿article,﻿a﻿novel﻿watermarking﻿method﻿is﻿proposed﻿which﻿applies﻿the﻿substitution﻿of﻿LSB﻿for﻿ low-value﻿parts﻿of﻿the﻿image﻿to﻿embed﻿confidential﻿data.﻿The﻿watermark﻿bits﻿are﻿encoded﻿before﻿ being﻿placed.﻿Also,﻿the﻿data﻿on﻿the﻿diameters﻿of﻿the﻿host﻿image﻿blocks﻿are﻿selected﻿on﻿the﻿basis﻿of﻿ an﻿irregular﻿determined﻿algorithm﻿that﻿is﻿resistant﻿to﻿attacks.﻿The﻿host﻿image﻿applied﻿in﻿the﻿proposed﻿ algorithm﻿ should﻿ be﻿ grayscale.﻿ The﻿ proposed﻿ algorithm﻿ uses﻿ three﻿ bits﻿ to﻿ embed﻿ data﻿ that﻿ will﻿ increase﻿the﻿capacity.﻿In﻿the﻿conventional﻿LSB﻿method,﻿in﻿host﻿image﻿matrix,﻿data﻿are﻿stored﻿in﻿the﻿ consecutive﻿numbers,﻿but﻿in﻿this﻿proposed﻿scheme,﻿the﻿host﻿image﻿blocks﻿are﻿divided﻿into﻿separate﻿n﻿ ×﻿n﻿parts﻿and﻿the﻿diameter﻿of﻿each﻿block﻿are﻿placed﻿in﻿a﻿vector.﻿Then﻿embedded﻿watermarking﻿bits﻿in﻿ a﻿vector﻿of﻿numbers﻿is﻿done,﻿from﻿the﻿resulting﻿vector﻿a﻿new﻿block﻿is﻿created,﻿and﻿finally,﻿that﻿block﻿ is﻿stored﻿in﻿a﻿new﻿matrix.﻿Then﻿the﻿host﻿image﻿block﻿is﻿overwritten﻿which﻿results﻿in﻿a﻿watermarked﻿ image﻿with﻿high﻿capacity. KeyWoRDS Digital Watermarking, Image Authentication, Reversible Data Embedding, Watermarking Capacity

Abdolreza Hatamlou

Papers

Solving Travelling Salesman Problem Using Heart Algorithm

A Novel Routing Algorithm for Mobile ad-hoc Networks Based on Q-learning and its Generalization to FSR Routing Protocol

Combining Variable Ordering Heuristics for Improving Search Algorithms Performance

Numerical Optimization Using the Heart Algorithm

Watermarking of Digital Images With the Substitution of Low-Value Bits to Increase Capacity