A Review on Biological Inspired Computation in Cryptology

Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research

A Real-time Global Optimal Path Planning for mobile robot in Dynamic Environment Based on Artificial Immune Approach

This paper illustrates a method to finding a globaloptimal path in a dynamic environment of known obstacles foran Mobile Robot (MR) to following a moving target. Firstly, theenvironment is defined by using a practical and standard graphtheory. Then, a suboptimal path is obtained by using DijkstraAlgorithm (DA) that is a standard graph searching method. Theadvantages of using DA are; elimination the uncertainness ofheuristic algorithms and increasing the speed, precision andperformance of them. Finally, Continuous Clonal SelectionAlgorithm (CCSA) that is combined with Negative SelectionAlgorithm (NSA) is used to improve the suboptimal path andderive global optimal path. To show the effectiveness of themethod it is compared with some other methods in this area

Hybrid ACO and SVM algorithm for pattern classification

Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO

Toplu taşıma sistemlerinin evrimsel algoritmalarla optimizasyonu

This study aims to examine, regulate, and update the land transportation of the Erzurum Metropolitan Municipality (EMM), Turkey using computerized calculation techniques. In line with these targets, some critical information has been obtained for study: the number of buses, the number of expeditions, the number of bus lines, and the number and maps of existing routes belonging to EMM. By using the information that has been obtained, this study aims at outlining specific outputs according to the input parameters, such as determining the optimal routes, the average travel, and the journey time. Once all of these situations were considered, various optimization algorithms were used to get the targeted outputs in response to the determined input parameters. In addition, the study found that the problem involved in modeling the land transport network of the EMM is in line with the so-called “traveling salesman problem,” which is a scenario about optimization often discussed in the literature. This study tried to solve this problem by using the genetic algorithm, the clonal selection algorithm, and the DNA computing algorithm. The location data for each bus stops on the bus lines selected for the study were obtained from the EMM, and the distances between these coordinates were obtained by using Google Maps via a Google API. These distances were stored in a distance matrix file and used as input parameters in the application and then were put through optimization algorithms developed initially on the MATLAB platform. The study’s results show that the algorithms developed for the proposed approaches work efficiently and that the distances for the selected bus lines can be shortened.Bu çalışma, Erzurum Büyükşehir Belediyesi'nin (EBB) Türkiye kara ulaşımını bilgisayarlı hesaplama teknikleri kullanarak incelemeyi, düzenlemeyi ve güncellemeyi amaçlamaktadır. Bu hedefler doğrultusunda, çalışma için bazı önemli bilgiler: otobüs sayısı, sefer sayısı, otobüs hattı sayısı ve EBB’ye ait mevcut güzergâh sayısı ve haritaları elde edilmiştir. Bu çalışma, elde edilen bilgileri kullanarak, optimal rotaların belirlenmesi, ortalama seyahat ve yolculuk süresi gibi girdi parametrelerine göre belirli çıktıların ana hatlarını çizmeyi amaçlamaktadır. Tüm bu durumlar göz önüne alındığında, belirlenen girdi parametrelerine karşılık hedeflenen çıktıları elde etmek için çeşitli optimizasyon algoritmaları kullanılmıştır. Çalışma, EBB’ nin ulaşım ağının modellenmesindeki problemin, literatürde sıklıkla tartışılan optimizasyonla ilgili bir senaryo olan “gezgin satıcı problemi” ile uyumlu olduğunu bulmuştur. Çalışmada genetik algoritma, klonal seçim algoritması ve DNA hesaplama algoritması kullanılarak bu problem çözülmeye çalışılmıştır. Çalışmada seçilen otobüs hatlarındaki her bir durak için konum bilgisi EBB'den alınmış ve bu koordinatlar arasındaki mesafeler bir Google API üzerinden Google Maps kullanılarak elde edilmiştir. Bu mesafeler bir mesafe matrisi dosyasında saklanmış ve uygulamada giriş parametreleri olarak kullanılmış daha sonra MATLAB platformunda geliştirilen optimizasyon algoritmalarına aktarılmıştır. Çalışmanın sonuçları, önerilen yaklaşımlar için geliştirilen algoritmaların verimli çalıştığını ve seçilen otobüs hatları için mesafelerin kısaltılabileceğini göstermektedir

Hybrid Honey Bees Mating Optimization Algorithm for Identifying the Near-Optimal Solution in Web Service Composition

This paper addresses the problem of optimality in semantic Web service composition by proposing a hybrid nature-inspired method for selecting the optimal or near-optimal solution in semantic Web Service Composition. The method hybridizes the Honey-Bees Mating Optimization algorithm with components inspired from genetic algorithms, reinforcement learning, and tabu search. To prove the necessity of hybridization, we have analyzed comparatively the experimental results provided by our hybrid selection algorithm versus the ones obtained with the classical Honey Bees Mating Optimization algorithm and with the genetic-inspired algorithm of Canfora et al