Divya Kumar

Portfolio optimization using novel co-variance guided Artificial Bee Colony algorithm

Abstract Although the use of evolutionary algorithms and fuzzy logic for portfolio optimization is an established research area, this field remains fascinating because of its important financial aspects. The field is brisk and it trances as there always remain research issues which are yet to explore. The problem of portfolio optimization comprises of finding an optimal distribution of funds among various available securities so as to maximize the return and minimize the risk. Artificial Bee Colony (ABC) is one of the effectual and widely used optimization technique based on swarm intelligence. Mixing co-variance principles with ABC algorithm assists in quick convergence with more precision. This paper presents a novel co-variance guided Artificial Bee Colony algorithm for portfolio optimization. As portfolio optimization consists of simultaneous optimization of multiple conflicting objectives, this algorithm is named as Multi-objective Co-variance based ABC (M-CABC). The efficacy of the proposed algorithm is tested on benchmark problems of portfolio optimization from the OR-library. The results validate the adept performance of the proposed algorithm in finding various optimal trade-off solutions simultaneously handling realistic constraints. The article concludes with exhaustive post-result analysis and observatory remarks to bring out some of the crucial properties of optimal portfolios.

Security Vs cost: An issue of multi-objective optimization for choosing PGP algorithms

PGP (Pretty Good Privacy) is most widely used standard in the world for securing electronic mails. It promises for confidentiality, integrity and authentication to its users. These security services are provided at a cost of various cryptographic algorithms. Given a data, choosing particular algorithms for its security, according to the user requirements, is a non-trivial task. As various algorithms with different security levels and cost are available. In this paper we have proposed a meta-heuristic based on Evolutionary Multi-objective Optimization for selecting appropriate algorithms for PGP according to the user requirements of cost and security levels.

Routing Path Determination Using QoS Metrics and Priority Based Evolutionary Optimization

Last decade has noticed an enormous growth of Internet based information services and applications such as Email, Teleconferencing, Videophony or VoIP etc. All these applications have different QoS (Quality of Service) expectations (bandwidth, delay, jitter and reliability etc). The purpose and the benefits from these applications could be blemished if the underlying communication network does not fulfills the QoS requirements. However, different applications have different prioritized QoS requirements. Some applications put heavy demands of bandwidth, some require a more degree of reliability while some expect a negligible response time and so on. So there is always a need of routing strategy which is not only efficient and precise but guarantees the QoS measure as according to applications priorities also. Tackling the issue, this paper presents the use of priority based Evolutionary Multi-objective Optimization algorithms to find the optimal routes for the data flows of various QoS classes via optimizing multiple QoS parameters namely response time, bandwidth requirements and reliability according to their importance for the application under consideration.

Incorporating logic in Artificial Bee Colony (ABC) algorithm to solve first order logic problems: The logical ABC

The fascination for generating reasons and drawing inferences has given a tremendous impetus to research in theoretical computer science. In spite of having well defined constructs and globally accepted notations for logic and First-order theorem provers, theorem proving is still a semi-decidable problem having exponential time complexity. On the other hand swarm intelligence is a swiftly growing research area for solving optimization problems. This paper presents a novel approach for automated theorem proving using meta-heuristics. In the present research we have tried to combine these two entirely different zones of computer science, i.e. meta-heuristics and concrete logic via modeling theorem provers as an optimization problem in a sound practical manner. Also we have experimentally shown how to automate first order reasoning using Artificial Bee Colony algorithm on a sample problem expressed in First-order predicate calculus.

Trust analysis of execution platform for self protected mobile code

Malicious host problem is still a challenging phenomenon in agent computing environment. In mobile agent computing, agent platform has full control over mobile agent to execute it. A host can analyze the code during stay of mobile agent on that host. A host can modify the mobile code for his benefits. A host can analyze and modify the data which is previously collected during agent itinerary. Hence to save the code from malicious host we need to identify it. Therefore we calculate the risk associated with that code executing on a mobile host using fuzzy logic. When a host performs an attack over the mobile agent it will take more execution time thus some risk is associated with it. If the calculated risk is greater than a user specified maximum value then the agent code is discarded and the host is identified to be malicious. In this paper, we proposed a fuzzy based risk evaluation model integrated with proposed self-protected security protocol to secure mobile code from insecure execution.

Artificial Bee Colony as a Frontier in Evolutionary Optimization: A Survey

Artificial Bee Colony (ABC) algorithm is now a long-familiar example of Swarm Intelligence. It has been consistently drawing the attention of research scholars since last decade. The adept performance of ABC algorithm has already been proved in various researches. Hence this algorithm has been used in wide variety of applications, spanning almost all aspects of engineering optimization. This manuscript details out some of the application areas of ABC algorithm in a concise way and it aims to provide a bird eye view of various application areas for the beginner researchers.

GA-EAM Based Hybrid Algorithm

The methods of searching optimal solutions are distinct in different evolutionary algorithms. Some of them do search by exploiting whereas others do by exploring the whole search space. For example Genetic Algorithm (GA) is good in exploitation whereas the Environmental Adaption Method (EAM) performs well in exploring the whole search space. Individually these algorithms have some limitations. In this paper a new hybrid algorithm has been proposed, which is created by combining the techniques of GA and EAM. The proposed algorithm attempts to remove the limitations of both GA and EAM and it is compared with some state-of-the-art algorithms like Particle Swarm Optimization-Time Variant Acceleration Coefficient (PSO-TVAC), Self-Adaptive Differential Evolution (SADE) and EAM on six benchmark functions with experimental results. It is found that the proposed hybrid algorithm gives better results than the existing algorithms.

Co-variance guided Artificial Bee Colony

Abstract Artificial Bee Colony (ABC) is one of the proficient and largely used optimization technique, inspired by the food search behavior of honey bees. This article presents a novel Co-variance guided Artificial Bee Colony (CABC) algorithm which is a unification of ABC and statistical co-variance. The co-variance matrix of data sets is a good approximation of the Hessian and acts as a source of gradient information in meta-heuristic optimization. In this article we have demonstrated how to use covariance information about the “population of candidate solutions” for speeding up the underlying ABC optimization technique. The COCO (COmparing Continuous Optimisers), Black Box Optimization Bench-marking 2015 (BBOB) test bed with 24 benchmarks has been used to evaluate the performance of CABC algorithm. It is observed from experimental as well as statistical results that CABC has a consistent better performance when compared with other state-of-the-art evolutionary algorithms.

A Novel Ensemble Based Identification of Phishing E-Mails

Due to high usage of emails, phishing email detection has been an area of interest for a lot of research scientists. Many techniques have been introduced in the past for fraud email detection at the server end. In this paper, we have proposed a novel ensemble classifier for detecting phishing emails by taking inputs from five machine learning algorithms to get maximum accuracy. The performance of the proposed model has been measured on the basis of an open dataset of emails from SpamAssassin public corpus.

Constrained Problem Optimization using Altered Artificial Bee Colony Algorithm

Constraint optimization is one of the major fields of decision science where variables and solutions are often constrained or restricted to a certain feasible space only. Moreover, it is always not possible to replace constraints by penalization function only. That is why it is hard to ascertain the exact solution of these types of problems. The Artificial Bee Colony (ABC) algorithm is one of the most prevalent Swarm Intelligence based meta-heuristic algorithm. It is established on the basis of food search behavior of swarms. To tackle constraint optimization problems arising mostly from real world, we have purported a novel Altered-Artificial Bee Colony algorithm (A-ABC). The performance analysis of A- ABC algorithm has been done by testing it on ten classical constraint optimization benchmark functions. The simulation results, when compared with other traditional meta-heuristic approaches, are found best on most of the problems and at-least comparable on remaining one.