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Sabat S.L.,University of Hyderabad | Udgata S.K.,University of Hyderabad | Abraham A.,Network Intelligence
Engineering Applications of Artificial Intelligence | Year: 2010

This paper presents an application of swarm intelligence technique namely artificial bee colony (ABC) to extract the small signal equivalent circuit model parameters of GaAs metal extended semiconductor field effect transistor (MESFET) device and compares its performance with particle swarm optimization (PSO) algorithm. Parameter extraction in MESFET process involves minimizing the error, which is measured as the difference between modeled and measured S parameter over a broad frequency range. This error surface is viewed as a multi-modal error surface and robust optimization algorithms are required to solve this kind of problem. This paper proposes an ABC algorithm that simulates the foraging behavior of honey bee swarm for model parameter extraction. The performance comparison of both the algorithms (ABC and PSO) are compared with respect to computational time and the quality of solutions (QoS). The simulation results illustrate that these techniques extract accurately the 16-element small signal model parameters of MESFET. The efficiency of this approach is demonstrated by a good fit between the measured and modeled S-parameter data over a frequency range of 0.5- 25 GHz.© 2010 Elsevier Ltd. All rights reserved.

Das S.,Jadavpur University | Mukhopadhyay A.,Jadavpur University | Roy A.,Jadavpur University | Abraham A.,Network Intelligence | Panigrahi B.K.,Indian Institute of Technology Delhi
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics | Year: 2011

The theoretical analysis of evolutionary algorithms is believed to be very important for understanding their internal search mechanism and thus to develop more efficient algorithms. This paper presents a simple mathematical analysis of the explorative search behavior of a recently developed metaheuristic algorithm called harmony search (HS). HS is a derivative-free real parameter optimization algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. This paper analyzes the evolution of the population-variance over successive generations in HS and thereby draws some important conclusions regarding the explorative power of HS. A simple but very useful modification to the classical HS has been proposed in light of the mathematical analysis undertaken here. A comparison with the most recently published variants of HS and four other state-of-the-art optimization algorithms over 15 unconstrained and five constrained benchmark functions reflects the efficiency of the modified HS in terms of final accuracy, convergence speed, and robustness. © 2010 IEEE.

Xhafa F.,University of London | Abraham A.,Network Intelligence
Future Generation Computer Systems | Year: 2010

In this paper we survey computational models for Grid scheduling problems and their resolution using heuristic and meta-heuristic approaches. Scheduling problems are at the heart of any Grid-like computational system. Different types of scheduling based on different criteria, such as static versus dynamic environment, multi-objectivity, adaptivity, etc., are identified. Then, heuristic and meta-heuristic methods for scheduling in Grids are presented. The paper reveals the complexity of the scheduling problem in Computational Grids when compared to scheduling in classical parallel and distributed systems and shows the usefulness of heuristic and meta-heuristic approaches for the design of efficient Grid schedulers. We also discuss on requirements for a modular Grid scheduling and its integration with Grid architecture. © 2009 Elsevier B.V. All rights reserved.

Chakraborty P.,Jadavpur University | Das S.,Jadavpur University | Roy G.G.,Jadavpur University | Abraham A.,Network Intelligence
Information Sciences | Year: 2011

Several variants of the particle swarm optimization (PSO) algorithm have been proposed in recent past to tackle the multi-objective optimization (MO) problems based on the concept of Pareto optimality. Although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of PSO as a single-objective optimizer, till date, to the best of our knowledge, no such analysis exists for the multi-objective PSO (MOPSO) algorithms. This paper presents a first, simple analysis of the general Pareto-based MOPSO and finds conditions on its most important control parameters (the inertia factor and acceleration coefficients) that govern the convergence behavior of the algorithm to the optimal Pareto front in the objective function space. Computer simulations over benchmark MO problems have also been provided to substantiate the theoretical derivations. © 2010 Published by Elsevier Inc. All rights reserved.

Chen W.-N.,Network Intelligence | Zhang J.,Sun Yat Sen University
IEEE Transactions on Software Engineering | Year: 2013

Research into developing effective computer aided techniques for planning software projects is important and challenging for software engineering. Different from projects in other fields, software projects are people-intensive activities and their related resources are mainly human resources. Thus, an adequate model for software project planning has to deal with not only the problem of project task scheduling but also the problem of human resource allocation. But as both of these two problems are difficult, existing models either suffer from a very large search space or have to restrict the flexibility of human resource allocation to simplify the model. To develop a flexible and effective model for software project planning, this paper develops a novel approach with an event-based scheduler (EBS) and an ant colony optimization (ACO) algorithm. The proposed approach represents a plan by a task list and a planned employee allocation matrix. In this way, both the issues of task scheduling and employee allocation can be taken into account. In the EBS, the beginning time of the project, the time when resources are released from finished tasks, and the time when employees join or leave the project are regarded as events. The basic idea of the EBS is to adjust the allocation of employees at events and keep the allocation unchanged at nonevents. With this strategy, the proposed method enables the modeling of resource conflict and task preemption and preserves the flexibility in human resource allocation. To solve the planning problem, an ACO algorithm is further designed. Experimental results on 83 instances demonstrate that the proposed method is very promising. © 2013 IEEE.

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