Avishek Ghosh

Multi-robot cooperative box-pushing problem using multi-objective Particle Swarm Optimization technique

The present work provides a new approach to solve the well-known multi-robot co-operative box pushing problem as a multi objective optimization problem using modified Multi-objective Particle Swarm Optimization. The method proposed here allows both turning and translation of the box, during shift to a desired goal position. We have employed local planning scheme to determine the magnitude of the forces applied by the two mobile robots perpendicularly at specific locations on the box to align and translate it in each distinct step of motion of the box, for minimization of both time and energy. Finally the results are compared with the results obtained by solving the same problem using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed scheme is found to give better results compared to NSGA-II.

A novel genetic algorithm to solve travelling salesman problem and blocking flow shop scheduling problem

This paper presents a novel genetic algorithm GA to address a wide range of sequencing and scheduling optimisation problems. As for the performance analysis we have applied our algorithm on travelling salesman problems TSPs and flow shop scheduling problems FSPs. Our main objective is to develop an adaptive method which is equally applicable to all kind of optimisation problems with discrete decision variables. Keeping that view in mind we present some new crossover and mutation operators to tackle TSP as well as FSP with GA with path representation. We have also used a new ring parent topology to generate offspring. A new selection procedure, trio-selection procedure is applied to avoid undesirable clustering before reaching the optima. Faster convergence is assured by applying some modified mutation schemes in finding optimal order of cities in TSP and minimising the maximum completion time i.e., makespan for blocking flow shop scheduling problems. This novel GA variant ensures much better results compared to other heuristics, which is apparent from the experimental results and comparisons with other existing algorithms provided below.

Impromptu Deployment of Wireless Relay Networks: Experiences Along a Forest Trail

We are motivated by the problem of impromptu or as-you-go deployment of wireless sensor networks. As an application example, a person, starting from a sink node, walks along a forest trail, makes link quality measurements (with the previously placed nodes) at equally spaced locations, and deploys relays at some of these locations, so as to connect a sensor placed at some a priori unknown point on the trail with the sink node. In this paper, we report our experimental experiences with some as-you-go deployment algorithms. Two algorithms are based on Markov decision process (MDP) formulations, these require a radio propagation model. We also study purely measurement based strategies: one heuristic that is motivated by our MDP formulations, one asymptotically optimal learning algorithm, and one inspired by a popular heuristic. We extract a statistical model of the propagation along a forest trail from raw measurement data, implement the algorithms experimentally in the forest, and compare them. The results provide useful insights regarding the choice of the deployment algorithm and its parameters, and also demonstrate the necessity of a proper theoretical formulation.

Linear phase low pass FIR filter design using Genetic Particle Swarm Optimization with dynamically varying neighbourhood technique

The paper presents an elegant approach for designing linear phase low pass digital FIR filter using swarm and evolutionary algorithms. Classical gradient based approaches are not efficient enough for accurate design and thus evolutionary approach is considered to be a better choice. In this paper a hybrid of Genetic Algorithm and Particle Swarm Optimization algorithm with varying neighbourhood topology, namely Genetic Lbest Particle Swarm Optimization with Dynamically Varying Neighbourhood (GLPSO DVN) is used to find the filter coefficients. In this work two objective functions (error metrics) are minimized. The first one is based on stop and pass band ripple and the second one studies the mean square error between the ideal and actual designed filter. The hybrid algorithm is found to produce fitter candidate solution than the classical Lbest PSO. The results are compared with the results obtained by solving the same problem using Lbest PSO (LPSO). It is also observed that GLPSO DVN gives better results than LPSO and as well LPSO DVN.

As-you-go deployment of a 2-connected wireless relay network for sensor-sink interconnection

A person walks along a line (which could be an idealisation of a forest trail, for example), placing relays as he walks, in order to create a multihop network for connecting a sensor at a point along the line to a sink at the start of the line. The potential placement points are equally spaced along the line, and at each such location the decision to place or not to place a relay is based on link quality measurements to the previously placed relays. The location of the sensor is unknown apriori, and is discovered as the deployment agent walks. In this paper, we extend our earlier work on this class of problems to include the objective of achieving a 2-connected multihop network. We propose a network cost objective that is additive over the deployed relays, and accounts for possible alternate routing over the multiple available paths. As in our earlier work, the problem is formulated as a Markov decision process. Placement algorithms are obtained for two source location models, which yield a discounted cost MDP and an average cost MDP. In each case we obtain structural results for an optimal policy, and perform a numerical study that provides insights into the advantages and disadvantages of multi-connectivity. We validate the results obtained from numerical study experimentally in a forest-like environment.

Notice of Violation of IEEE Publication Principles Design and simulation of MEMS based piezoresistive pressure sensor for enhanced sensitivity

The application of MEMS to the measurement of pressure is a mature application of micromachined silicon mechanical sensors. The present paper describes the design and simulation of surface micromachined piezo resistive type pressure sensor for enhanced sensitivity. The principle of the sensing mechanism is based on the deflection of sensing silicon nitride diaphragm. In order to achieve better sensor performance, a FEM analysis using mechanical analysis module of Intellisuite software is performed to evaluate the system output sensitivity of the pressure sensor. The deflections of the diaphragms (for square as well as circular) have been studied for different applied pressure. From this, the operating range as well as the sensitivity of the sensors can be easily determined. A detailed analysis of the deflection with different applied pressure is presented graphically. The output voltage and material elasticity of diaphragm are affected by temperature variations. So, use of heat sink is required for temperature compensation purpose. This simulation results depict that proper selection of the diaphragm geometry and piezoresistor location can enhance the sensor sensitivity with lower power consumption.

An evolutionary approach to drug-design using Quantam binary Particle Swarm optimization algorithm

The present work provides a new approach to evolve ligand structures which represent possible drug to be docked to the active site of the target protein. The structure is represented as a tree where each non-empty node represents a functional group. It is assumed that the active site configuration of the target protein is known with position of the essential residues. In this paper the interaction energy of the ligands with the protein target is minimized. Moreover, the size of the tree is difficult to obtain and it will be different for different active sites. To overcome the difficulty, a variable tree size configuration is used for designing ligands. The optimization is done using a quantum discrete PSO. The result using fixed length and variable length configuration are compared.

Measurement Based As-You-Go Deployment of Two-Connected Wireless Relay Networks

Motivated by the need for impromptu or as-you-go deployment of wireless sensor networks in some situations, we study the problem of optimal sequential deployment of wireless sensors and relays along a line (e.g., a forest trail) of unknown length. Starting from the sink node (e.g., a base station), a ”deployment agent„ walks along the line, stops at equally spaced points (”potential„ relay locations), placing relays at some of these points, until he reaches a location at which the source node (i.e., the sensor) needs to be placed, the objective being to create a multihop wireless relay network between the source and the sink. The deployment agent decides whether to place a relay or not at each of the potential locations, depending upon the link quality measurements to the previously placed relays. In this article, we seek to design efficient deployment algorithms for this class of problems, to achieve the objective of 2-connectivity in the deployed network. We ensure multi-connectivity by allowing each node to communicate with more than one neighbouring node. By proposing a network cost objective that is additive over the deployed relays, we formulate the relay placement problem as a Markov decision process. We provide structural results for the optimal policy and evaluate the performance of the optimal policy via numerical exploration. Computation of such an optimal deployment policy requires a statistical model for radio propagation; we extract this model from the raw data collected via measurements in a forestlike environment. To validate the results obtained from the numerical study, we provide an experimental study of algorithms for 2-connected network deployment.