Pickup and delivery problem using metaheuristics techniques

Dial-a-ride problem (DARP) is an optimization problem which deals with the minimization of the cost of the provided service where the customers are provided a door-to-door service based on their requests. This optimization model presented in earlier studies, is considered in this study. Due to the non-linear nature of the objective function the traditional optimization methods are plagued with the problem of converging to a local minima. To overcome this pitfall we use metaheuristics namely Simulated Annealing (SA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Immune System (AIS). From the results obtained, we conclude that Artificial Immune System method effectively tackles this optimization problem by providing us with optimal solutions. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

Swarm intelligence algorithms for integrated optimization of piezoelectric actuator and sensor placement and feedback gains

Swarm intelligence algorithms are applied for optimal control of flexible smart structures bonded with piezoelectric actuators and sensors. The optimal locations of actuators/sensors and feedback gain are obtained by maximizing the energy dissipated by the feedback control system. We provide a mathematical proof that this system is uncontrollable if the actuators and sensors are placed at the nodal points of the mode shapes. The optimal locations of actuators/sensors and feedback gain represent a constrained non-linear optimization problem. This problem is converted to an unconstrained optimization problem by using penalty functions. Two swarm intelligence algorithms, namely, Artificial bee colony (ABC) and glowworm swarm optimization (GSO) algorithms, are considered to obtain the optimal solution. In earlier published research, a cantilever beam with one and two collocated actuator(s)/sensor(s) was considered and the numerical results were obtained by using genetic algorithm and gradient based optimization methods. We consider the same problem and present the results obtained by using the swarm intelligence algorithms ABC and GSO. An extension of this cantilever beam problem with five collocated actuators/sensors is considered and the numerical results obtained by using the ABC and GSO algorithms are presented. The effect of increasing the number of design variables (locations of actuators and sensors and gain) on the optimization process is investigated. It is shown that the ABC and GSO algorithms are robust and are good choices for the optimization of smart structures.

Conservative Failure Criteria for Optimal Design of Composite Structures Including Effect of Shear Loading

A minimum weight design of laminated composite structures is carried out for different loading conditions and failure criteria using genetic algorithm. The phenomenological maximum stress (MS) and Tsai-Wu (TW) criteria and the micro-mechanism-based failure mechanism based (FMB) failure criteria are considered. A new failure envelope called the Most Conservative Failure Envelope (MCFE) is proposed by combining the three failure envelopes based on the lowest absolute values of the strengths predicted. The effect of shear loading on the MCFE is investigated. The interaction between the loading conditions, failure criteria, and strength-based optimal design is brought out.

MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures

This paper presents a decentralized/peer-to-peer architecture-based parallel version of the vector evaluated particle swarm optimization (VEPSO) algorithm for multi-objective design optimization of laminated composite plates using message passing interface (MPI). The design optimization of laminated composite plates being a combinatorially explosive constrained non-linear optimization problem (CNOP), with many design variables and a vast solution space, warrants the use of non-parametric and heuristic optimization algorithms like PSO. Optimization requires minimizing both the weight and cost of these composite plates, simultaneously, which renders the problem multi-objective. Hence VEPSO, a multi-objective variant of the PSO algorithm, is used. Despite the use of such a heuristic, the application problem, being computationally intensive, suffers from long execution times due to sequential computation. Hence, a parallel version of the PSO algorithm for the problem has been developed to run on several nodes of an IBM P720 cluster. The proposed parallel algorithm, using MPI's collective communication directives, establishes a peer-to-peer relationship between the constituent parallel processes, deviating from the more common master-slave approach, in achieving reduction of computation time by factor of up to 10. Finally we show the effectiveness of the proposed parallel algorithm by comparing it with a serial implementation of VEPSO and a parallel implementation of the vector evaluated genetic algorithm (VEGA) for the same design problem. (c) 2012 Elsevier Ltd. All rights reserved.

Prediction of Shallow Landslide prone regions in Undulating Terrains

Genetic Algorithm for Rule-set Prediction (GARP) and Support Vector Machine (SVM) with free and open source software (FOSS) - Open Modeller were used to model the probable landslide occurrence points. Environmental layers such as aspect, digital elevation, flow accumulation, flow direction, slope, land cover, compound topographic index and precipitation have been used in modeling. Simulated output of these techniques is validated with the actual landslide occurrence points, which showed 92% (GARP) and 96% (SVM) accuracy considering precipitation in the wettest month and 91% and 94% accuracy considering precipitation in the wettest quarter of the year.

Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region.

Role of melt convection on optimization of PCM-based heat sink under cyclic heat load

In this article, we study the thermal performance of phase-change material (PCM)-based heat sinks under cyclic heat load and subjected to melt convection. Plate fin type heat sinks made of aluminum and filled with PCM are considered in this study. The heat sink is heated from the bottom. For a prescribed value of heat flux, design of such a heat sink can be optimized with respect to its geometry, with the objective of minimizing the temperature rise during heating and ensuring complete solidification of PCM at the end of the cooling period for a given cycle. For given length and base plate thickness of a heat sink, a genetic algorithm (GA)-based optimization is carried out with respect to geometrical variables such as fin thickness, fin height, and the number of fins. The thermal performance of the heat sink for a given set of parameters is evaluated using an enthalpy-based heat transfer model, which provides the necessary data for the optimization algorithm. The effect of melt convection is studied by taking two cases, one without melt convection (conduction regime) and the other with convection. The results show that melt convection alters the results of geometrical optimization.

CAVVA: Computational Affective Video-in-Video Advertising

Advertising is ubiquitous in the online community and more so in the ever-growing and popular online video delivery websites (e. g., YouTube). Video advertising is becoming increasingly popular on these websites. In addition to the existing pre-roll/post-roll advertising and contextual advertising, this paper proposes an in-stream video advertising strategy-Computational Affective Video-in-Video Advertising (CAVVA). Humans being emotional creatures are driven by emotions as well as rational thought. We believe that emotions play a major role in influencing the buying behavior of users and hence propose a video advertising strategy which takes into account the emotional impact of the videos as well as advertisements. Given a video and a set of advertisements, we identify candidate advertisement insertion points (step 1) and also identify the suitable advertisements (step 2) according to theories from marketing and consumer psychology. We formulate this two part problem as a single optimization function in a non-linear 0-1 integer programming framework and provide a genetic algorithm based solution. We evaluate CAVVA using a subjective user-study and eye-tracking experiment. Through these experiments, we demonstrate that CAVVA achieves a good balance between the following seemingly conflicting goals of (a) minimizing the user disturbance because of advertisement insertion while (b) enhancing the user engagement with the advertising content. We compare our method with existing advertising strategies and show that CAVVA can enhance the user's experience and also help increase the monetization potential of the advertising content.

Quantum simulation using fidelity-profile optimization

Experimental quantum simulation of a Hamiltonian H requires unitary operator decomposition (UOD) of its evolution unitary U = exp(-iHt) in terms of native unitary operators of the experimental system. Here, using a genetic algorithm, we numerically evaluate the most generic UOD (valid over a continuous range of Hamiltonian parameters) of the unitary operator U, termed fidelity-profile optimization. The optimization is obtained by systematically evaluating the functional dependence of experimental unitary operators (such as single-qubit rotations and time-evolution unitaries of the system interactions) to the Hamiltonian (H) parameters. Using this technique, we have solved the experimental unitary decomposition of a controlled-phase gate (for any phase value), the evolution unitary of the Heisenberg XY interaction, and simulation of the Dzyaloshinskii-Moriya (DM) interaction in the presence of the Heisenberg XY interaction. Using these decompositions, we studied the entanglement dynamics of a Bell state in the DM interaction and experimentally verified the entanglement preservation procedure of Hou et al. Ann. Phys. (N.Y.) 327, 292 (2012)] in a nuclear magnetic resonance quantum information processor.

Comparative evaluation of inversion approaches of the radiative transfer model for estimation of crop biophysical parameters

The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for para-meters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.

Search For Critical Slip Surface In Slope Stability Analysis By Spline-Based Ga Method

The stability of a soil slope is usually analyzed by limit equilibrium methods, in which the identification of the critical slip surface is of principal importance. In this study the spline curve in conjunction with a genetic algorithm is used to search the critical slip surface, and Spencer's method is employed to calculate the factor of safety. Three examples are presented to illustrate the reliability and efficiency of the method. Slip surfaces defined by a series of straight lines are compared with those defined by spline curves, and the results indicate that use of spline curves renders better results for a given number of slip surface nodal points comparing with the approximation using straight line segments.

利用遗传算法进行水平井水平段长度优化设计

在油藏开发过程中，随着水平井水平段长度的增加，水平井段与油藏的接触面也随之增加，同时井筒内流体的流动阻力也增加，从而导致水平井的产液量减少，即水平井的最终产量是2个相互制约的因素共同作用的结果。利用遗传算法全局寻优的特点，结合一种计算无限导流和有限导流情况下水平井产量的井筒／油藏耦合模型，提出了一种用于水平井水平段长度的优化设计方法。该方法可以在不同的油藏地质模型中使用。

Analytical Methods of Residual Strength in Injecting Water Pipeline

In this paper, common criterions about residual strength evaluation at home and abroad are generalized and seven methods are acquired, namely ASME-B31G, DM, Wes-2805-97, CVDA-84, Burdekin, Irwin and J integral methods. BP neural network are Combined with Genetic Algorithm (GA) named by modified BP-GA methods to successfully predict residual strength and critical pressure of injecting water, corrosion pipelines. Examples are shown that calculation results of every kind of method have great difference and calculating values of Wes-2805-97 criterion, ASME-B31G criterion, CVDA-84 criterion and Irwin fracture mechanics model are conservative and higher than, those of J integral methods while calculating values of Burdiken model and DM fracture mechanics model are dangerous and less than those of J integral methods and calculating values of modified BP-GA methods are close and moderate to those of J integral methods. Therefore modified BP-GA methods and J integral methods are considered better methods to calculate residual strength and critical pressure of injecting water corrosion pipelines

Coherent enhancement of broadband frequency up-conversion in BBO crystal by shaping femtosecond laser pulses

An optimal feedback control of broadband frequency up-conversion in BBO crystal is experimentally demonstrated by shaping femto-second laser pulses based on genetic algorithm, and the frequency up-conversion efficiency can be enhanced by similar to 16%. SPIDER results show that the optimal laser pulses have shorter pulse-width with the little negative chirp than the original pulse with the little positive chirp. By modulating the fundamental spectral phase with periodic square distribution on SLM-256, the frequency up-conversion can be effectively controlled by the factor of about 17%. The experimental results indicate that the broadband frequency up-conversion efficiency is related to both of second harmonic generation (SHG) and sum frequency generation (SFG), where the former depends on the fundamental pulse intensity, and the latter depends on not only the fundamental pulse intensity but also the fundamental pulse spectral phase. (c) 2006 Elsevier B.V. All rights reserved.