Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures

In this paper, we present a generic method/model for multi-objective design optimization of laminated composite components, based on Vector Evaluated Artificial Bee Colony (VEABC) algorithm. VEABC is a parallel vector evaluated type, swarm intelligence multi-objective variant of the Artificial Bee Colony algorithm (ABC). In the current work a modified version of VEABC algorithm for discrete variables has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria: failure mechanism based failure criteria, maximum stress failure criteria and the tsai-wu failure criteria. The optimization method is validated for a number of different loading configurations-uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA). The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations. (C) 2009 Elsevier B.V. All rights reserved.

Influence of cross-correlation between dipolar and chemical shift anisotropy relaxation mechanisms upon the transverse relaxation rates of 15N in macromolecules

Heteronuclear multiple-quantum coherence relaxation rate are calculated for the individual transitions of the S spin in an AIS nuclear spin system assuming that the heteronucleus (S spin) has relaxation contributions from both intramolecular dipole-dipole and chemical shift anisotropy relaxation. The individual multiplet components of the heteronuclear zero- and double-quantum coherences are shown to have different transverse relaxation rates. The cross-correlation between the two relaxation mechanisms is shown to be the dominant cause of the calculated differential line broadening. Experimental data are presented using as an example a uniformly 15N labelled sample of human epidermal growth factor.

Evidence for follicle-stimulating hormone mediation in the hemiorchidectomy-induced compensatory increase in the function of the remaining testis of the adult male bonnet monkey (Macaca radiata)

Hemiorchidectomy (HO) in the adult male bonnet monkey results in a selective increase in circulating concentrations of FSH and testosterone, and this is accompanied by compensatory increase in sperm production by the remaining testis. We investigated the possible role of increased FSH concentration that occurs after HO in the compensatory increase in the activity of the remaining testis. Of eight adult male bonnet monkeys that underwent HO, four received i.v. injections every other day for 30 days of a well-characterized ovine FSH antiserum (a/s) that cross-reacts with monkey FSH. The remaining four males received normal monkey serum (NMS) as control treatment in a protocol similar to that employed for ais-treated males. Blood samples were collected between 2100 and 2200 h before and 1/2, 1, 3, 5, 7, 14, 22, and 29 days after HO. Testicular weight, number of 3 beta-hydroxy steroid dehydrogenase-positive (3 beta-HSD+) cells, and DNA flow cytometric analysis of germ cell populations were obtained for testes collected before and at the termination of NMS or ais treatment. In NMS-treated males, circulating serum FSH concentrations progressively increased to reach a maximal level by Day 7 after HO (1.95 +/- 0.3 vs. 5.6 +/- 0.7 ng/ml on Days -1 and 7, respectively). Within 30 min of ais injection, FSH antibodies were detected in circulation, and the antibody level was maintained at a constant level between Day 7 and end of treatment (exhibiting 50-60% binding to I-125-hFSH). Although circulating mean nocturnal serum testosterone concentration showed an initial decrease, it rose gradually to pre-HO concentrations by Day 7 in NMS-treated males. In contrast, nocturnal mat serum testosterone concentrations in a/s-treated males remained lower than in NMS-treated controls (p < 0.05) up to Day 22 and thereafter only marginally increased. Testicular weights increased (p < 0.05) over the pre-HO weight in NMS- but not in ais-treated males. After HO, the number of 3 beta-HSD+ cells (Leydig cells) was markedly increased but was significantly (p < 0.05) higher in NMS-treated males compared to a/s-treated males. A significant (p < 0.05) reduction in the primary spermatocyte population of germ cells was observed in ais-treated compared to NMS-treated males. These results suggest that the increased FSH occurring after HO could be intimately involved in increasing the compensatory functional activity of the remaining testis in the male bonnet monkey.

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.

An Optimal Fuzzy Logic Controller tuned with Artificial Immune System

In this paper, a method for the tuning the membership functions of a Mamdani type Fuzzy Logic Controller (FLC) using the Clonal Selection Algorithm(CSA) a model of the Artificial Immune System(AIS) paradigm is examined. FLC's are designed for two problems, firstly the linear cart centering problem and secondly the highly nonlinear inverted pendulum problem. The FLC tuned by AIS is compared with FLC tuned by GA. In order to check the robustness of the designed PLC's white noise was added to the system, further, the masses of the cart and the length and mass of the pendulum are changed. The PLC's were also tested in the presence of faulty rules. Finally, Kruskal Wallis test was performed to compare the performance of the GA and AIS. An insight into the algorithms are also given by studying the effect of the important parameters of GA and AIS.

Cooperative Communication of UAV to Perform Multi-task Using Nature Inspired Techniques

In this paper, the approach for assigning cooperative communication of Uninhabited Aerial Vehicles (UAV) to perform multiple tasks on multiple targets is posed as a combinatorial optimization problem. The multiple task such as classification, attack and verification of target using UAV is employed using nature inspired techniques such as Artificial Immune System (AIS), Particle Swarm Optimization (PSO) and Virtual Bee Algorithm (VBA). The nature inspired techniques have an advantage over classical combinatorial optimization methods like prohibitive computational complexity to solve this NP-hard problem. Using the algorithms we find the best sequence in which to attack and destroy the targets while minimizing the total distance traveled or the maximum distance traveled by an UAV. The performance analysis of the UAV to classify, attack and verify the target is evaluated using AIS, PSO and VBA.

Empirical Analysis of Sampling Based Estimators for Evaluating RBMs

The Restricted Boltzmann Machines (RBM) can be used either as classifiers or as generative models. The quality of the generative RBM is measured through the average log-likelihood on test data. Due to the high computational complexity of evaluating the partition function, exact calculation of test log-likelihood is very difficult. In recent years some estimation methods are suggested for approximate computation of test log-likelihood. In this paper we present an empirical comparison of the main estimation methods, namely, the AIS algorithm for estimating the partition function, the CSL method for directly estimating the log-likelihood, and the RAISE algorithm that combines these two ideas.