There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Tables of integrals

Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Recent advances in differential evolution – An updated survey

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

/pdf/a-comprehensive-survey-on-particle-swarm-optimization-1d380v5xx1.pdf

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn from the northern USA and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called monarch butterfly optimization (MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals are located in two distinct lands, viz. southern Canada and the northern USA (Land 1) and Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways. Firstly, the offsprings are generated (position updating) by migration operator, which can be adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by means of butterfly adjusting operator. In order to keep the population unchanged and minimize fitness evaluations, the sum of the newly generated butterflies in these two ways remains equal to the original population. In order to demonstrate the superior performance of the MBO algorithm, a comparative study with five other metaheuristic algorithms through thirty-eight benchmark problems is carried out. The results clearly exhibit the capability of the MBO method toward finding the enhanced function values on most of the benchmark problems with respect to the other five algorithms. Note that the source codes of the proposed MBO algorithm are publicly available at GitHub (
 https://github.com/ggw0122/Monarch-Butterfly-Optimization
 , C++/MATLAB) and MATLAB Central (
 http://www.mathworks.com/matlabcentral/fileexchange/50828-monarch-butterfly-optimization
 , MATLAB).

Monarch butterfly optimization

In this article, the performance of n-type junctionless (JL) double-gate (DG) MOSFET-based biosensors with and without gate stack (GS) has been studied. Here, the dielectric modulation (DM) method is applied to detect biomolecules in the cavity. The sensitivity of n-type JL-DM-DG-MOSFET and n-type JL-DM-GSDG-MOSFET-based biosensors have also been evaluated. The sensitivity (ΔVth) improved in JL-DM-GSDG MOSFET/JL-DM-DG-MOSFET-based biosensors for neutral/charged biomolecules is 116.66%/66.66% and 1165.78%/978.94%, respectively, compared with the previously reported results. The electrical detection of biomolecules is validated using the ATLAS device simulator. The noise and analog/RF parameters are compared between both biosensors. A lower threshold voltage is observed in the GSDG-MOSFET-based biosensor. The Ion/Ioff ratio is higher for DG-MOSFET-based biosensors. The proposed GSDG-MOSFET-based biosensor demonstrates higher sensitivity than the DG-MOSFET-based biosensor. The GSDG-MOSFET-based biosensor is suitable for low-power, high-speed, and high sensitivity applications.

/pdf/a-novel-dielectric-modulated-gate-stack-double-gate-metal-dynfgh3a.pdf

A Novel Dielectric Modulated Gate-Stack Double-Gate Metal-Oxide-Semiconductor Field-Effect Transistor-Based Sensor for Detecting Biomolecules

This paper presents in front of us a novel approach for the optimal design of a Low Noise Amplifier (LNA) with inductive source degeneration circuit using a recently proposed evolutionary optimization technique called PSO with Aging Leader and Challenger (ALC-PSO). The proposed ALC-PSO based approach has succeeded in dealing with the disadvantages faced by PSO algorithm and is employed in this paper for the optimal design of LNA circuit. The MOSFET widths and component’s values are optimized by using ALC-PSO algorithm in order to maximize the gain, minimize the Noise Figure (NF) and to optimize the overall performance of the LNA circuit. The simulation results obtained for the designed LNA circuit confirm the effectiveness of the ALC-PSO based approach over PSO in terms of solution quality, design specifications, and design objectives. The optimally implemented LNA circuit in 0.35 µm CMOS technology yields the gain of 18.64 dB, noise figure of 1.779 dB and power dissipation of 10.60 mW.

Optimal Design of 2.4 GHz CMOS LNA Using PSO with Aging Leader and Challenger

This paper addresses one of the aspects of the high frequency effects, namely the skin effect. The basic problem with skin effect is that it attenuates the high frequency components of a signal more than that of the low frequency components. Due to the increase in operating frequency and die sizes, RC models are becoming insufficient for analysis of global VLSI interconnects. Accurate noise modelling for RLC lines is thus critical for timing and signal integrity analysis. Skin effect basically affects the resistance and also the inductance, which in turn affects the system integrity in particular and its response as a whole. The current distribution inside the conductor changes as frequency increases. These changes produced in the conductors are known as skin and proximity effect. Till now the skin effect has been neglected for the modelling the on-chip interconnects. But with the increase in frequency to the GHz range, the skin effect has become prominent in performance parameter modelling. In this paper, firstly a crosstalk noise formula for on chip VLSI interconnects has been proposed without considering the skin effect. The voltage response at the output node is analytically derived and then the skin effect on the line resistance is analysed. From that an efficient and novel model is derived to estimate the crosstalk in the on-chip interconnects. On-chip inductive effects are becoming predominant in deep submicron interconnects due to increasing clock speeds; circuit complexity and decreasing interconnect lengths. Inductance causes noise in the signal waveforms, which can adversely affect the performance of the circuit and signal integrity. In this paper, we have also proposed a novel analytical model to find the impact of the skin effect on the noise in RLC interconnect without considering the skin effect on inductance because the value of the resistance increases dramatically compared to that of the value of inductance. In this work, the resistance variation due to the skin effect is considered in two wire transmission line model. The correlation between the skin effect and the noise induced is also discussed. The simulation results justify the efficacy of the proposed crosstalk noise model in the presence of skin effect. Keywords: On-Chip Interconnect, Skin- Effect, Crosstalk, RLC Segments, Coupling, VLSI

Modelling of Skin Effect in On-Chip VLSI RLC Global Interconnect

Performance Assessment of Graded Channel Gate-Stack based Double Gate MOSFET for Bio-sensing Applications

The Standardized Precipitation Index (SPI) is used throughout the world as a meteorological drought index to identify the duration and/or severity of drought. Early forecasting of drought is a critical issue to mitigate the adverse effects of drought of varying intensities. To address this issue, linear stochastic models, such as ARIMA and SRIMA have been used in this study. We studied ARIMA and SARIMA models to identify the most appropriate model to describe the SPI series at 3, 6, 9, 12 and 24 month time scale for the Ballary region in Southern India. Temporal characteristics of droughts based on SPI as an indicator of drought severity indicated that the region has been affected by a prolonged drought during the study period (1968–2012). Our study followed ARIMA calibration approach using time series data of SPI series for drought forecasting. The best model among different data sets has been identified using minimum Akaike Information Criteria (AIC), Schwarz-Bayesian Information Criteria (SBC) criteria along with the independency and normality criteria of the residuals. For 3-month SPI series ARIMA was observed to be appropriate while SARIMA model series is promising for the remaining SPI series. The stochastic models developed to predict drought were observed to give reasonably good results with 3 month lead time. Since drought prediction plays an important role in conservation of water resources, water storage management and mitigating drought severity, stochastic models has been observed to be the best and is recommended for drought forecasting in this region of India.

http://krishi.icar.gov.in/jspui/bitstream/123456789/3203/1/2016_NM%20Alam_JISAS.pdf

Durbadal Mandal

Papers

A Novel Dielectric Modulated Gate-Stack Double-Gate Metal-Oxide-Semiconductor Field-Effect Transistor-Based Sensor for Detecting Biomolecules

Optimal Design of 2.4 GHz CMOS LNA Using PSO with Aging Leader and Challenger

Modelling of Skin Effect in On-Chip VLSI RLC Global Interconnect

Performance Assessment of Graded Channel Gate-Stack based Double Gate MOSFET for Bio-sensing Applications

Forecasting meteorological drought for a typical drought affected area in India using stochastic models.