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 paper a modified version of swarm intelligence technique called Novel Particle Swarm Optimization (NPSO) technique is applied to IIR adaptive filter design problem. The proposed technique NPSO in close similarity with Real coded Genetic Algorithm (RGA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) performs a structured randomized search of unknown parameters within a multidimensional search space by manipulating a swarm of particles to converge on optimal solutions. NPSO velocity equation is modified with good and bad experience components along with judiciously chosen random variables to bind the bird to fly only toward the promising zone of food. The exploration and exploitation of entire search space can be handled efficiently with NPSO along with the benefits of overcoming the premature convergence and stagnation problems. The simulation results justify the optimization superiority of the proposed NPSO over RGA, PSO and DE.

http://ieeexplore.ieee.org/iel7/6507008/6512767/06512801.pdf

IIR filter design and identification using NPSO technique

In this paper, the behaviour of CMOS inverter driving RLC interconnect load is analyzed The analysis is based on the modelling of RLC load, developed for submicron devices Sakurai's alpha-power law is used here for representing the transistor current Accurate and analytical expressions for the output voltage waveform are derived by solving the system of differential equations which describe the behaviour of the circuit The 50% delay for on-chip interconnect is then calculated from the transient response The output response derived from this model is compared with the SPICE simulation results and the results obtained by using the proposed model are in good agreement with SPICE model The maximum error has been found to be 94%

A novel method for delay analysis of CMOS inverter with on-chip RLC interconnect load

In this paper, an optimal design of stable digital high pass infinite impulse response (IIR) filter using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSO-CFIWA) has been presented. The conventional gradient based optimization techniques are not efficient enough for handling digital IIR filter design due to the sub-optimality problem. The proposed optimization technique PSO-CFIWA is a heuristic search algorithm and capable enough to handle non differentiable optimization problem to find optimal solution in multidimensional search space. Performance of the proposed algorithm is compared with well accepted evolutionary algorithms such as particle swarm optimization (PSO) and real coded genetic algorithm (RGA). From the simulation study it is established that the PSO-CFIWA outperforms RGA and PSO, not only in the accuracy of the designed filter but also in the convergence speed and solution quality i.e. the stop band attenuation, transition width, pass band and stop band ripples. Further, the pole-zero analysis justifies the stability of the designed optimized IIR filter.

Digital stable IIR high pass filter optimization using PSO-CFIWA

Crosstalk Aware Bandwidth Modelling for VLSI RC Global Interconnects using 2-π Model

This paper focuses on the analysis of static and dynamic power dissipations and stability analysis of a proposed 10T SRAM cell. In the proposed structure there are two voltage sources, one connected with the Bit line and the other connected with the Bit bar line for reducing the voltage swing during the switching activity. This reduction in voltage swing causes less dynamic power dissipation during switching activity. Two stack transistors are also connected in the pull-down paths which increase the threshold voltages of the transistors and this cause the reduction in sub-threshold leakage current and static power dissipation. Simulation has been done in 45nm CMOS technology with 0.7 volt power supply in Microwind 3.1 software. Simulation results have been compared to those of other existing 10T SRAM cells.

https://ieeexplore.ieee.org/iel7/6829975/6841829/06841868.pdf

Durbadal Mandal

Papers

IIR filter design and identification using NPSO technique

A novel method for delay analysis of CMOS inverter with on-chip RLC interconnect load

Digital stable IIR high pass filter optimization using PSO-CFIWA

Crosstalk Aware Bandwidth Modelling for VLSI RC Global Interconnects using 2-π Model

A novel 10T SRAM cell with low power dissipation in active and sleep mode for write operation