scholarly journals A Research Optimization of CMOS Analog Circuits using Modified Particle Swarm Algorithm

2019 ◽  
Vol 8 (6S4) ◽  
pp. 739-744
Keyword(s):  
Metal Oxide ◽  
Analog Circuits ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Particle Swarm Algorithm ◽  
Metaheuristic Search ◽  
Cmos Analog Circuits ◽  
Swarm Algorithm

In this paper for proficiently dealing with three of the most generally utilized fundamental circuits, for example complementaryl metal-oxide semiconductor (CMOS) operational enhancer circuits A Metaheuristic search Algorithm called Modified Particle swarm Algorithm (MPSO) is appeared as a mechanical get-together. The MPSO estimation begins the framework by making some principal enthusiastic happy plans and using condition based for precariousness of these methodologies towards and outwards the best methodology. Promptly, the transistor estimations of the above-said circuits are refreshed utilizing MPSO to improve the structure focal motivations behind the circuit by rapidly lessening the district required by the transistors in a circuit utilizing MATLAB. By then the parameters are gotten are approved utilizing CADENCE. Here three operational intensifiers are anticipated model Two-plan operational intensifier, Folded course operational speaker and Telescopic operational enhancer. Ideally organizing clear circuits, the extension results and mix plots exhibit the consistency of adjusted molecule swarm improvement estimation over obtained figuring and molecule swarm algorithm

scholarly journals Design and Characteristics of CMOS Inverter based on Multisim and Cadence

2021 ◽  
Vol 2108 (1) ◽  
pp. 012034
Author(s):  
Haoran Xu ◽  
Jianghua Ding ◽  
Jian Dang
Keyword(s):  
Metal Oxide ◽  
Analog Circuits ◽  
Integrated Circuit ◽  
Complementary Metal Oxide Semiconductor ◽  
Cmos Technology ◽  
Data Converters ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Cmos Inverter ◽  
And Performance

Abstract Known as complementary symmetrical metal oxide semiconductor (cos-mos), complementary metal oxide semiconductor is a metal oxide semiconductor field effect transistor (MOSFET) manufacturing process, which uses complementary and symmetrical pairs of p-type and n-type MOSFETs to realize logic functions. CMOS technology is used to build integrated circuit (IC) chips, including microprocessors, microcontrollers, memory chips (including CMOS BIOS) and other digital logic circuits. CMOS technology is also used in analog circuits, such as image sensors (CMOS sensors), data converters, RF circuits (RF CMOS), and highly integrated transceivers for various types of communications. Based on multisim 14.0 and cadence, the characteristics and performance of CMOS inverter are studied by simulation.

Software test data generation technology based on polymorphic particle swarm evolutionary algorithm

2021 ◽  
pp. 1-13
Author(s):  
Wenning Zhang ◽  
Qinglei Zhou
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Test Case Generation ◽  
Particle Swarm Algorithm ◽  
Test Case ◽  
Test Cases ◽  
Data Generation ◽  
Learning Factor ◽  
Swarm Algorithm

Combinatorial testing is a statute-based software testing method that aims to select a small number of valid test cases from a large combinatorial space of software under test to generate a set of test cases with high coverage and strong error debunking ability. However, combinatorial test case generation is an NP-hard problem that requires solving the combinatorial problem in polynomial time, so a meta-heuristic search algorithm is needed to solve the problem. Compared with other meta-heuristic search algorithms, the particle swarm algorithm is more competitive in terms of coverage table generation scale and execution time. In this paper, we systematically review and summarize the existing research results on generating combinatorial test case sets using particle swarm algorithm, and propose a combinatorial test case generation method that can handle arbitrary coverage strengths by combining the improved one-test-at-a-time strategy and the adaptive particle swarm algorithm for the variable strength combinatorial test problem and the parameter selection problem of the particle swarm algorithm. To address the parameter configuration problem of the particle swarm algorithm, the four parameters of inertia weight, learning factor, population size and iteration number are reasonably set, which makes the particle swarm algorithm more suitable for the generation of coverage tables. For the inertia weights.

A Novel Particle Swarm Algorithm for Online Trading Customer Classification

2014 ◽  
Vol 644-650 ◽  
pp. 2181-2184
Author(s):  
Chen Chen
Keyword(s):  
Search Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Computation Method ◽  
Original Algorithm ◽  
Online Trading ◽  
Customer Classification ◽  
Swarm Algorithm ◽  
Improved Algorithm

Particle swarm algorithm is an efficient evolutionary computation method and wildly used in various disciplines. But as a random global search algorithm, particle swarm algorithm easily falls into the local optimal solution for its rapid propagation in populations and in order to overcome these shortcomings, a novel particle swarm algorithm is presented and used in classifying online trading customers. The corresponding improvements include improving the speed update formula of particles and improving the balance between the development and detection capability of original algorithm and redesigning the calculation flow of the improved algorithm. Finally after designing 21 customer classification indicators, the improved algorithm is realized for customer classification of a certain E-commerce enterprise and experimental results show that the algorithm can improve classification accuracy and decreases the square errors.

Binary Decision Tree Construction using the Hybrid Swarm Intelligence

2021 ◽  
pp. 52-65
Author(s):  
B.K. Lebedev ◽  
O.B. Lebedev ◽  
A.A. Zhiglaty
Keyword(s):  
Genetic Algorithm ◽  
Decision Tree ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Classification Model ◽  
Particle Swarm Algorithm ◽  
Genetic Search ◽  
Swarm Algorithm ◽  
Parameter Values

Solving the problem of a classification model construction is presented in the form of a sequence of considered attributes and values thereof included in the Mk route from the root to the dangling vertex. Decision tree developed interpretation is presented as a pair of chromosomes (Sk, Wk). The Sk chromosome list of genes corresponds to the list of all attributes included in the Mk route in the decision tree. The Wk chromosome gene values correspond to the attribute values included in the Mk route. Unification of data structures, search space and modernization of integrable algorithms was carried out for hybridization. Hybrid algorithm operators are using the integer parameters and synthesize new integer parameter values. Method was developed to account for simultaneous attraction of the αi particle to three xi (t), x*i (t), x*(t) attractors dislocating from the xi (t) position to the xi (t + 1) position. Modified hybrid metaheuristic of the search algorithm is proposed for constructing a classification model using recombination of swarm and genetic search algorithms. The first approach uses genetic algorithm initially and then the particle swarm algorithm. The second approach uses the high-level nesting hybridization method based on combination of genetic algorithm and particle swarm algorithm. The proposed approach to constructing a modified paradigm uses chromosomes with integer parameter values in the indicated hybrid algorithm and operators, which assist chromosomes to evolve according to the rules of particle swarm and genetic search

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