Inclined Planes System Optimization Research Articles

An ultra‐low‐power low‐noise amplifier using a combination of current reuse and self‐forward body bias techniques for biomedical applications

SummaryLow‐power low noise amplifiers (LNAs) are critical block in the radio frequency (RF) front‐end of medical device radiocommunications service (MedRadio) receivers for amplifying weak received signals while introducing minimal noise. Despite the inherently low transconductance of MOS transistors, it is possible to achieve low noise, high gain, and good linearity in low power LNAs. This paper presents a combination of techniques to realize an ultra‐low‐power LNA with a compact size and high performance. The proposed LNA utilizes multiple gm‐boosting and self‐forward body bias techniques, with certain transistors operating in weak inversion. Additionally, the Multi‐Objective Inclined Planes System Optimization algorithm is employed to determine the optimal values of the circuit components, thereby achieving the best compromise among the various performance parameters. The proposed LNA with buffer is designed and simulated using an 180 nm CMOS process over the frequency band of 0.3–1 GHz. Simulated results show that the proposed LNA achieves a gain of 19.6 dB, a noise figure of 5.1 dB, good input matching (S11 < −10 dB), and a third‐order intermodulation intercept point (IIP3) of 3.8 dBm. Additionally, it consumes 320 μW (without buffer) from a 1 V power supply, and the chip area is 235 μm × 372 μm. The simulated results demonstrate that the proposed ultra‐low power LNA is a promising candidate for biomedical applications.

Correction to: Nature-Inspired Metaheuristic Search Algorithms for Optimizing Benchmark Problems: Inclined Planes System Optimization to State-of-the-Art Methods

Correction to: Nature-Inspired Metaheuristic Search Algorithms for Optimizing Benchmark Problems: Inclined Planes System Optimization to State-of-the-Art Methods

Nature-Inspired Metaheuristic Search Algorithms for Optimizing Benchmark Problems: Inclined Planes System Optimization to State-of-the-Art Methods

Nature-Inspired Metaheuristic Search Algorithms for Optimizing Benchmark Problems: Inclined Planes System Optimization to State-of-the-Art Methods

Inclined planes system optimization: Theory, literature review, and state-of-the-art versions for IIR system identification

The Inclined Planes System Optimization (IPO) algorithm is recent algorithm that uses Newton’s second law to perform optimization. After conducting a thorough literature review, this paper proposes an improved version of IPO called IIPO. This improvement is achieved by changing exploratory and exploitative behavior of the standard IPO proportional to the progress of optimization (iteration). The IIPO is employed for optimizing IIR digital filter design, which is a challenging engineering problem. Adaptive IIR modeling as a multimodal optimization problem is designed and developed under system identification structure with an appropriate single-objective function in the frequency domain. Implementations are performed in both modeling cases with same and reduced orders, and under two identification forms with and without environmental additive noise. The results are reported along with various analyzes compared to a wide range of IPO variants. The statistical results on 100 independent trials show a success of more than 90% of cases, the proposed IIPO algorithm substantially outperforms other comparative algorithms in terms of accuracy of estimated coefficients, convergence, fitness, output responses, noise analysis, stability, and reliability.11All source codes are fully and publicly available at https://github.com/ali-ece.

Optimum design of a new ultra-wideband LNA using heuristic multiobjective optimization

This paper presents an optimum design of an ultra-wideband (UWB) 2.5–10.5-GHz low-noise amplifier (LNA) in 180-nm and 65-nm radiofrequency (RF)-complementary metal–oxide–semiconductor (CMOS) technology. A novel input matching network employing resistive–inductive feedback and a noise-canceling technique is proposed to achieve broadband matching as well as a low noise figure (NF). Moreover, a current-reused structure and the inductive peaking technique are applied in the proposed LNA to reduce its power consumption and provide high, flat gain. The proposed UWB-LNA is optimized using heuristic multiobjective optimization based on inclined planes system optimization (IPO) and particle swarm optimization (PSO) as simulation-based evolutionary techniques. The proposed UWB-LNA is designed and simulated using HSPICE and Cadence Spectre RF. The postlayout simulation results show an input return loss (S11) of less than −10 dB, a flat power gain (S21) of 13.2 ± 0.5 and 14 ± 0.5 dB, and an NF below 5 and 2.5 dB over the whole UWB band when implemented in 180-nm and 65-nm CMOS technology, respectively. The UWB-LNA consumes 7.2 and 9.5 mW from a 1.8-V power supply when implemented in 180-nm and 65-nm CMOS technology, respectively.

An improved multi-objective learning automata and its application in VLSI circuit design

In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis.

Automatic and heuristic complete design for ANFIS classifier

ABSTRACTThere is a variety of fuzzy classifiers, one of which is Adaptive Neuro-Fuzzy Inference system (ANFIS) classifier. One of the main challenges in designing such data classifiers is selection of effective and appropriate type and location of membership functions and its training method to reduce the classification error. In this paper, a new technique (based on intelligent methods) is presented and implemented to select and locate the membership functions and simultaneous training using a new method based on Inclined Planes System Optimization (IPO) to minimize errors of an ANFIS classifier for the first time. The presented method is evaluated for classification of data sets with different reference classes and different length feature vectors, which have acceptable complexity. According to the results of the research, the presented method has a higher level of accuracy and efficiency in selecting the type and location of membership functions (based on intelligent methods) and simultaneous training with IPO, compared to other methods, such as particle swarm optimization, genetic algorithm, differential evolution, and ACOR algorithms.

Application of IPO: a heuristic neuro-fuzzy classifier

Heuristic methods are used to design an adaptive-network-based fuzzy inference system (ANFIS) neuro-fuzzy classifier. The reason is that these classifiers include diverse structures, each of which has a considerable effect on their performance. So, the designer of an ANFIS classifier confronts a high-dimensional solution space and heuristic methods are of high capability in solving such problems (finding the best optimum values of these parameters). Using an efficient method of accurate designing to achieve the best performance is considered as the main challenge in terms of these classifiers. In this paper, an intelligent method based on one of the newest heuristic methods called inclined planes system optimization algorithm (IPO) has been proposed and implemented for the first time so that automatic designing of a neuro-fuzzy classifier is performed. IPO method is inspired by the dynamics of spherical objects’ sliding motion along a set of frictionless inclined planes based on which objects in cooperation with each other move towards the best response to the problem according to Newton’s Second Law and equations of motion. The results obtained from repetitive tests performed on several well-known databases with various numbers of reference classes as well as different feature vector lengths with acceptable and certain complexities indicated capability of the proposed method compared to other heuristic methods for automatic design of a neuro-fuzzy classifier.

Variable Length IPO and its application in concurrent design and train of ANFIS systems

In this paper, a new version of IPO called (VLIPO- Variable Length Inclined Planes System Optimization Algorithm) has been provided primarily. Then, an efficient tool for simultaneous design and training of an ANFIS (adaptive neuro-fuzzy inference system) has been proposed using the mentioned algorithm. It should be noted that till the present time, related research has been only dealing with finding type and location of membership functions or proposing a method of training such networks. Length of standard versions of heuristic algorithms has been mainly the reason for not specifying the type and location of membership and training an ANFIS network simultaneously. For the same reason, first, a new version of the IPO is introduced with such factors being variable. Then, such capability is used for specifying the type and location of membership functions and simultaneous training of an ANFIS classifier. It goes without saying that the idea of making variations in search factors could be also implemented in other heuristic methods. Some of them have been reported in the research. Therefore, the presented idea in the paper may be applied to other heuristic methods and used in the design and simultaneous training of an ANFIS. So, the results from the comparison made between implementing the proposed method and other methods of which a version with a variable length has been previously reported (PSO, ACOR, DE, and GA) have been presented in several well-known databases. The results showed the better execution of ANFIS classifier designed by VLIPO compared to other heuristic methods.

Infinite impulse response systems modeling by artificial intelligent optimization methods

Artificial Intelligent Optimization (AIO) algorithms learn from the past searches via using a group of individuals or agents. These Artificial Intelligence-based optimizing techniques are able to solve complex optimization problems with complicated constraints. They find the optimal in the low possible number of iterations, where optimal means the best from all possibilities selected from a special point of view. This paper presents a research on employing AIO methods with aim to Infinite Impulse Response (IIR) system modeling for design and optimization of IIR digital filters. The proposed methods cover a variety of AIO methods; algorithm based on evolution strategy (genetic algorithm) and heuristic algorithms (particle swarm optimization, population-based; gravitational search algorithm, and inclined planes system optimization, both population-based and based on Newton’s laws). In this paper, the IIR system modeling is solved as a constrained single-objective optimization problem in the Mean Squared Error (MSE) fitness function and is evaluated for two different benchmark IIR plants with high and low orders. To evaluate performance, efficiency and efficacy of the methods, two important criteria are used: “Indicator of Success (IoS)” and “Degree of Reliability (DoR)”. In addition, the effect of decreasing population size (search agents) is analyzed on the performance and efficiency of the algorithms. Simulation results clarify the success of the research in terms of the MSE, IoS and DoR.

Design of optimal CMOS ring oscillator using an intelligent optimization tool

This paper presents an intelligent sizing method to improve the performance and efficiency of a CMOS ring oscillator (RO). The proposed approach is based on the simultaneous utilization of powerful and new multi-objective optimization techniques along with a circuit simulator under a data link. The proposed optimizing tool creates a perfect trade-off between the contradictory objective functions in CMOS RO optimal design. This tool is applied for intelligent estimation of the circuit parameters (channel width of transistors), which have a decisive influence on RO specifications. Along the optimal RO design in an specified range of oscillation frequency, the Power Consumption, Phase Noise, Figure of Merit, Integration Index, Design Cycle Time are considered as objective functions. Also, in generation of Pareto front some important issues, i.e., Overall Nondominated Vector Generation, and Spacing are considered for more effectiveness of the obtained feasible solutions in application. Four optimization algorithms called Multi-Objective Genetic Algorithm, Multi-Objective Inclined Planes system Optimization, Multi-Objective Particle Swarm Optimization and Multi-Objective Modified Inclined Planes System Optimization (MOMIPO) are utilized for 0.18-mm CMOS technology with supply voltage of 1-V. Based on our extensive simulations and experimental results MOMIPO outperforms the best performance among other multi-objective algorithms in presented RO designing tool.

Inclined planes system optimization algorithm for IIR system identification

In this paper the digital infinite impulse response (IIR) filter design is modeled as an optimization problem. A new design method based on inclined planes system optimization (IPO) is introduced for the IIR system identification. IPO is a heuristic technique based on the dynamics of sliding motion along a frictionless inclined surface that has been demonstrated the reliable performance in solving of engineering complex problems. The effectiveness of the proposed method is verified in presence of the additive noise. In this work, both actual and reduced order identification of few benchmarked IIR plants is carried out in the simulations. Newton’s Mechanics-based, swarm intelligence based and conventional evolutionary algorithms are used to model the same examples and simulation results are evaluated. The final results clearly demonstrate the good performance and premier identification of the proposed method along with well-tuned other algorithms.

Erratum to: IIR model identification using a modified inclined planes system optimization algorithm

Erratum to: IIR model identification using a modified inclined planes system optimization algorithm

IIR model identification using a modified inclined planes system optimization algorithm

Inclined planes system optimization (IPO) is a new optimization algorithm inspired by the sliding motion dynamic along a frictionless inclined surface. In this paper, with the aim of create a powerful trade-off between the concepts of exploitation and exploration, and rectify the complexity of their structural parameters in the standard IPO, a modified version of IPO (called MIPO) is introduced as an efficient optimization algorithm for digital infinite-impulse-response (IIR) filters model identification. The IIR model identification is a complex and practical challenging problem due to multimodal error surface entanglement that many researches have been reported for it. In this work, MIPO utilizes an appropriate mechanism based on the executive steps of algorithm with the constant damp factors. To do this, unknown filter parameters are considered as a vector to be optimized. In implementation, at first, to demonstrate the effectiveness of the proposed method, 10 well-known benchmark functions have been considered for evaluating and testing. In addition, statistical analysis on the powerfulness, efficiency and applicability of the MIPO algorithm are presented. Obtained results in compared to some other popular methods, confirm the efficiency of the MIPO algorithm that makes the best optimal solutions and has a better performance and acceptable solutions.

UNSUPERVISED DATA AND HISTOGRAM CLUSTERING USING INCLINED PLANES SYSTEM OPTIMIZATION ALGORITHM

Within the last decades, clustering has gained significant recognition as one of the data mining methods, especially in the relatively new field of medical engineering for diagnosing cancer. Clustering is used as a database to automatically group items with similar characteristics. Researchers aim to introduce a novel and powerful algorithm known as Inclined Planes system Optimization (IPO), with capacity to overcome clustering problems. The proposed method identifies each agent used in the algorithm to indicate the centroids of the clusters and automatically select the number of centroids in each time interval (unsupervised clustering). The evaluation method for clustering is based on the Davies Bouldin index (DBi) to show cluster validity. Researchers compare known algorithm on series of data bases from various studies to demonstrate the power and capability of the proposed method. These datasets are popular for pattern recognition with diversity in space dimension. Method performance was tested on standard images as a dataset. Study results show significant method advantage over other algorithms.

UNSUPERVISED DATA AND HISTOGRAM CLUSTERING USING INCLINED PLANES SYSTEM OPTIMIZATION ALGORITHM

Within the last decades, clustering has gained significant recognition as one of the data mining methods, especially in the relatively new field of medical engineering for diagnosing cancer. Clustering is used as a database to automatically group items with similar characteristics. Researchers aim to introduce a novel and powerful algorithm known as Inclined Planes system Optimization (IPO), with capacity to overcome clustering problems. The proposed method identifies each agent used in the algorithm to indicate the centroids of the clusters and automatically select the number of centroids in each time interval (unsupervised clustering). The evaluation method for clustering is based on the Davies Bouldin index (DBi) to show cluster validity. Researchers compare known algorithm on series of data bases from various studies to demonstrate the power and capability of the proposed method. These datasets are popular for pattern recognition with diversity in space dimension. Method performance was tested on standard images as a dataset. Study results show significant method advantage over other algorithms.