Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program

Comparison of different optimization criteria for optimal sizing of hybrid active power filters parameters

Praise Worthy Prize granted a permission for Brunel University London to archive this article in BURA.Harmonic distortion in power systems has increased considerably due to the increasing use of nonlinear loads in industrial firms and elsewhere. This distortion can give rise to overheating in all sectors of the power system, leading to reduced efficiency, reliability, operational life and sometimes failure. This article seeks to propose a new methodology for the optimal sizing of hybrid active power filter (HPF) parameters in order to overcome the difficulties in hybrid power filters design when estimating the preliminary feasible values of the parameters. Sequential Quadratic Programming based on FORTRAN subroutines is used to find out the planned filter size in two different optimization criteria depending on design concerns. The first criterion is to minimize the total voltage harmonic distortion. The second one is to maximize the load power factor, while taking into account compliance with IEEE standard 519-1992 limits for the total voltage harmonic distortion and the power factor.The effectiveness of the proposed filter is discussed using four exemplary case

免疫学的および進化的アルゴリズムに基づく改良された群知能最適化に関する研究

富山大学・富理工博甲第175号・楊玉・2020/3/24富山大学202

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature- inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field

Hybrid ACO and SVM algorithm for pattern classification

Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

PENGEMBANGAN metaheuristicOpt: R PACKAGE UNTUK OPTIMASI DENGAN MENGGUNAKAN ALGORITMA POPULATION BASED METAHEURISTIC

Optimasi diterapkan diberbagai disiplin ilmu seperti teknik sipil, teknik mekanika, ekonomi, teknik elektro dan lain-lain. Karena optimasi diterapkan diberbagai disiplin ilmu maka optimasi sangatlah penting. Banyak sekali pendekatan yang dilakukan dalam melakukan optimasi salah satunya adalah population based metaheuristic. Di bahasa pemrograman R terdapat package optimasi menggunakan algoritma population based metaheuristic yaitu “metaheuristicOpt”. Algoritma-algoritma pada R package “metaheuristicOpt” memiliki dua kelemahan yaitu kompleksitas yang tinggi dan hyperparameter yang sedikit. Tujuan penelitian ini adalah mengembangkan R package “metaheuristicOpt” dengan menambahkan 10 algoritma baru yaitu clonal selection algorithm, differential evolution, shuffled frog leaping, cat swarm optimization, artificial bee colony algorithm, krill herd algorithm, cuckoo search, bat algorithm, gravitational based search dan black hole optimization untuk menutupi kelemahan algoritma sebelumnya. Dalam menambahkan algoritma ini kami menjaga konsistensi arsitektur package tersebut. Untuk menganalisis performa dari algoritma baru yang ditambahkan setiap fungsi diuji menggunakan 13 fungsi uji. Yang menjadi tolok ukur eksperimen adalah fitness dan waktu eksekusi. Berdasarkan eksperimen yang dilakukan beberapa algoritma baru memiliki kecepatan eksekusi yang lebih cepat dari algoritma sebelumnya dan beberapa algoritma baru juga memiliki fitness yang lebih baik dari algoritma sebelumnya. Optimization is applied in various scientific disciplines such as civil engineering, mechanical engineering, economics, electrical engineering and others. Because optimization is applied in various disciplines, optimization is very important. There are a lot of approaches used to optimize one of them is population based metaheuristic. In the R programming language there is an optimization package using the population based metaheuristic algorithm, namely "metaheuristicOpt". Algorithms in the R package "metaheuristicOpt" have two disadvantages: high complexity and few hyperparameters. Our goal is to develop the "metaheuristicOpt" package by adding 10 new algorithms namely clonal selection algorithm, differential evolution, shuffled frog leaping, cat swarm optimization, artificial bee colony algorithm, krill herd algorithm, cuckoo search, bat algorithm, gravitational based search and black hole optimization to cover up the weaknesses of the previous algorithm. In adding of these algorithms we maintain the consistency of the package architecture. To analyse performance of the new algorithm added to each function of the algorithm, experiments were carried out using 13 test functions. The benchmarks of the experiment are fitness and execution time. Based on experiments some of new algorithms added have a faster execution speed and better fitness than the previous algorithms

Elevation, pitch and travel axis stabilization of 3DOF helicopter with hybrid control system by GA-LQR based PID controller

This research work presents an efficient hybrid control methodology through combining the traditional proportional-integral-derivative (PID) controller and linear quadratic regulator (LQR) optimal controlher. The proposed hybrid control approach is adopted to design three degree of freedom (3DOF) stabilizing system for helicopter. The gain parameters of the classic PID controller are determined using the elements of the LQR feedback gain matrix. The dynamic behaviour of the LQR based PID controller, is modeled and the formulated in state space form to enable utlizing state feedback controller technique. The performance of the proposed LQR based LQR controller is improved by using Genetic Algorithm optimization method which are adopted to obtain optimum values for LQR controller gain parameters. The LQR-PID hybrid controller is simulated using Matlab environment and its performance is evaluated based on rise time, settling time, overshoot and steady state error parameters to validate the proposed 3DOF helicopter balancing system. Based on GA tuning approach, the simulation results suggest that the hybrid LQR-PID controller can be effectively adopted to stabilize the 3DOF helicopter system

Improved Malware detection model with Apriori Association rule and particle swarm optimization

The incessant destruction and harmful tendency of malware on mobile devices has made malware detection an indispensable continuous field of research. Different matching/mismatching approaches have been adopted in the detection of malware which includes anomaly detection technique, misuse detection, or hybrid detection technique. In order to improve the detection rate of malicious application on the Android platform, a novel knowledge-based database discovery model that improves apriori association rule mining of a priori algorithm with Particle Swarm Optimization (PSO) is proposed. Particle swarm optimization (PSO) is used to optimize the random generation of candidate detectors and parameters associated with apriori algorithm (AA) for features selection. In this method, the candidate detectors generated by particle swarm optimization form rules using apriori association rule. These rule models are used together with extraction algorithm to classify and detect malicious android application. Using a number of rule detectors, the true positive rate of detecting malicious code is maximized, while the false positive rate of wrongful detection is minimized. The results of the experiments show that the proposed a priori association rule with Particle Swarm Optimization model has remarkable improvement over the existing contemporary detection models. © 2019 Olawale Surajudeen Adebayo and Normaziah Abdul Aziz