This paper proposes an algorithm for optimization inspired by the imperialistic competition. Like other evolutionary ones, the proposed algorithm starts with an initial population. Population individuals called country are in two types: colonies and imperialists that all together form some empires. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. During this competition, weak empires collapse and powerful ones take possession of their colonies. Imperialistic competition hopefully converges to a state in which there exist only one empire and its colonies are in the same position and have the same cost as the imperialist. Applying the proposed algorithm to some of benchmark cost functions, shows its ability in dealing with different types of optimization problems.

Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition

A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.

Multiobjective evolutionary algorithms: A survey of the state of the art

Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

/pdf/a-survey-on-analysis-and-design-of-model-based-fuzzy-control-3hrjlrel0k.pdf

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Recently a novel DNA virus (Torque Teno Virus (TTV) has been identified in Japan and shown to be associated with elevated aminotransferase s levels after transfusion. However the exact role of TTV in pathogenesis of liver disease is yet to be established. The purpose of this study was to determine the prevalence of TTV in thalassemic patients and its relationship with elevated alanine-aminotransfrase (ALT) and aspartateaminotransfrase (AST). This cross-sectional analysis study was conducted on 452 thallasemic patients. Serums were collected from all of the patients, first ALT and AST levels were determined. Then, after DNA extraction, TTV DNA was amplified and detected using semi-nested PCR, followed by gel electrophoresis. Demographic characteristics and clinical data were collected from each participant for statistical analysis. The findings showed that 160 of 452 (35.4%) samples had TTV DNA detected by PCR. From 160 TTV DNA positive, 98 (61.20%) were female and 62 (38.80%) of them were male (P=0.549). The mean ALT and AST values in TTV positive group were higher than in TTV negative group, and the difference was statistically significant (p<0.0001). The result showed that the prevalence of TTV in thalassemic patients in Jahrom is less than other studies in Iran and the mean ALT and AST values in TTV positive individuals were about 2 times more than in TTV negative individuals.

/pdf/molecular-prevalence-and-clinical-importance-of-torque-teno-2fi9z1ixeg.pdf

Molecular Prevalence and Clinical Importance of Torque Teno Virus Infection in Thalassemia Patients as High Risk Individuals

After demonstrating adequately the usefulness of evolutionary multiobjective optimization (EMO) algorithms in finding multiple Pareto-optimal solutions for static multiobjective optimization problems, there is now a growing need for solving dynamic multiobjective optimization problems in a similar manner. In this paper, we focus on addressing this issue by developing a number of test problems and by suggesting a baseline algorithm. Since in a dynamic multiobjective optimization problem, the resulting Pareto-optimal set is expected to change with time (or, iteration of the optimization process), a suite of five test problems offering different patterns of such changes and different difficulties in tracking the dynamic Pareto-optimal front by a multiobjective optimization algorithm is presented. Moreover, a simple example of a dynamic multiobjective optimization problem arising from a dynamic control loop is presented. An extension to a previously proposed direction-based search method is proposed for solving such problems and tested on the proposed test problems. The test problems introduced in this paper should encourage researchers interested in multiobjective optimization and dynamic optimization problems to develop more efficient algorithms in the near future.

/pdf/dynamic-multiobjective-optimization-problems-test-cases-dx88tj1lyn.pdf

Dynamic multiobjective optimization problems: test cases, approximations, and applications

Kinematics studies the motion of bodies without consideration of the forces or moments that cause the motion. Robot kinematics refers the analytical study of the motion of a robot manipulator. Formulating the suitable kinematics models for a robot mechanism is very crucial for analyzing the behaviour of industrial manipulators. There are mainly two different spaces used in kinematics modelling of manipulators namely, Cartesian space and Quaternion space. The transformation between two Cartesian coordinate systems can be decomposed into a rotation and a translation. There are many ways to represent rotation, including the following: Euler angles, Gibbs vector, Cayley-Klein parameters, Pauli spin matrices, axis and angle, orthonormal matrices, and Hamilton 's quaternions. Of these representations, homogenous transformations based on 4x4 real matrices (orthonormal matrices) have been used most often in robotics. Denavit & Hartenberg (1955) showed that a general transformation between two joints requires four parameters. These parameters known as the Denavit-Hartenberg (DH) parameters have become the standard for describing robot kinematics. Although quaternions constitute an elegant representation for rotation, they have not been used as much as homogenous transformations by the robotics community. Dual quaternion can present rotation and translation in a compact form of transformation vector, simultaneously. While the orientation of a body is represented nine elements in homogenous transformations, the dual quaternions reduce the number of elements to four. It offers considerable advantage in terms of computational robustness and storage efficiency for dealing with the kinematics of robot chains (Funda et al., 1990). The robot kinematics can be divided into forward kinematics and inverse kinematics. Forward kinematics problem is straightforward and there is no complexity deriving the equations. Hence, there is always a forward kinematics solution of a manipulator. Inverse kinematics is a much more difficult problem than forward kinematics. The solution of the inverse kinematics problem is computationally expansive and generally takes a very long time in the real time control of manipulators. Singularities and nonlinearities that make the

/pdf/robot-kinematics-forward-and-inverse-kinematics-1pcuyobcf6.pdf

Robot Kinematics: Forward and Inverse Kinematics

In this paper, a 2 DOF planar robot was controlled by Fuzzy Logic Controller tuned with a particle swarm optimization. For a given trajectory, the parameters of Mamdani-type-Fuzzy Logic Controller (the centers and the widths of the Gaussian membership functions in inputs and output) were optimized by the particle swarm optimization with three different cost functions. In order to compare the optimized Fuzzy Logic Controller with different controller, the PID controller was also tuned with particle swarm optimization. In order to test the robustness of the tuned controllers, the model parameters and the given trajectory were changed and the white noise was added to the system. The simulation results show that Fuzzy Logic Controller tuned by particle swarm optimization is better and more robust than the PID tuned by particle swarm optimization for robot trajectory control.

A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control

This article presents a novel tuning design of Proportional-Integral-Derivative (PID) controller in the Automatic Voltage Regulator (AVR) system by using Cuckoo Search (CS) algorithm with a new time domain performance criterion. This performance criterion was chosen to minimize the maximum overshoot, rise time, settling time and steady state error of the terminal voltage. In order to compare CS with other evolutionary algorithms, the proposed objective function was used in Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms for PID design of the AVR system. The performance of the proposed CS based PID controller was compared to the PID controllers tuned by the different evolutionary algorithms using various objective functions proposed in the literature. Dynamic response and a frequency response of the proposed CS based PID controller were examined in detail. Moreover, the disturbance rejection and robustness performance of the tuned controller against parametric uncertainties were obtained, separately. Energy consumptions of the proposed PID controller and the PID controllers tuned by the PSO and ABC algorithms were analyzed thoroughly. Extensive simulation results demonstrate that the CS based PID controller has better control performance in comparison with other PID controllers tuned by the PSO and ABC algorithms. Furthermore, the proposed objective function remarkably improves the PID tuning optimization technique.

A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system

The inverse kinematics problem of robot manipulators is solved analytically in order to have complete and simple solutions to the problem. This approach is also called as a closed form solution of robot inverse kinematics problem. In this paper, the inverse kinematics of sixteen industrial robot manipulators classified by Huang and Milenkovic were solved in closed form. Each robot manipulator has an Euler wrist whose three axes intersect at a common point. Basically, five trigonometric equations were used to solve the inverse kinematics problems. Robot manipulators can be mainly divided into four different group based on the joint structure. In this work, the inverse kinematics solutions of SN (cylindrical robot with dome), CS (cylindrical robot), NR (articulated robot) and CC (selectively compliant assembly robot arm-SCARA, Type 2) robot manipulator belonging to each group mentioned above are given as an example. The number of the inverse kinematics solutions for the other robot manipulator was also summarized in a table.

The inverse kinematics solutions of industrial robot manipulators

Comparative study of performance indices for fundamental robot manipulators

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