Opposition based Spiral Dynamic Algorithm with an Application to a PID Control of a Flexible Manipulator

This paper presents an improved version of a Spiral Dynamic Algorithm (SDA). The original SDA is a relatively simple optimization algorithm. It uses a spiral strategy to move search agents within the feasible search space. However, SDA suffers from a premature convergence due to an unbalanced diversification and intensification throughout its search operation. Hence, the algorithm unable to acquire an optimal accuracy solution. An Opposition learning is adopted into SDA to improve the searching strategy of the SDA agents. Therefore in the proposed strategy, a random and a deterministic approaches are synergized and complement each other. The algorithm is tested on several benchmark functions in comparison to the original SDA. A statistical nonparametric Wilcoxon sign rank test is conducted to analyze the accuracy achievement of both algorithms. For solving a real world application, the algorithms are applied to optimize a PID controller for a flexible manipulator system. Result of the test on the benchmark functions shows that the Opposition based SDA outperformed the SDA significantly. For solving the PID control design, both algorithms acquire PID parameters and hence can control the flexible manipulator very well. However, the proposed algorithm shows a better control response

Hybrid genetic manta ray foraging optimization and its application to interval type 2 fuzzy logic control of an inverted pendulum system

This paper presents an improvised version of Manta-Ray Foraging Optimization (MRFO) by using components in Genetic Algorithm (GA). MRFO is a recent proposed algorithm which based on the behaviour of manta rays. The algorithm imitates three foraging strategies of this cartilaginous fish, which are chain foraging, cyclone foraging and somersault foraging to find foods. However, this optimization algorithm can be improved in its strategy which increases its accuracy. Thus, in this proposed improvement, mutation and crossover strategy from GA were adopted into MRFO. Crossover operation is a convergence action which is purposely to pull the agents towards an optimum point. At the meanwhile, mutation operation is a divergence action which purposely to spread out the agents throughout wider feasible region. Later, the algorithms were performed on several benchmark functions and statically tested by using Wilcoxon signed-rank test to know their performances. To test the algorithm with a real application, the algorithms were applied to an interval type 2 fuzzy logic controller (IT2FLC) of an inverted pendulum system. Result of the test on benchmark functions shows that GMRFO outperformed MRFO and GA and it shows that it provides a better parameter of the control system for a better response

Non-dominated sorting manta ray foraging algorithm with an application to optimize PD control

This paper presents a multi-objective (MO) version of Manta Ray Foraging Algorithm (MRFO) by using components in Non-Dominated Sorting Genetic Algorithm (NSGAII). MFRO is a recent developed algorithm which was inspired from behavior of a cartilaginous fish called Manta Ray. MRFO search solution by using three strategies of manta ray which are chain foraging, cyclone foraging and somersault foraging. However, this algorithm solves only single-objective problem and can be improved to solve multi-objective problem. Thus, non-dominated sorting (NS) strategies including crowding distance (CD) were adopted into MRFO. NS is a sorting technique based on Pareto’s game. It is a fast strategy to develop a good characteristic of Pareto’s front (PF). Meanwhile, CD is a strategy to preserve good distribution of solutions along the PF. This proposed algorithm is called NSMRFO. It is tested using several benchmark functions and its performance is compared to its parent by using statically analysis of hypervolume indicator. Then, it is applied to a Proportional-Derivative (PD)-controller for an Inverted Pendulum System (IPS) in order to know its performance on real-world application. Result of the NSMRFO on benchmark functions shows that it outperforms NSGAII and satisfactorily optimizes PD-control for the IPS

Simulated kalman filter algorithm with improved accuracy

This paper presents an improved Simulated Kalman Filter optimiza-tion algorithm. It is a further enhancement of a Simulated Kalman Filter (SKF) optimization algorithm. SKF is a random based optimization algorithm inspired from Kalman Filter theory. An exponential term is introduced into Estimation stage of SKF to speed up the searching process and gain more chances in find-ing better solutions. Cost function value that represent an accuracy of a solution is considered as the ultimate goal. Every single agent carries an information about the accuracy of a solution in which will be used to compare with other so-lutions from other agents. A solution that has a lower cost function is consid-ered as the best solution. The algorithm is tested with various benchmark func-tions and compared with the original SKF algorithm. Result of the analysis on the accuracy tested on the benchmark functions shows that the proposed algo-rithm outperforms SKF significantly

Hybrid bacterial foraging sine cosine algorithm for solving global optimization problems

This paper proposes a new hybrid algorithm between Bacterial Foraging Algorithm (BFA) and Sine Cosine Algorithm (SCA) called Hybrid Bacterial Foraging Sine Cosine Algorithm (HBFSCA) to solve global optimization problems. The proposed HBFSCA algorithm synergizes the strength of BFA to avoid local optima with the adaptive step-size and highly randomized movement in SCA to achieve higher accuracy compared to its original counterparts. The performances of the proposed algorithm have been investigated on a set of single-objective minimization problems consist of 30 benchmark functions, which include unimodal, multimodal, hybrid, and composite functions. The results obtained from the test functions prove that the proposed algorithm outperforms its original counterparts significantly in terms of accuracy, convergence speed, and local optima avoidance

GPS navigation system

A general automotive navigation system is a satellite navigation system designed for use in automobiles. It typically uses GPS to acquire position data to locate the user on a road in the unit's map database. In the other side an automated guided vehicle or so called automatic guided vehicle (AGV) is a system designed to perform an unmanned travel of a vehicle. This vehicle can be used in many area especially dangerous places such as at harbor, surveying and military purpose. There are two main sensors AGV use for navigation, a wired sensor and a wireless sensor. The purpose of this project is to combine the concept of both systems to produce a prototype of an AGV navigated by a combination of GPS receiver and digital compass instead of using camera, guide tape, laser, inertial or gyroscopic. The project called as GPS Navigation System. The whole project can be divided into two main parts. The first part is concern on the hardware development where all electronics component are connected via the circuit design using wire wrapping technique. A GPS receiver, digital compass, RF receiver, and keypad are the input components while RF transmitter, LED, and LCD display are the output components. All of this input and output devices are connected to a PIC14F8550 microcontroller. The second part is about software programming which used to control the whole operation of the system. The program is written using MikroBasic and downloaded to the PIC18F4550 using PIC programmer after compiled into a *.hex file. As a conclusion, this system is designed to enable user to control and define the destination of a vehicle from a base station wirelessly and the advantage comes from the key component, GPS receiver which enable the vehicle to do movement in any direction according to the signal received from the base station without need a track

Adaptive sine-cosine algorithms for global optimization

This paper introduces improved versions of a Sine-Cosine algorithm called Adaptive Sine-Cosine algorithms. It is made adaptive through incorporation of a linear and an exponential term with respect to an individual agent’s fitness. Based on the newly introduced formulas, an individual agent moves with a dynamic and different step sizes compared to other agents through the whole searching process. It also introduces a balance exploration and exploitation strategies. The proposed algorithms in comparison to the original algorithm are then tested with several test functions that have different properties and landscapes. The algorithms performance in terms of their achievement of finding a near optimal solution is analyzed and discussed. Numerical result of the test shows that the proposed algorithms have achieved a better accuracy. The finding also shows that the proposed algorithms have attained a faster convergence toward the near optimal solution

Exponentially adaptive sine-cosine algorithm for global optimization

Sine-Cosine algorithm (SCA) is an optimization algorithm formulated based on mathematical Sine and Cosine terms. It is widely used to solve various optimization problems. However the algorithm performance in terms of accuracy is not at optimum level. This paper presents an improved SCA with a new adaptive strategy based on an exponential term. The exponential term is adopted to establish a relationship between searching agents step size and fitness cost. The agents step size is exponentially changed due to the change of the fitness cost. The proposed algorithm is tested with a set of benchmark functions in comparison to the original SCA. A statistical analysis of the algorithms performance in terms of their accuracy is conducted. A Wilcoxon Sign Rank test is adopted to check significance level of the proposed algorithm as compared to the original SCA. Based on the simulation conducted, the adaptive strategy has resulted a significance improvement of the accuracy and convergence speed

Spiral sine-cosine algorithm for global optimization

This paper presents a Hybrid Spiral and Sine-Cosine Algorithm (SSCA). Sine-Cosine algorithm (SCA) is a random-based optimization that utilizes an elitism approach and adaptive step size in its strategy. The step size is linearly varied and thus has caused the algorithm to produce steady convergence trend towards an optimal solution. It also has resulted the algorithm unable to achieve the true optimal solution. On the other hand, Spiral Dynamic Algorithm (SDA) is a deterministic-based algorithm that offers a nonlinear trend of agents step size in its operation. Therefore, an adoption of spiral equation from SDA into SCA is proposed as a solution to increase SCA convergence speed and its corresponding accuracy. The proposed algorithm is tested with a set of benchmark functions. Its accuracy and convergence trend performances are measured and recorded. A nonparametric Wilcoxon Sign Rank test is applied to statistically analyze the significance improvement of the SSCA accuracy in comparison to original SCA. Finding from the accuracy analysis indicates that the proposed SSCA algorithm significantly outperformed the original SCA. Moreover, from a graphical result, it shows that the SSCA has faster speed compared to another contestant algorithm

A Kalman-Filter-Based Sine-Cosine Algorithm

This paper presents a Kalman-Filter-based Sine Cosine algorithm (KFSCA). It is a synergy of a Simulated Kalman Filter (SKF) algorithm and a Sine Cosine (SCA) algorithm. SKF is a random based optimization algorithm inspired from the Kalman Filter theory. A Kalman gain is formulated following the prediction, measurement and estimation steps of the Kalman filter design. The Kalman gain is utilized to introduce a dynamic step size of a search agent in the SKF algorithm. On the other hand, a Sine Cosine algorithm is formulated based on mathematical sine and cosine terms. A random based searching strategy is formulated through a little modification on both of the terms. In the KFSCA, a Kalman gain is introduced to vary an individual agent’s step and thus balances exploration and exploitation strategies of the original SCA. Cost function value that represent an accuracy of a solution is considered as the ultimate goal. Every single agent carries an information about the accuracy of a solution in which will be used to compare with other solutions from other agents. A solution that has a lower cost function is considered as the best solution. The algorithm is tested with various benchmark functions and compared with the original SCA algorithm. Result of the analysis on the accuracy tested on the benchmark functions is tabulated in a table form and shows that the proposed algorithm outperforms SCA significantly. The result also is presented in a graphical form to have a clearer visual on the solution