Bats echolocation-inspired algorithms for global optimisation problems

Swarm intelligence algorithms, are among popular metaheuristic methods, developed and inspired by the collective behaviour of swarms that have attracted significant attention of researchers. The works related to swarm intelligence algorithms include the development of the algorithm itself, its modification and improvisation as well as its application in solving global optimisation problems. This thesis presents works on swarm intelligence algorithms that are inspired by real echolocation of a colony of bats and its performance evaluation to solve optimisation problems. The aim of the research is to introduce novel form of swarm intelligence algorithms based on real echolocation behaviour of bats. An adaptive bats sonar algorithm is proposed for solving single objective optimisation problems. A modified adaptive bats sonar algorithm is then proposed for solving constrained optimisation problems. Furthermore, a dual-particle swarm optimisation-modified adaptive bats sonar algorithm is proposed for solving multi objective optimisation problems. The algorithm is a hybrid algorithm that operates using dual level search strategy that takes merits of a particle swarm optimisation algorithm and a modified adaptive bats sonar algorithm. The superior performances of the developed bats echolocation-inspired algorithms are verified through rigorous tests with optimisation benchmark test functions and problems. Further, the performances of the developed algorithms are assessed in solving selected practical problems in business, mechanical/manufacturing engineering and electrical engineering fields. The results validate the better performance of the developed algorithms in single objective optimisation, constrained optimisation and multi objective optimisation problems of various fields

Bat echolocation-inspired algorithms for global optimisation problems

Optimisation according to the definition of Merriam-Webster Dictionary is an act, process, or methodology of making something (as a design, system, or decision) as fully perfect, functional, or effective as possible. In general, optimisation is the process of obtaining either the best minimum or maximum result under specific circumstance. The optimisation process engages with defining and examining objective or fitness function that suits some parameters and constraints. Nowadays, a vast range of business, management and engineering applications utilise the optimisation approach to save time, cost and resources while gaining better profit, output, performance and efficienc

Kejuruteraan Mekatronik, Integrasi antara Domain Ilmu dalam Mendepani Era IR4.0

Minggu ini adalah minggu yang bermakna buat para calon Sijil Pelajaran Malaysia (SPM) 2022 apabila keputusan SPM 2022 diumumkan pada Khamis lalu. Menurut Ketua Pengarah Pendidikan, Datuk Pkharuddin Ghazali. Seramai 10,109 calon SPM 2022 memperoleh semua A manakala prestasi Gred Purata Nasional (GPN) menunjukkan peningkatan berbanding tahun terdahulu. Tahniah kepada calon yang berjaya, manakala yang lain, anda masih ada banyak peluang untuk dicuba demi kemajuan dan kejayaan masing-masing

Mekatronik penuhi keperluan IR 4.0

BIDANG kejuruteraan mekatronik sedang berkembang seiring dengan keperluan Revolusi Perindustrian Keempat atau lebih dikenali sebagai IR 4.0

Fuzzy logic controller optimized by MABSA for DC servo motor on physical experiment

This paper represents the control system of the DC servo motor using fuzzy logic controller optimized by MABSA. The fuzzy logic controller that can use in a wide range is well known to the industry application and control system. However, there are still problem with the speed and position control of DC servo motor. Both speed and position cannot be balanced when loading and unloading materials. Therefore, the fuzzy logic controller will be designed using the Matlab toolbox and then will be optimized by the modified adaptive bats sonar algorithm (MABSA) to solve this problem. The best position of the range of the membership functions will be generated through the algorithm and then will be inserted in the membership function of the designed fuzzy logic controller. After the proposed design is fully developed, an experiment will be carried out to test the performance. The performance will be in terms of rising time, settling time and percentage of overshoot. The experiment will be using Arduino as the microcontroller and encoder as the feedback. The experiment will be compared with the cases that use the fuzzy logic controller only without the optimization of MABSA. The result shows that the proposed design gives a 19% improvement in rising time and 8% in settling time. In conclusion, the proposed design of FLC optimized by MABSA is better compared to FLC without optimization

Comparison performances between MABSA-PI controller and MABSA-PD controller for DC motor position system

The goal of this project is to create two different types of controllers for regulating DC motor position systems which are the MABSA-PI controller and the MABSA-PD controller. The MATLAB controllers are developed using the Simulink toolbox, while the transfer function of a DC motor is obtained from its circuit. Modified adaptive bats sonar algorithm (MABSA) will be implemented in the PI controller and PD controller as the main algorithm. MABSA is one of the swarm intelligences and is usually for an optimization problem. Next, the tuning method for the PI controller and PD controller will be the trial-and-error method. The combination graph of each controller will be shown in Simulink scope as the project is tested for various positions. The controller's ability to regulate the position of a DC motor was studied. The comparision among the system's transient response parameters, including percentage overshoot, rising time (Tr), and settling time (Ts), would be discused. Various toolbox functions available in Simulink software can be employed to repossess these parameter outcomes

PSO-LFDE Algorithm on Constrained Real-Parameter Optimisation Test Functions

This paper introduces a new version of the particle swarm optimisation (PSO) algorithm particle swarm optimisation with Lévy Flight and the Doppler Effect (PSO-LFDE), maintaining an optimal balance between the exploration and exploitation phases of the optimisation process. The proposed algorithm will hold a better exploration–exploitation equilibrium if the contributions of convergence and diversity and global and individual bests in attracting particles are maintained in balance. The proposed PSO-LFDE algorithm is compared with the PSO algorithm by Gaing on single-objective constrained real-parameter optimisation test functions. The results indicated that the PSO-LFDE has achieved competitive results on the single-objective constrained real-parameter optimisation test functions as compared to PSO algorithm by Gaing. Thus, the PSO-LFDE is validated as a stable, well-designed algorithm and can be a functional alternative approach to deal with various single-objective constrained real-parameter optimisation problems

A mathematical model of a brushed DC motor system

Mathematical model has been proposed for some system that involves a brushed DC motor and it is widely used in industry. Brushed DC motor ideals for applications with a low- torque, manage to change pace or speed and it is widely used in many applications such as x-y table positioning system, conveyor systems and other system that required to use the features that brushed DC motor have. Mathematical model of brushed DC motor in order to verify the performance of the DC motor. In this paper, mathematical model of brushed DC motor will be derived from a brushed DC motor circuit that consist of two parts that are electrical and mechanical part. To validate the functionality of mathematical model, the performance of the brushed DC motor without any controller will be compared with the brushed DC motor with the presence of PI-PD controller that will be tuned by trial-and-error method. Performances of both brushed DC motor with and without controller will be compared in terms of transient response which are, rise time, Tr, settling time, Ts, steady state error, ess and lastly percentage overshoot. At the end of the study, the brushed DC motor with PI-PD controller show a better performance compared to the brushed DC motor without any controller

A comparison of type 1 and type 2 fuzzy logic controller for DC motor system

In today’s modern world application, there are high-level uncertainties that are faced and affecting every kind of operation in various industries. Thus, researchers, today are on the rise to find solutions that will able help to reduce these uncertainties in many types of situations especially control system applications. The type-1 Fuzzy Logic Controller is shown not to be able to handle a high level of uncertainties and the new type-2 Fuzzy Logic Controller is now being said to be able to do a better performance than the type-1 especially in controlling a DC motor system. This can be seen by the simulation graph that clearly observes the comparison of both types. The result where FLC type 2 outperforms FLC type 1 with reduced settling time and rising time can be seen. In conclusion, the new type-2 FLC is now able to overcome the limits of what type-1 FLC are able to do and this will give birth to better and improved performance of new Fuzzy Logic Controllers that is well suited as controllers for DC motor system. This paper will briefly discuss the comparison between both of these types of FLC and the benefits

Metaheuristic nature-inspired algorithms for reservoir optimization operation: A systematic literature review

The purpose of this systematic literature review (SLR) article is to discuss the findings of the state-of-art metaheuristic nature-inspired algorithm (MHNIA) in reservoir optimization operation. The rationale of this approach is to elucidate the optimal way as decision making that implemented MHNIA for several complex problems in reservoir optimization operation. Commonly, the metaheuristic optimization algorithm has always been used in hydrology field, especially in reservoir optimization. Hence, this presented study reviewed a considerable amount from the previous studies of commonly nature-based optimization algorithms applied in reservoir operations. Hence, preferred reporting items for systematic review and meta-analyses (PRISMA) has been used as guidance. The source was utilized from two primary journal databases: Scopus and web of science. According to the proposed search string, the findings managed to express into nine main themes which are optimize in water release, optimize reservoir operation problems, optimize hydropower operation, optimize condensate fluids in reservoir storage, optimize water pumped storage, optimize water quality control, optimize system performance operation, optimize water demand and optimize reservoir control as flood preventing. Overall, 24 articles that passed the minimum quality were retrieved using systematic searching strategies