Improved versions of the bees algorithm for global optimisation

This research focuses on swarm-based optimisation algorithms, specifically the Bees Algorithm. The Bees Algorithm was inspired by the foraging behaviour of honey bees in nature. It employs a combination of exploration and exploitation to find the solutions of optimisation problems. This thesis presents three improved versions of the Bees Algorithm aimed at speeding up its operation and facilitating the location of the global optimum. For the first improvement, an algorithm referred to as the Nelder and Mead Bees Algorithm (NMBA) was developed to provide a guiding direction during the neighbourhood search stage. The second improved algorithm, named the recombination-based Bees Algorithm (rBA), is a variant of the Bees Algorithm that utilises a recombination operator between the exploited and abandoned sites to produce new candidates closer to optimal solutions. The third improved Bees Algorithm, called the guided global best Bees Algorithm (gBA), introduces a new neighbourhood shrinking strategy based on the best solution so far for a more effective exploitation search and develops a new bee recruitment mechanism to reduce the number of parameters. The proposed algorithms were tested on a set of unconstrained numerical functions and constrained mechanical engineering design problems. The performance of the algorithms was compared with the standard Bees Algorithm and other swarm based algorithms. The results showed that the improved Bees Algorithms performed better than the standard Bees Algorithm and other algorithms on most of the problems tested. Furthermore, the algorithms also involve no additional parameters and a reduction on the number of parameters as well

Al-Jazari contribution on the development of water supply systems

The objective of this study is to highlight AI-Jazari contribution in the development of water supply system. The significance of the study is that it places emphais on AI-Jazari and his inventions which laid the foundation for the machinery of irrigation and machine design. The methodology adopted in this research is library based and data is collected from reliable resources. The main focus of the chapter is to understand AI-Jazari's contribution in water raising machines which some of it can still be seen in several contemporary machines. The chapter explores the fifth chapter of his book which presents five different, useful inventions mainly to raise water for irrigation and domestic purpos

Assembly sequence optimization using the bees algorithm

The determination of the assembly sequence is an important decision in assembly planning. Optimum sequence selection is challenging because of several reasons such as optimization criteria and precedence constraints. Furthermore, a product can be assembled in many different alternatives in accordance with different sequences, thereby making the optimization of assembly sequences a multi-modal solution optimization problem. To allow the process planner to decide, unique optimum solutions are required to be develop as much as possible. In this study, the assembly sequence of a product was optimized by applying an algorithm known as the Bees Algorithm. To assess the performance of this Algorithm, the results are compared with results found by other algorithms. It is shown that, the Bees Algorithm obtained similar optimum fitness value with other algorithms but with the greatest number of optimal assembly sequences. As a result, the Bees Algorithm outperforms other algorithms in dealing with the multi-modal optimization problem of assembly sequence optimization

Application of the Bees Algorithm for Constrained Mechanical Design Optimisation Problem

Optimisation is a technique or procedure to find the optimal or feasible solution whether it is to minimise or maximise by comparing other possible solutions until the best solution is found. Nowadays, many optimisation algorithms have been introduced due to the advancement of technology such as Teaching Learning Based Optimisation (TLBO), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO) and the Bees Algorithm. The Bees Algorithm is considered as one of the best optimisation algorithms because it has been successfully solved different type optimisation problem from in various field. It is inspired by the foraging behaviour of honey bees in nature. This study applies the Bees Algorithm to minimise the mass of disc clutch brake in its design. To find the optimal solution for the multiple disc clutch design, the Bees Algorithm will be used and expected to give better result compared to other optimisation algorithms that already have been used

The Effects of FDM Printing Parameters on the Compression Properties of Polymethylmethacrylate (PMMA) using Finite Element Analysis

3D printing technology has become a favored alternative in fabricating parts due to its flexibility in product customization. Recently, an abundant number of studies have been conducted to improve the overall quality of the 3D printed parts. One of the essential qualities is to provide mechanical properties that fulfill the functionality of the final product. Thus, providing the best option in tailoring the mechanical properties of 3D printed parts is very useful. This paper investigates the effects of printing parameters on the mechanical properties of Polymethylmethacrylate (PMMA) using finite element analysis (FEA). Taguchi's 33 design-of-experiment methods were used to design the experiment for the following printing parameters: shell thickness, type of infill, and infill density. The compressive test was performed using Ansys software and the variables under study were strain and total deformation. The results obtained from the FEA simulation show that the strain and total deformation are mainly influenced by infill density, followed by the type of infill and shell thickness. It is deduced from the study that the optimum printing parameters with higher infill density (70%) and combination with triangular infill pattern able to hold the structure more rigidly, therefore providing more resistance against deformation. This study proposed a platform for determining the mechanical properties of 3D models for FDM printed parts using FEA analysis

The effects of FDM printing parameters on the compression properties of polymethylmethacrylate (PMMA) using finite element analysis

3D printing technology has become a favored alternative in fabricating parts due to its flexibility in product customization. Recently, an abundant number of studies have been conducted to improve the overall quality of the 3D printed parts. One of the essential qualities is to provide mechanical properties that fulfill the functionality of the final product. Thus, providing the best option in tailoring the mechanical properties of 3D printed parts is very useful. This paper investigates the effects of printing parameters on the compression properties of Polymethylmethacrylate (PMMA) using finite element analysis (FEA). Taguchi's 33 design-of-experiment methods were used to design the experiment for the following printing parameters: shell thickness, type of infill, and infill density. The compressive test was performed using Ansys software and the variables under study were strain and total deformation. The results obtained from the FEA simulation show that the compressive strain and total deformation are mainly influenced by infill density, followed by the type of infill and shell thickness. It is deduced from the study that the optimum printing parameters with higher infill density (70%) and combination with triangular infill pattern are able to hold the structure more rigidly, therefore providing more resistance against deformation. This study proposed a platform for determining the mechanical properties of 3D models for FDM printed parts using FEA analysis

Bees algorithm enhanced with Nelder and Mead method for numerical function optimisation

The Bees Algorithm is a population-based optimisation algorithm inspired by the food foraging behaviour of honey bees. Over the years, this algorithm has been successfully applied to many optimisation problems. In order to enhance its accuracy and convergence rate, it is proposed to employ the Nelder and Mead (NM) method to implement the local search phase of the algorithm. The enhanced algorithm uses directional information to direct recruited bees towards better fitness positions within the local search area. The performance of the proposed algorithm was tested on a set of seventeen well-known benchmark functions. Numerical results show that the proposed algorithm generally performs better than the standard Bees Algorithm

Design simulation and development of prototype filling nozzle in food industry

This project necessitates the use of simulation to design the filling nozzles for the food industry. Filling nozzles are used for food packaging production and the speed and efficiency are dependent on the filling nozzle design. The current filling nozzle design is only capable of producing a low volume of production due to the design limitation. Therefore, the new design was proposed to solve this problem by improving several components that can be modified based on the density of the food and the volume of the liquid to be filled. Several designs were proposed and simulated using finite element analysis (FEA) to observe the efficiency of the fluid flow behaviour to imitate the filling process. The same properties of coconut milk utilized in the industry were used for the simulation with A DENSITY OF 1014 kg/m3 and a viscosity of 0.00161Pa.s. All proposed designs were evaluated using the Pugh method and the best design with more outflow channels was proposed which appeared can provide higher flow velocity and a smoother flow at the outlet and eventually lead to higher production

Effects of coating and lubrication on friction and wear for metal-to metal application

Friction and wear between sliding surfaces can lead to various issues in industrial applications, such as increased costs, reduced machine lifespan, loss of functionality, energy loss, and decreased system efficiency. To mitigate these problems, lubricants and coatings are commonly employed. This study aims to investigate the impact of coatings and lubrication on friction coefficient, wear volume loss, and lubricant temperature using the block-on-ring wear test. The effectiveness of different coatings (uncoated, DLC, CrN, and TiALN) and lubricants (anti-friction graphene oxide additive oil and strong nano engine oil additive) in reducing friction and wear is evaluated. The block-on-ring tests are conducted under varying loads (6-60 N), speeds (1450 rpm), lubricant volumes (40 ml), and durations (2-20 min). The coefficient of friction is measured using an inline load cell, wear volume loss is determined by weighing the blocks before and after the experiment, and lubricant temperature is monitored using thermocouples. The results indicate that the coefficient of friction decreases with increasing load, while the lubricant temperature rises. Coated blocks exhibit lower wear volume loss compared to uncoated blocks. Overall, the combination of CrN-coated blocks and anti-friction graphene oxide additive oil demonstrates the best tribological performance

Optimal design of step – cone pulley problem using the bees algorithm

Nowadays, there is a lot of optimization algorithms available to find an optimal solution in engineering problems. Most of these algorithms were developed based on the collective behavior of social swarms of ants, bees, a flock of birds, and schools of fish. It is commonly known as Swarm Intelligence (SI). Examples of algorithm categorized under SI are Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and the Bees Algorithm (BA). The Bees Algorithm is considered one of the recent optimization algorithms and it has been successfully solved various types of problems. It is inspired by the food foraging behavior of honeybees in nature. This study applies the Bees Algorithm to minimize the weight of the stepped-cone pulley in its design and satisfy the constraints. The Bees Algorithm is used in this study to find the optimum solution for stepped-cone pulley design and found better results compared to other optimization algorithms