Ismail Ibrahim

A Particle Swarm Optimization Approach to Robotic Drill Route Optimization

Most of the operational time of a PCB Robotic Drill is spent on moving the drill bit between the holes. This operational time can be kept at a minimal level by optimizing the route taken by the robot. An optimized route translates to a minimal cost of operating the robot. This paper proposes a new model that implements Particle Swarm Optimization (PSO) in order to find optimized routing path when using the PCB Robotic Drill. The main task of the PCB Robotic Drill is to drill holes at Printed Circuit Board (PCB). This PCB Robotic Drill will route the drill site by moving the drill bit along Cartesian axes from it’s initial position. Then, the drill bit will return back to the initial position. The drill route consists of a number of potential locations where the holes are going to be drilled. As the number of holes required increases so thus does the complexity to find the optimized route. The proposed model can be used to solve this complex problem with minimal computational time. The result of a case study indicates that the proposed model is capable to find the shortest path for the robot to complete its task. Thus concluded the proposed model can be implemented in any drill route problems.

A Particle Swarm Optimization Approach for Routing in VLSI

The performance of very large scale integration (VLSI) circuits is depends on the interconnected routing in the circuits. In VLSI routing, wire sizing, buffer sizing, and buffer insertion are techniques to improve power dissipation, area usage, noise, crosstalk, and time delay. Without considering buffer insertion, the shortest path in routing is assumed having the minimum delay and better performance. However, the interconnect delay can be further improved if buffers are inserted at proper locations along the routing path. Hence, this paper proposes a heuristic technique to simultaneously find the optimal routing path and buffer location for minimal interconnect delay in VLSI based on particle swarm optimization (PSO). PSO is a robust stochastic optimization technique based on the movement and information sharing of swarms. In this study, location of doglegs is employed to model the particles that represent the routing solutions in VLSI. The proposed approach has a good potential in VLSI routing and can be further extended in futureTo seek for a hyperchaotic attractor with complex topological attractor structure, a new four-dimensional continuous autonomous hyperchaotic system is proposed. Within a wider region of the variation of the control parameter, this system can generate novel hperchaotic and chaotic attractors along with quasi-periodic and periodic orbits. By employing Lyapunov exponent spectrum, bifurcation diagram, Poincaré mapping and phase portrait, etc., the existence of hyperchaotic behaviors of new system is verified and the dynamical routes from period, quasi-period, chaos and hyperchaos are observed. Furthermore, a practical circuit is designed to realize the system, which the experimental results indicate that new four-dimensional hyperchaotic system is a realizable chaotic system with potential values of engineering applications.

A Noise Elimination Procedure for Printed Circuit Board Inspection System

Image difference operation is frequently used in automated printed circuit board (PCB) inspection system as well as in many other image processing applications. During the implementation, this operation brings along the unwanted noise due to misalignment and uneven binarization. Thus, this paper proposes a method to eliminate, if possible, or to reduce as much as possible such noise during the computation of defect detection. This paper used a template PCB image and the tested PCB image as the input. Image subtraction operation will be applied between the images. The results of applying the proposed method showed a significant improvement during the real-time inspection of printed circuit boards.

An Assembly Sequence Planning Approach with a Multi-state Particle Swarm Optimization

Assembly sequence planning (ASP) becomes one of the major challenges in the product design and manufacturing. A good assembly sequence leads in reducing the cost and time of the manufacturing process. However, assembly sequence planning is known as a classical hard combinatorial optimization problem. Assembly sequence planning with more product components becomes more difficult to be solved. In this paper, an approach based on a new variant of Particle Swarm Optimization Algorithm (PSO) called the multi-state of Particle Swarm Optimization (MSPSO) is used to solve the assembly sequence planning problem. As in of Particle Swarm Optimization Algorithm, MSPSO incorporates the swarming behaviour of animals and human social behaviour, the best previous experience of each individual member of swarm, the best previous experience of all other members of swarm, and a rule which makes each assembly component of each individual solution of each individual member is occurred once based on precedence constraints and the best feasible sequence of assembly is then can be determined. To verify the feasibility and performance of the proposed approach, a case study has been performed and comparison has been conducted against other three approaches based on Simulated Annealing (SA), Genetic Algorithm (GA), and Binary Particle Swarm Optimization (BPSO). The experimental results show that the proposed approach has achieved significant improvement.

Synchronous vs Asynchronous Gravitational Search Algorithm

Gravitational search algorithm (GSA) is a new member of swarm intelligence algorithms. It stems from Newtonian law of gravity and mass interaction. Typically the agents in GSA are updated synchronously, where the whole population is updated together after every members performance is evaluated. However, asynchronous update of agent has been used by other optimization algorithms. Therefore the performance of asynchronous GSA (A-GSA) is studied in this work. An agent in A-GSA is updated immediately after its performance evaluation. Hence an agent in A-GSA is updated without using complete and updated information of its entire population. Asynchronous update is more attractive from the perspective of parallelization. The results show that improvement to the straight forward implementation of A-GSA is needed.

Experimental study of hybrid interface cooling system using air ventilation and nanofluid

The hybrid interface cooling system needs to be established to chill the battery compartment of electric car and maintained its ambient temperature inside the compartment between 25°C to 35°C. The air cooling experiment has been conducted to verify the cooling capacity, compressor displacement volume, dehumidifying value and mass flow rate of refrigerant (R-410A). At the same time, liquid cooling system is analysed theoretically by comparing the performance of two types of nanofluid, i.e., CuO + Water and Al2O3 + Water, based on the heat load generated inside the compartment. In order for the result obtained to be valid and reliable, several assumptions are considered during the experimental and theoretical analysis. Results show that the efficiency of the hybrid interface cooling system is improved as compared to the individual cooling system.The hybrid interface cooling system needs to be established to chill the battery compartment of electric car and maintained its ambient temperature inside the compartment between 25°C to 35°C. The air cooling experiment has been conducted to verify the cooling capacity, compressor displacement volume, dehumidifying value and mass flow rate of refrigerant (R-410A). At the same time, liquid cooling system is analysed theoretically by comparing the performance of two types of nanofluid, i.e., CuO + Water and Al2O3 + Water, based on the heat load generated inside the compartment. In order for the result obtained to be valid and reliable, several assumptions are considered during the experimental and theoretical analysis. Results show that the efficiency of the hybrid interface cooling system is improved as compared to the individual cooling system.

Analysis of hybrid interface cooling system using air ventilation and nanofluid

The hybrid interface cooling system needs to be designed for maintaining the electric vehicle’s battery cell temperature at 25°C. The hybrid interface cooling system is a combination of two individual systems, where the primary cooling system (R-134a) and the secondary cooling system (CuO + Water) will be used to absorb the heat generated by the battery cells. The ventilation system is designed using air as the medium to transfer the heat from the batteries to the refrigeration system (R-134a). Research will focus on determining the suitable compressor displacement, the heat exchanger volume and the expansion valve resistance value. The analysis for the secondary cooling system is focused on the cooling coil where low temperature nanofluid is passing through each interval of the battery cells. For analysing purposes, the thermal properties of the mixture of 50 grams, Copper (II) Oxide and the base fluid have been determined. The hybrid interface cooling system are able to achieve 57.82% increments in term of rate of heat transfer as compared to the individual refrigeration system.

Review on application CuO/distilled water & Al2O3/distilled water for enhancement heat transfer characteristics in cooling systems

The brief review is made by considering various thermo-physical properties such as thermal conductivity, density, dynamic viscosity and specific heat which govern the Copper Oxide (CuO) and Aluminum Oxide (Al2O3) nanoparticle based nano-fluids; and its applications in the literatures. This paper also summarize the technique used for preparation of stable nano-fluids. Most effort is taken to account the contributions of various experimental and numerical studies available in literature on two types of (CuO) and (Al2O3) nanoparticle on heat transfer performances and preparation of stable nano-fluids. However, there are many challenges and issues of nano-fluids that need to be addressed by using more experimental applications.

Rule-based multi-state gravitational search algorithm for discrete optimization problem

Gravitational search algorithm swarm (GSA) is a metaheuristic optimization algorithm, which is based on the Newtons law of gravity and the law of motion, has been successfully applied to solve various optimization problems in real-value search space. Later, binary gravitational search algorithm (BGSA) is designed to solve discrete optimization problems. In this study, rule-based multi-state gravitational search algorithm (RBMSGSA) algorithm is proposed to solve discrete combinatorial optimization problems. The algorithm operates based on a simplified mechanism of transition between two states. The algorithm able to produce feasible solution in solving traveling salesman problem (TSP), one of the most intensively studied discrete combinatorial optimization problems. To evaluate the performances of the proposed algorithm and the BGSA, several experiments using six sets of selected benchmarks instances of traveling salesman problem (TSP) are conducted. The experimental results showed the newly introduced approach consistently outperformed the BGSA in all TSP benchmark instances used.

BSKF: Simulated Kalman Filter

Inspired by the estimation capability of Kalman filter, we have recently introduced a novel estimation-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering and measurement process, every agent estimates the global minimum/maximum. Measurement, which is required in Kalman filtering, is mathematically modelled and simulated. Agents communicate among them to update and improve the solution during the search process. However, the SKF is only capable to solve continuous numerical optimization problem. In order to solve combinatorial optimization problems, an extended version of SKF algorithm, which is termed as Binary SKF (BSKF), is proposed. Similar to existing approach, a mapping function is used to enable the SKF algorithm to operate in binary search space. A set of traveling salesman problems are used to evaluate the performance of the proposed BSKF against Binary Gravitational Search Algorithm (BGSA) and Binary Particle Swarm Optimization (BPSO).