Kamal Khalil

Bio: Kamal Khalil is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Multi-swarm optimization & Routing (electronic design automation). The author has an hindex of 8, co-authored 25 publications receiving 166 citations.

A Fast Discrete Gravitational Search Algorithm

Abstract: This study introduces a variant of Gravitational Search Algorithm (GSA) for discrete optimization problems, namely, Fast Discrete Gravitational Search Algorithm (FDGSA). The main difference between the proposed FDGSA and the existing Binary Gravitational Search Algorithm (BGSA) is that an agent's position is updated based on its direction and velocity. Both the direction and velocity determine the candidates of integer values for the position update of an agent and then the selection is done randomly. Unimodal test functions, such as De Jong's function, Scwefel's function and Rosenbrock's valley are used to evaluate the performance of the proposed FDGSA. Comparison with BGSA is done to benchmark the proposed FDGSA in terms of speed of convergence and quality of solution. The experimental result shows that the proposed FDGSA converges faster compared to the BGSA.

Predicting Material Removal Rate of Electrical Discharge Machining (EDM) using Artificial Neural Network for High I gap current

Abstract: This article presents a prediction of Material Removal Rate (MRR) in Electrical Discharge Machining (EDM) using Artificial Neural Network (ANN). Experimental data were gathered from Die sinking EDM process for copper-electrode and steel-workpiece. It is aimed to develop a behavioral model using input-output pattern of raw data from EDM process experiment. The behavioral model is used to predict MRR and than the predicted MRR is compared to actual MRR value. The results show good agreement of predicting MRR between them.

A Novel Multi-state Particle Swarm Optimization for Discrete Combinatorial Optimization Problems

Abstract: Particle swarm optimization (PSO) has been widely used to solve real-valued optimization problems. A variant of PSO, namely, binary particle swarm optimization (BinPSO) has been previously developed to solve discrete optimization problems. Later, many studies have been done to improve BinPSO in term of convergence speed, stagnation in local optimum, and complexity. In this paper, a novel multi-state particle swarm optimization (MSPSO) is proposed to solve discrete optimization problems. Instead of evolving a high dimensional bit vector as in BinPSO, the proposed MSPSO mechanism evolves states of variables involved. The MSPSO algorithm has been applied to two benchmark instances of traveling salesman problem (TSP). The experimental results show that the the proposed MSPSO algorithm consistently outperforms the BinPSO in solving the discrete combinatorial optimization problem.

Anant colony systemfor routing in PCB holes drilling process

Abstract: Most electronic manufacturing industries use computer numerical controlled (CNC) machines for drilling holes on printed circuit board (PCB). Some machines do not choose the optimal route when completing their tasks. Hence, this paper proposes an approach, which is based on ant colony system (ACS), for finding the optimal route in PCB holes drilling process. In ACS, an artificial ant starts to move from a random hole location and moves to the next hole location, based on the pheromone level between the locations of two holes. The higher the pheromones level, the higher the chance for the artificial ant to choose that path. At the same time, that ant deposits its pheromone on the path chosen. This process is repeated until the artificial ant builds a solution, which is evaluated with other artificial ants’ solutions. The best artificial ant deposits additional pheromone to its path. The best-found path is updated as the iteration continues. Experimental result indicates that the proposed ACS-based approach is capable to efficiently find the optimal route for PCB holes drilling process.

A printed circuit board inspection system with defect classification capability

Abstract: An automated visual printed circuit board (PCB) inspection is an approach used to counter difficulties occurred in human’s manual inspection that can elimin ates subjective aspects and then provides fast, quantitative, and dimens ional assessments. In this study, referentia l approach has been implemented on template and defective PCB images to detectnumerous defects on bare PCBs before etching process, since etching usually contributes most destructive defects found on PCBs. The PCB inspection system is then improved by incorporating a geometrical image registration, minimum thresholding technique and median filtering in order to solve alignment and uneven illumination problem. Finally, defect classification operation is employed in order to identify the source for six types of defects namely, missing hole, pin hole, underetch, short -circuit, mousebite, and open- circuit.

Comparison Studies of Electrical Discharge Machining (EDM) Process Model for Low Gap Current

Abstract: This paper aims to compare the material removal rate, ν between a Dimensional Analysis (DA) model, an Artificial Neural Network (ANN) model and an experimental result for a low gap current of an Electrical Discharge Machining (EDM) process. The data analysis is based on a copper electrode and steel workpiece materials. The DA and ANN model that have been developed and reported earlier by authors are used to compare the material removal of EDM process. The result indicated that the ANN model provides better accuracy towards the experimental results.

A comprehensive survey on gravitational search algorithm

Abstract: Gravitational Search Algorithm (GSA) is an optimization method inspired by the theory of Newtonian gravity in physics. Till now, many variants of GSA have been introduced, most of them are motivated by gravity-related theories such as relativity and astronomy. On the one hand, to solve different kinds of optimization problems, modified versions of GSA have been presented such as continuous (real), binary, discrete, multimodal, constraint, single-objective, and multi-objective GSA. On the other hand, to tackle the difficulties in real-world problems, the efficiency of GSA has been improved using specialized operators, hybridization, local search, and designing the self-adaptive algorithms. Researchers have utilized GSA to solve various engineering optimization problems in diverse fields of applications ranging from electrical engineering to bioinformatics. Here, we discussed a comprehensive investigation of GSA and a brief review of GSA developments in solving different engineering problems to build up a global picture and to open the mind to explore possible applications. We also made a number of suggestions that can be undertaken to help move the area forward.

Crisscross optimization algorithm and its application

Abstract: How to improve the global search ability without significantly impairing the convergence speed is still a big challenge for most of the meta-heuristic optimization algorithms. In this paper, a concept for the optimization of continuous nonlinear functions applying crisscross optimization algorithm is introduced. The crisscross optimization algorithm is a new search algorithm inspired by Confucian doctrine of gold mean and the crossover operation in genetic algorithm, which has distinct advantages in solution accuracy as well as convergence rate compared to other complex optimization algorithms. The procedures and related concepts of the proposed algorithm are presented. On this basis, we discuss the behavior of the main search operators such as horizontal crossover and vertical crossover. It is just because of the perfect combination of both, leading to a magical effect on improving the convergence speed and solution accuracy when addressing complex optimization problems. Twelve benchmark functions, including unimodal, multimodal, shifted and rotated functions, are used to test the feasibility and efficiency of the proposed algorithm. The experimental results show that the crisscross optimization algorithm has an excellent performance on most of the test functions, compared to other heuristic algorithms. At the end, the crisscross optimization algorithm is successfully applied to the optimization of a large-scale economic dispatch problem in electric power system. It is concluded that the crisscross optimization algorithm is not only robust in solving continuous nonlinear functions, but also suitable for addressing the complex real-world engineering optimization problems.

An aggregative learning gravitational search algorithm with self-adaptive gravitational constants

Abstract: The gravitational search algorithm (GSA) is a meta-heuristic algorithm based on the theory of Newtonian gravity. This algorithm uses the gravitational forces among individuals to move their positions in order to find a solution to optimization problems. Many studies indicate that the GSA is an effective algorithm, but in some cases, it still suffers from low search performance and premature convergence. To alleviate these issues of the GSA, an aggregative learning GSA called the ALGSA is proposed with a self-adaptive gravitational constant in which each individual possesses its own gravitational constant to improve the search performance. The proposed algorithm is compared with some existing variants of the GSA on the IEEE CEC2017 benchmark test functions to validate its search performance. Moreover, the ALGSA is also tested on neural network optimization to further verify its effectiveness. Finally, the time complexity of the ALGSA is analyzed to clarify its search performance.

CS-PSO: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems

Abstract: Combinatorial optimization problems are typically NP-hard, due to their intrinsic complexity. In this paper, we propose a novel chaotic particle swarm optimization algorithm (CS-PSO), which combines the chaos search method with the particle swarm optimization algorithm (PSO) for solving combinatorial optimization problems. In particular, in the initialization phase, the priori knowledge of the combination optimization problem is used to optimize the initial particles. According to the properties of the combination optimization problem, suitable classification algorithms are implemented to group similar items into categories, thus reducing the number of combinations. This enables a more efficient enumeration of all combination schemes and optimize the overall approach. On the other hand, in the chaos perturbing phase, a brand-new set of rules is presented to perturb the velocities and positions of particles to satisfy the ideal global search capability and adaptability, effectively avoiding the premature convergence problem found frequently in traditional PSO algorithm. In the above two stages, we control the number of selected items in each category to ensure the diversity of the final combination scheme. The fitness function of CS-PSO introduces the concept of the personalized constraints and general constrains to get a personalized interface, which is used to solve a personalized combination optimization problem. As part of our evaluation, we define a personalized dietary recommendation system, called Friend, where CS-PSO is applied to address a healthy diet combination optimization problem. Based on Friend, we implemented a series of experiments to test the performance of CS-PSO. The experimental results show that, compared with the typical HLR-PSO, CS-PSO can recommend dietary schemes more efficiently, while obtaining the global optimum with fewer iterations, and have the better global ergodicity.