Zulkifli Md. Yusof

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.

Fuzzy logic controller design for intelligent drilling system

An intelligent drilling system can be commercially very profitable in terms of reduction in crude material and labor involvement. The use of fuzzy logic based controller in the intelligent cutting and drilling operations has become a popular practice in the ever growing manufacturing industry. In this paper, a fuzzy logic controller has been designed to select the cutting parameter more precisely for the drilling operation. Specifically, different input criterion of machining parameters are considered such as the tool and material hardness, the diameter of drilling hole and the flow rate of cutting fluid. Unlike the existing fuzzy logic based methods, which use only two input parameters, the proposed system utilizes more input parameters to provide spindle speed and feed rate information more precisely for the intelligent drilling operation.

Single-Solution Simulated Kalman Filter Algorithm for Routing in Printed Circuit Board Drilling Process

Optimal drilling path for printed circuit board is crucial in increasing productivity and reduce production costs. Single-solution Simulated Kalman Filter (ssSKF) is a new optimizer inspired by the Kalman filtering process. It uses only a single agent to solve optimization process by finding the estimate of the optimal solution. Principally, ssSKF algorithm uses the standard Kalman filter framework, aided by a local neighborhood technique during its prediction step. This paper reveals the potential of ssSKF as a good routing method in printed circuit board (PCB) drilling process. Experimental results indicate that the ssSKF algorithm outperforms the existing methods in searching a good route to speed up a PCB drilling process.

Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks

DNA microarray technology plays an important role in advancing the analysis of gene expression and gene functions. However, gene expression data often contain missing values, which cause problems as most of the analysis methods of gene expression data require a complete matrix. Several missing value imputation methods have been developed to overcome the problems. In this paper, effects of the missing value imputation methods in modeling of gene regulatory network are investigated. Three missing value imputation methods are used, which are k-Nearest Neighbor (kNN), Iterated Local Least Squares (ILLsimpute), and Fixed Rank Approximation Algorithm (FRAA). Dataset used in this paper is E. coli. The results suggest that the performance of each missing value imputation method is influenced by the percentage and distribution of the missing values in the dataset, which subsequently affect the modeling of gene regulatory network using Dynamic Bayesian network.

Exploring the High Performance Computing-Enablement of a Suite of Gene-Knockout Based Genetic Engineering Applications

Genetic engineering provides methods to modify the genes of microorganisms to achieve desired effects. This can be done for improved organism growth rate or increasing production yield of a desired gene product. Gene knockout is a technique that can improve the specific characteristics of microorganisms by disabling selected sets of genes. However, microorganisms are complex and predicting the effects of gene modification is difficult. Several algorithms have been proposed to support a range of gene knockout strategies, including BAFBA, BHFBA and DBFBA. In this paper, scaling these algorithms and methods to utilise High Performance Computing (HPC) resources have been explored. The applications have been parallelized on HPC and the scalability and performance of these approaches were explored and documented.

A Hybrid of Harmony Search and Minimization of Metabolic Adjustment for Optimization of Succinic Acid Production

Succinic acid has been favored by researchers due to its industrial multi-uses. However, the production of succinic acid is far below cell theoretical maximum. The goal of this research is to identify the optimal set of gene knockouts for obtaining high production of succinic acid in microorganisms. Gene knockout is a widely used genetic engineering technique. Hence, a hybrid of Harmony Search (HS) and Minimization of Metabolic Adjustment (MOMA) is proposed. The dataset applied is a core Escherichia coli metabolic network model. Harmony Search is a meta-heuristic algorithm inspired by musicians’ improvisation process. Minimization of Metabolic Adjustment is used to calculate fitness closest to the wild-type, after mutant gene knockout. The result obtained from the proposed hybrid technique are knockout genes list and production rate after the deletion. This proposed technique is possible to be applied in wet laboratory experiment to increase the production of succinic acid in E. coli.

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.

A Multi-state Gravitational Search Algorithm for Combinatorial Optimization Problems

The binary-based algorithms including the binary gravitational search algorithm (BGSA) were designed to solve discrete optimization problems. Many improvements of the binary-based algorithms have been reported. In this paper, a variant of GSA called multi-state gravitational search algorithm (MSGSA) for discrete optimization problems is proposed. The MSGSA concept is based on a simplified mechanism of transition between two states. The performance of the MSGSA is empirically compared to the original BGSA based on six sets of selected benchmarks instances of traveling salesman problem (TSP). The experimental results show the effectiveness of the newly introduced approach, regarding its ability to consistently outperform the binary-based algorithm in solving the discrete optimization problems.

An Improved Multi-state Particle Swarm Optimization for Discrete Optimization Problems

Particle swarm optimization (PSO) has been successfully applied to solve various optimization problems. Recently, a state-based algorithm called multi-state particle swarm optimization (MSPSO) has been proposed to solve discrete combinatorial optimization problems. The algorithm operates based on a simplified mechanism of transition between two states. However, the MSPSO algorithm has to deal with the production of infeasible solutions and hence, additional step to convert the infeasible solution to feasible solution is required. In this paper, the MSPSO is improved by introducing a strategy that directly produces feasible solutions. The performance of the improved multi-state particle swarm optimization (IMSPSO) is empirically evaluated based on a set of travelling salesman problems (TSPs). The experimental results showed the newly introduced approach is promising and consistently outperformed the binary PSO algorithm.