BackgroundGene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes.MethodsWe propose an enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification. Particle speed, rule, and modified sigmoid function are introduced in this proposed method to increase the probability of the bits in a particle’s position to be zero. The method was empirically applied to a suite of ten well-known benchmark gene expression data sets.ResultsThe performance of the proposed method proved to be superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also requires lower computational time compared to BPSO.

In this paper, a new population-based metaheuristic optimization algorithm, named Simulated Kalman Filter (SKF) is introduced. This new algorithm is inspired by the estimation capability of the Kalman Filter. In principle, state estimation problem is regarded as an optimization problem, and each agent in SKF acts as a Kalman Filter. An agent in the population finds solution to optimization problem using a standard Kalman Filter framework, which includes a simulated measurement process and a best-so-far solution as a reference. To evaluate the performance of the Simulated Kalman Filter algorithm, it is applied to 30 benchmark functions of CEC 2014 for real-parameter single objective optimization problems. Statistical analysis is then carried out to rank SKF results to those obtained by other metaheuristic algorithms. The experimental results show that the proposed SKF algorithm is a promising approach, and has a comparable performance to some well-known metaheuristic algorithms.

Simulated Kalman Filter (SKF) is a population-based optimization algorithm which exploits the estimation capability of Kalman filter to search for a solution in a continuous search space. The SKF algorithm only capable to solve numerical optimization problems which involve continuous search space. Some problems, such as routing and scheduling, involve binary or discrete search space. At present, there are three modifications to the original SKF algorithm in solving combinatorial optimization problems. Those modified algorithms are binary SKF (BSKF), angle modulated SKF (AMSKF), and distance evaluated SKF (DESKF). These three combinatorial SKF algorithms use binary encoding to represent the solution to a combinatorial optimization problem. This paper introduces the latest version of distance evaluated SKF which uses state encoding, instead of binary encoding, to represent the solution to a combinatorial problem. The algorithm proposed in this paper is called state-encoded distance evaluated SKF (SEDESKF) algorithm. Since the original SKF algorithm tends to converge prematurely, the distance is handled differently in this study. To control and exploration and exploitation of the SEDESKF algorithm, the distance is normalized. The performance of the SEDESKF algorithm is compared against the existing combinatorial SKF algorithm based on a set of Traveling Salesman Problem (TSP).

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One of the backbones in electronic manufacturing industry is the printed circuit board (PCB) manufacturing. Due to the human limited resources and speed requirements, manual inspection is ineffective to inspect every printed circuit board. Hence, this paper presents an efficient algorithm for an automated visual PCB inspection system that detects and locates any defect found on PCBs. The detection mechanism utilizes the wavelet-based image difference algorithm. In order to locate the defective areas, the coarse resolution defect localization algorithm is proposed which is applied to the coarse resolution of the differenced image. This algorithm will then map the defective areas found in the coarse differenced image to the fine resolution of the tested image. Based on experimental results, the proposed method has achieved a reduction up to 82.11% of the computation time in comparison to the traditional image difference operation without sacrificing the accuracy of the defect detection. This tremendous amount of saving has been made possible by the use of Haar wavelet transform. Consequently, such amount of reduction will benefit the industries as the automatic inspection for each PCB can now be realized at high speed.