Adam Baharum

Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing.

This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, “MOPSOSA”. The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets.

Reliability Measurement for Mixed Mode Failures of 33/11 Kilovolt Electric Power Distribution Stations

The reliability of the electrical distribution system is a contemporary research field due to diverse applications of electricity in everyday life and diverse industries. However a few research papers exist in literature. This paper proposes a methodology for assessing the reliability of 33/11 Kilovolt high-power stations based on average time between failures. The objective of this paper is to find the optimal fit for the failure data via time between failures. We determine the parameter estimation for all components of the station. We also estimate the reliability value of each component and the reliability value of the system as a whole. The best fitting distribution for the time between failures is a three parameter Dagum distribution with a scale parameter and shape parameters and . Our analysis reveals that the reliability value decreased by 38.2% in each 30 days. We believe that the current paper is the first to address this issue and its analysis. Thus, the results obtained in this research reflect its originality. We also suggest the practicality of using these results for power systems for both the maintenance of power systems models and preventive maintenance models.

Portfolio Resampling in Malaysian Equity Market

Since the work of Markowitz (1959) and Sharpe (1964), Mean-Variance (MV) analysis has been a central focus of financial economics. Problems involving quadratic objective functions generally incorporate a MV analysis. However, estimation error is known to have huge impact on MV optimized portfolios, which is one of the primary reasons to make standard Markowitz optimization unfeasible in practice. In these studies we focus on a relatively new approach introduced by Michaud (1998), resampled efficiency. Michaud argues that the limitations of MV efficiency in practice generally derive from a lack of statistical understanding of MV optimization. He advocates a statistical view of MV optimization that leads to new procedures that can reduce estimation error. Optimal portfolio based on MV efficiency and resampled efficiency is compared in an empirical out-of sample study in term of their performances using Malaysian stock market. We divided the data to three groups, daily, weekly and monthly. We found that, resampled efficiency performed well and group of daily and weekly data have the least estimation error.

Good solution for multi-objective optimization problem

Multi-objective optimization problems have been solved widely by determination of a Pareto optimal set. Practically, the decision-makers need to choose only one solution to implement on their system, which is a challenge for them especially when the number of solutions in the Pareto set is large. In this paper, new method has been proposed to get a good solution for multi-objective optimization problem. The method consists of two stages; the first stage used the Multi Objective Simulated Annealing algorithm to find the Pareto set that contains the non-dominated solutions, whereas the second stage used the optimal computing allocation technique to reduce the number of solutions in the Pareto set to one solution that depends on ranking the preferences of the objective functions. To validate this method, multi-objective 0\1 knapsack problem was analyzed.

A Case Study of Reliability and Performance of the Electric Power Distribution Station Based on Time between Failures

This paper presents an algorithm for estimating the performance of high-power station systems connected in series, parallel, and mixed series-parallel with collective factor failures caused by any part of the system equipment. Failures that occur frequently can induce a selective effect, which means that the failures generated from different equipment parts can cause failures in various subsets of the system elements. The objectives of this study are to increase the lifetime of the station and reduce sudden station failures. The case study data was collected from an electricity distribution company in Baghdad, Iraq. Data analysis was performed using the most valid distribution of the Weibull distribution with scale parameter α = 1.3137 and shape parameter β = 94.618. Our analysis revealed that the reliability value decreased by 2.82% in 30 days. The highest critical value was obtained for components T1, CBF5, CBF7, CBF8, CBF9, and CBF10 and must be changed by a new item as soon as possible. We believe that the results of this research can be used for the maintenance of power systems models and preventive maintenance models for power systems.

Reliability and failure analysis for high power station based on operation time

Many human activities are electricity-dependent. As major providers of electricity, the performance of high-power stations represents a vital part of any national economy. This paper introduces a reliability analysis of the High Power Station system connected (series, parallel, and mixed). This station transforms electricity from 33000 KV to 11000 KV. The operational time, time between failures, (TBF) has an exponential distribution. The present study calculates the reliability of each component of the station. Finally, we calculated the station reliability as a whole.

Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits

F. Mora, C.A. Scapim, A. Baharum, and A.T. Amaral-Junior. 2010. Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits. Cien. Inv. Agr. 37(3):83-89. In genetic studies, most Quantitative Trait Loci (QTL) mapping methods presuppose that the continuous trait of interest follows a normal (Gaussian) distribution. However, many economically important traits of agricultural crops have a non-normal distribution. Composite interval mapping (CIM) has been successfully applied to the detection of QTL in animal and plant breeding. In this study we report a generalized CIM (GCIM) method that permits QTL analysis of non-normally distributed variables. GCIM was based on the classic Generalized Linear Model method. We applied the GCIM method to a F 2 population with co-dominant molecular markers and the existence of a QTL controlling a trait with Gamma distribution. Computer simulations indicated that the GCIM method has superior performance in its ability to map QTL, compared with CIM. QTL position differed by 5 cM and was located at different marker intervals. The Likelihood Ratio Test values ranged from 52 (GCIM) to 76 (CIM). Thus, wrongly assuming CIM may overestimate the effect of the QTL by about 47%. The usage of GCIM methodology can offer improved efficiency in the analysis of QTLs controlling continuous traits of non-Gaussian distribution.

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. ...

Cooling schedule for multi-objective simulated annealing algorithm

In this paper, the cooling schedule set up for Multi-Objective Simulated Annealing algorithm (MOSA) is studied. The MOSA algorithm is used to solve multi objective optimization problem by finding the Pareto set of solutions. To apply the MOSA algorithm, a cooling schedule must be determined, which has two main components; the initial temperature, and the rate at which the temperature is decrement. These two components affect the performance of the algorithm. We study the effect of the initial temperature on the MOSA algorithm, and for each value of initial temperature, several temperature decrements are performed. During the algorithm’s process, the number of iterations is kept fixed. The 0\1 multi objective knapsack problem is used to illustrate the impact of the initial and decrement temperatures on finding the Pareto set.

Modelling the breeding of Aedes Albopictus species in an urban area in Pulau Pinang using polynomial regression

Polynomial regression is used to model a curvilinear relationship between a response variable and one or more predictor variables. It is a form of a least squares linear regression model that predicts a single response variable by decomposing the predictor variables into an nth order polynomial. In a curvilinear relationship, each curve has a number of extreme points equal to the highest order term in the polynomial. A quadratic model will have either a single maximum or minimum, whereas a cubic model has both a relative maximum and a minimum. This study used quadratic modeling techniques to analyze the effects of environmental factors: temperature, relative humidity, and rainfall distribution on the breeding of Aedes albopictus, a type of Aedes mosquito. Data were collected at an urban area in south-west Penang from September 2010 until January 2011. The results indicated that the breeding of Aedes albopictus in the urban area is influenced by all three environmental characteristics. The number of mosqui...