Aderemi Adewumi | University of KwaZulu-Natal (original) (raw)

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Papers by Aderemi Adewumi

— This paper presents an integrated dynamic pricing scheme (iDPS) developed for an off grid commu... more — This paper presents an integrated dynamic pricing scheme (iDPS) developed for an off grid community in sub-Sahara Africa (SSA). This proposed model employs a neighbourhood approach in effectively determining the electricity units due to connected consumers based on their monthly contributions. Assuming base values, this model obviates the need for real time inputs from the users owing to the high illiteracy level in SSA and dynamically computes electricity price in real time such that below par paying consumers pay more compared to base or above base consumers. Additionally, the smart load distribution board employed ensures that electricity units are dispatched in quantized values demanding greater energy efficiency from the consumers. This model achieves economic accrual which guarantees the sustainability of the off grid DG project.

Scientific research and essays

Type 2 diabetes has been an increasing public health problem with an estimated forecast of 300 mi... more Type 2 diabetes has been an increasing public health problem with an estimated forecast of 300 million around the world by the year 2025. It places a serious constraint on individual's activities caused by hyperglycemia resulting from defects in insulin secretion, insulin action or both. Although extensive epidemiological researches have shown an association between various risk factors and the development of type 2 diabetes, there has been no research on the measurement or determination of the relative severity of these risk factors regarding their contributions to the incidence and prevalence of type 2 diabetes. In this research, 13 risk factors associated with type 2 diabetes were identified from epidemiological studies. The degree of severity of these risk factors was ascertained by professionals using structured Liket format with 6 choices. The data obtained were used in ranking the risk factors, which assisted in selecting the most contributing risk factors to the development of type 2 diabetes. The result revealed that heredity contributes as high as 0.5388; obesity contributes 0.1038; physical inactivity contributes 0.0230; dietary contributes 0.0230; age contributes 0.1038; IGT contributes 0.1038; and gestational diabetes is 0.1038. This result could serve as input to neural network model.

IMA Journal of Applied Mathematics

paper solves the dynamic traveling salesman problem (DTSP) using dynamic Gaussian Process Regress... more paper solves the dynamic traveling salesman problem (DTSP) using dynamic Gaussian Process Regression (DGPR) method. The problem of varying correlation tour is alleviated by the nonstationary covariance function interleaved with DGPR to generate a predictive distribution for DTSP tour. This approach is conjoined with Nearest Neighbor (NN) method and the iterated local search to track dynamic optima. Experimental results were obtained on DTSP instances. The comparisons were performed with Genetic Algorithm and Simulated Annealing. The proposed approach demonstrates superiority in finding good traveling salesman problem (TSP) tour and less computational time in nonstationary conditions.

TheScientificWorldJournal, 2014

Hunger component is introduced to the existing cockroach swarm optimization (CSO) algorithm to im... more Hunger component is introduced to the existing cockroach swarm optimization (CSO) algorithm to improve its searching ability and population diversity. The original CSO was modelled with three components: chase-swarming, dispersion, and ruthless; additional hunger component which is modelled using partial differential equation (PDE) method is included in this paper. An improved cockroach swarm optimization (ICSO) is proposed in this paper. The performance of the proposed algorithm is tested on well known benchmarks and compared with the existing CSO, modified cockroach swarm optimization (MCSO), roach infestation optimization RIO, and hungry roach infestation optimization (HRIO). The comparison results show clearly that the proposed algorithm outperforms the existing algorithms.

Journal of Applied Mathematics, 2015

ABSTRACT We would like to express our profound gratitude to all the reviewers whose professional ... more ABSTRACT We would like to express our profound gratitude to all the reviewers whose professional expertise contributed immensely to the outcome of this special issue.

2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, 2014

Stock price prediction is an important topic in finance and economics which has spurred the inter... more Stock price prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The autoregressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model. Published stock data obtained from New York Stock Exchange (NYSE) and Nigeria Stock Exchange (NSE) are used with stock price predictive model developed. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction and can compete favourably with existing techniques for stock price prediction.

The Scientific World Journal, 2014

A new local search technique is proposed and used to improve the performance of particle swarm op... more A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants.

GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion, 2013

In this paper, a real-coded genetic algorithm (RCGA) which incorporates an exploratory search mec... more In this paper, a real-coded genetic algorithm (RCGA) which incorporates an exploratory search mechanism based on vector projection termed projection-based RCGA (PRCGA) is benchmarked on the noisefree BBOB 2013 testbed. It is an enhanced version of RCGA-P in . The projection operator incorporated in PRCGA shows promising exploratory search capability in some problem landscape. PRCGA is equipped with a multiple independent restart mechanism and a stagnation alleviation mechanism. The maximum number of function evaluations (#F Es) for each test run is set to 10 5 times the problem dimension. PRCGA shows encouraging results on several problems in the low and moderate search dimensions. It is able to solve each type of problem with the dimension up to 40 with lower precision but not all the functions to the desired level of accuracy of 10 −8 especially for high conditioning and multi-modal functions within the specified maximum #F Es.

2014 IEEE International Advance Computing Conference (IACC), 2014

ABSTRACT This paper introduces simple Euler method to the existing roach infestation optimization... more ABSTRACT This paper introduces simple Euler method to the existing roach infestation optimization algorithm to improve swarm stability and enhance local and global search performance. A dynamic step size adaptation roach infestation optimization (DSARIO) algorithm is proposed using the Euler step size adaptation. Experimental results obtained from the proposed algorithm demonstrated improved accuracy and convergence ability over existing roach infestation optimization algorithm. Also the numerical results with the proposed algorithm show clearly its ability to solve multi-dimensional problems. The performance of the proposed algorithm is compared with that of existing roach infestation optimization and hungry roach infestation optimization algorithms.

Mathematical Problems in Engineering, 2014

The effect of stochastic constriction on cockroach swarm optimization (CSO) algorithm performance... more The effect of stochastic constriction on cockroach swarm optimization (CSO) algorithm performance was examined in this paper. A stochastic constriction cockroach swarm optimization (SCCSO) algorithm is proposed. A stochastic constriction factor is introduced into CSO algorithm for swarm stability enhancement; control cockroach movement from one position to another while searching for solution to avoid explosion; enhanced local and global searching capabilities. SCCSO performance was tested through simulation studies and its performance on multidimensional functions is compared with that of original CSO, modified cockroach swarm optimization (MCSO), and one of the well-known global optimization techniques in the literature known as line search restart techniques (LSRS). Standard benchmarks that have been widely used for global optimization problems are considered for evaluating the proposed algorithm. The selected benchmarks were solved up to 3000 dimensions by the proposed algorithm.

Journal of Applied Mathematics, 2014

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishin... more Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.

Mathematical and Computer Modelling, 2010

We considered the case of a multi-stage hostel space allocation problem based on data set obtaine... more We considered the case of a multi-stage hostel space allocation problem based on data set obtained from a tertiary institution. Genetic Algorithm was applied at different levels of the allocation. We studied the effects of parameter change on solutions obtained. The rate of feasibility of the solutions was also determined.

Applied Mathematics and Computation, Feb 1, 2014

"Genetic algorithms are efficient global optimizers, but they are weak in performing fine grained... more "Genetic algorithms are efficient global optimizers, but they are weak in performing fine grained local searches. In this paper, the local search capability of genetic algorithm is improved by hybridizing real coded genetic algorithm with ‘uniform random’ local search to form a hybrid real coded genetic algorithm termed ‘RCGAu’. The incorporated local technique is applied to all newly created offspring so that each offspring solution is given the opportunity to effectively search its local neighborhood for the best local optimum. Numerical experiments show that the performance of RCGA is remarkably improved by the uniform random local search technique."

Computational and mathematical methods in medicine, 2015

This paper reports the performance of particle swarm optimization (PSO) for the assignment of blo... more This paper reports the performance of particle swarm optimization (PSO) for the assignment of blood to meet patients' blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessitate the development of mathematical models and techniques for effective handling of blood distribution among available blood types in order to minimize wastages and importation from external sources. This gives rise to the blood assignment problem (BAP) introduced recently in literature. We propose a queue and multiple knapsack models with PSO-based solution to address this challenge. Simulation is based on sets of randomly generated data that mimic real-world population distribution of blo...