Hybridization Of Deterministic And Metaheuristic Approaches In Global Optimization

In solving general global optimization problems, various approaches methods have been developed since 1970’s which can be divided into two classes named deterministic and the probabilistic/metaheuristic approaches. Deterministic approaches provided a theoretical guarantee of locating the -global optimum solution. However, most of the time deterministic approaches required very high cost and time of computational to obtain the global optimum solution. The probabilistic/metaheuristic approaches are methods based on probability, genetic and evolution as its metaheuristic function for the guidance when solving the global optimization problem, and their accuracy of the solution obtained are not guaranteed. However, some time the metaheuristic approaches work very well in selected problems. The main objective of this research is to increase the accuracy of the solution obtained by Metaheuristic approaches by hybridization with some well-developed local deterministic approaches such as Steepest descent method, conjugate gradient methods and quasi-Newton’s methods. In the analysis of the literature, Artificial Bees Colony (ABC) Algorithm has been selected as the metaheuristic approach to be improved its capability and efficiency to solve the global optimization problems. Several enhancements have been done in this research. For derivative free, a new method called Simplexed ABC method hav an obtained a more accurate global optimum solution by using only 10 colony e been introduced. The numerical results show that Simplexed ABC c of bees with 10 cycle each compare to the 10,000 colony of bees with 100 cycles each in original ABC method. The successful of Simplexed ABC method leads this research to develop a mechanism to transform those well-developed gradient based local deterministic optimization approaches into solving global optimization approaches. These enhancements had produced methods called as ABCED Steepest Descent Method, five variants of ABCED Conjugate Gradient Methods and three variants of ABCED Quasi-Newton’s Methods. The numerical results prove that the enhanced ABCED Steepest Descent and two variants of ABCED Quasi-Newton Methods had perfectly solving all the selected benchmark global optimization problems. In another hand, numerical results of ABCED Conjugate Gradient Methods also achieved up to 80.95% of the selected benchmark global optimization been solved successfully. Besides that, the comparison results also indicated that the numerical performance of the new developed methods converges faster than the original ABC algorithm. The results reported are obtained by using standard benchmark test problems and all computation is done by using C++ programming language

New approach on global optimization problems based on meta-heuristic algorithm and quasi-Newton method

This paper presents an innovative approach in finding an optimal solution of multimodal and multivariable function for global optimization problems that involve complex and inefficient second derivatives. Artificial bees colony (ABC) algorithm possessed good exploration search, but the major weakness at its exploitation stage. The proposed algorithms improved the weakness of ABC algorithm by hybridized with the most effective gradient based method which are Davidon-Flecher-Powell (DFP) and Broyden-Flecher-Goldfarb-Shanno (BFGS) algorithms. Its distinguished features include maximizing the employment of possible information related to the objective function obtained at previous iterations. The proposed algorithms have been tested on a large set of benchmark global optimization problems and it has shown a satisfactory computational behaviour and it has succeeded in enhancing the algorithm to obtain the solution for global optimization problems

A new class of Shariah-compliant portfolio optimization model: diversification analysis

This study proposes a novel Shariah-compliant portfolio optimization model tested on the daily historical return of 154 Shariah-compliant securities reported by the Shariah Advisory Council of Securities Commission Malaysia from 2011 to 2020. The mathematical model employs an annual rebalancing strategy subject to a Conditional Value-at-Risk (CVaR) constraint while considering practical and Islamic trading concerns, including transaction costs, holding limits, and zakat payment. To validate the model, the optimal portfolios are compared against an Islamic benchmark index, a market index, and portfolios generated by the mean-variance model, as well as a forecast accuracy test by the Mean Absolute Percentage Error and Mean Absolute Arctangent Percentage Error. Furthermore, this study examines the inter-stock relationship within the generated portfolios using correlation and Granger causality tests to identify the diversification performance. Results show an outperformance of the model in offering portfolios with higher risk-adjusted returns under a comparably short computational time and an indication of generally well-diversified portfolios by the weak correlations between securities. The study further noted that the model is adept at risk management in addition to higher forecast accuracy during financial crises by showing remarkably fewer causal relationships during bear markets in 2011, 2014, and 2020. The findings of an inversed relationship between portfolio risk and the number of causalities between securities offer new insights into the effect of dynamic relationships between securities on portfolio diversification. In conclusion, the proposed model carries higher moral and social values than the conventional models while portraying high potential in enhancing the efficiency of asset allocation, contributing to economic diversification and the scarce literature on Islamic portfolio optimization modelling. The study also supports the substantially increasing demand for Shariah-compliant strategies following globalization and the changing demographic of the real financial world with growing priorities of social and sustainability values

Optimization of Markov Weighted Fuzzy Time Series Forecasting Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)

The Markov Weighted Fuzzy Time Series (MWFTS) is a method for making predictions based on developing a fuzzy time series (FTS) algorithm. The MWTS has overcome certain limitations of FTS, such as repetition of fuzzy logic relationships and weight considerations of fuzzy logic relationships. The main challenge of the MWFTS method is the absence of standardized rules for determining partition intervals. This study compares the MWFTS model to the partition methods Genetic Algorithm-Fuzzy K-Medoids clustering (GA-FKM) and Fuzzy K-Medoids clustering-Particle Swarm Optimization (FKM-PSO) to solve the problem of determining the partition interval and develop an algorithm. Optimal partition optimization. The GA optimization algorithm’s performance on GA-FKM depends on optimizing the clustering of FKM to obtain the most significant partition interval. Implementing the PSO optimization algorithm on FKM-PSO involves maximizing the interval length following the FKM procedure. The proposed method was applied to Anand Vihar, India’s air quality data. The MWFTS method combined with the GA-FKM partitioning method reduced the mean absolute square error (MAPE) from 17.440 to 16.85%. While the results of forecasting using the MWFTS method in conjunction with the FKM-PSO partition method were able to reduce the MAPE percentage from 9.78% to 7.58%, the MAPE percentage was still 9.78%. Initially, the root mean square error (RMSE) score for the GA-FKM partitioning technique was 48,179 to 47,01. After applying the FKM-PSO method, the initial RMSE score of 30,638 was reduced to 24,863. Doi: 10.28991/ESJ-2022-06-06-010 Full Text: PD

Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering

The fuzzy clustering algorithm is a partition method that assigns objects from a data set to a cluster by marking the average location. Furthermore, Fuzzy Subtractive Clustering (FSC) with hamming distance and exponential membership function is used to analyze the cluster center of a data point. The data point with the highest density will be the cluster's center. Therefore, this research aims to determine the number of collections with the best quality by comparing the Partition Coefficient (PC) values for each number produced. The data set, which is heart failure patient data, is 150 data obtained from UCI Machine Learning. The data consists of 11 variables, including age

Over-the-Counter Medicine Attitudes and Knowledge among University and College Students in Brunei Darussalam:Findings from the First National Survey

Over-the-counter (OTC) medicine is defined as safe and effective for the general public to use, without seeking therapy from a health professional. As primary social media and internet users, university and college students are more likely to be exposed to unverified sources of health information. This study aims to assess the knowledge, attitudes, and behaviour of students at institutions of higher learning in Brunei with regard to the safe use of OTC medicines. A cross-sectional study was performed using a self-administered online questionnaire, adapted from the literature with additional information from the United States Food and Drug Administration (FDA) on the educational resources in understanding OTC medicine for consumers. The questionnaire consisted of 4 sections: demographic information, knowledge of OTC medicines, attitudes, and practice. Descriptive and inferential statistics were used for data analysis. A total of 335 students returned a completed questionnaire. The students had a mean knowledge score of 7.1 out of 9, with more than half (53.4%) having good knowledge (knowledge score > 7), and only a small percentage (5.7%) with poor knowledge. Almost all of the students (99.7%) had a positive attitude toward OTC medicine use. Few of the students practiced improper habits in terms of OTC medicine use, such as not reading the instructions or taking more than the recommended dose. Awareness of proper OTC medicine use among students in institutions of higher learning is necessary to prevent the rise of inappropriate user practices

Economic Burdens of Uncomplicated Malaria in Primary Health Care (PHC) Facilities of Plateau State, Nigeria: Patients' Perspectives

Objectives: This study aims at evaluating the costs incurred by patients in Primary Healthcare facilities of Plateau State, Nigeria, due to uncomplicated malaria management. Methods: Patients’ information on resources used and absence from the labour market due to uncomplicated malaria illness were collected using the self-reported cost of illness instruments across 24 selected Primary Health Care (PHC) facilities in Plateau State. The collated data were used to estimate the direct medical and non-medical costs incurred by patients through the summation of the various costs paid out of pocket for the services; while the indirect cost was estimated using the human capital theory. All analyses were conducted through Microsoft Excel and IBM Statistical Package for Social Sciences (SPSS®) version 23 software. Results: The average direct cost per episode of uncomplicated malaria was estimated at NGN 2808.37/USD 7.39, while the indirect average money equivalence of the time lost due to the ailment was estimated at NGN 2717/USD 7.55, giving an average cost of treating uncomplicated malaria borne by patients in Plateau State per episode to be NGN 5525.37/USD 14.94. The projected annual cost of the disease was NGN 9, 921,671,307.22 (USD 27, 560,198.08). Conclusions: The study showed substantial financial costs borne by patients due to uncomplicated malaria in Plateau State, comprising 50.83% of direct cost and 49.17% of the indirect cost of medications

Decentralized blockchain network for resisting side-channel attacks in mobility-based IoT

The inclusion of mobility-based Internet-of-Things (IoT) devices accelerates the data transmission process, thereby catering to IoT users’ demands; however, securing the data transmission in mobility-based IoT is one complex and challenging concern. The adoption of unified security architecture has been identified to prevent side-channel attacks in the IoT, which has been discussed extensively in developing security solutions. Despite blockchain’s apparent superiority in withstanding a wide range of security threats, a careful examination of the relevant literature reveals that some common pitfalls are associated with these methods. Therefore, the proposed scheme introduces a novel computational security framework wherein a branched and decentralized blockchain network is formulated to facilitate coverage from different variants of side-channel IoT attacks that are yet to be adequately reported. A unique blockchain-based authentication approach is designed to secure communication among mobile IoT devices using multiple stages of security implementation with Smart Agreement and physically unclonable functions. Analytical modeling with lightweight finite field encryption is used to create this framework in Python. The study’s benchmark results show that the proposed scheme offers 4% less processing time, 5% less computational overhead, 1% more throughput, 12% less latency, and 30% less energy consumption compared to existing blockchain methods