Mohd Ismail Abd Aziz

Enhanced compact artificial bee colony

Challenges in many real-world optimization problems arise from limited hardware availability, particularly when the optimization must be performed on a device whose hardware is highly restricted due to cost or space. This paper proposes a new algorithm, namely Enhanced compact Artificial Bee Colony (EcABC) to address this class of optimization problems. The algorithm benefits from the search logic of the Artificial Bee Colony (ABC) algorithm, and similar to other compact algorithms, it does not store the actual population of tentative solutions. Instead, EcABC employs a novel probabilistic representation of the population that is introduced in this paper. The proposed algorithm has been tested on a set of benchmark functions from the CEC2013 benchmark suite, and compared against a number of algorithms including modern compact algorithms, recent population-based ABC variants and some advanced meta-heuristics. Numerical results demonstrate that EcABC significantly outperforms other state of the art compact algorithms. In addition, simulations also indicate that the proposed algorithm shows a comparative performance when compared against its population-based versions.

A self-adaptive binary differential evolution algorithm for large scale binary optimization problems

A new binary variant of the DE algorithm is presented.A new approach to design search strategies for the binary DE algorithms is suggested.The proposed algorithm is implemented and tested on modern benchmark problems and high dimensional knapsack problems.The performance of the proposed algorithm is compared against some recently presented binary algorithms. This study proposes a new self-adaptive binary variant of a differential evolution algorithm, based on measure of dissimilarity and named SabDE. It uses an adaptive mechanism for selecting how new trial solutions are generated, and a chaotic process for adapting parameter values. SabDE is compared against a number of existing state of the art algorithms, on a set of benchmark problems including high dimensional knapsack problems with up to 10,000 dimensions as well as on the 15 learning based problems of the Congress on Evolutionary Computation (CEC 2015). Experimental results reveal that the proposed algorithm performs competitively and in some cases is superior to the existing algorithms.

Single-Row Transformation of Complete Graphs

A complete graph is a fully-connected graph where every node is adjacent to all other nodes in the graph. Very often, many applications in science and engineering are reducible to this type of graph. Hence, a simplified form of a complete graph contributes in providing the solutions to these problems. In this paper, we present a technique for transforming a complete graph into a single-row routing problem. Single-row routing is a classical technique in the VLSI design that is known to be NP-complete. We solved this problem earlier using a method called ESSR, and, the same technique is applied to the present work to transform a complete graph into its single-row routing representation. A parallel computing model is proposed which contributes in making the problem modular and scalable. We also discuss the application of this work on the channel assignment problem in the wireless cellular telephone networks.

Malaysia: Becoming an Education Hub to Serve National Development

During the past 15 years, Singapore has been decidedly committed to transitioning from the Global Schoolhouse project to a twenty-first-century knowledge hub. This chapter describes key initiatives introduced to remodel the city-state into a global knowledge and education hub and a site for the continuing accumulation of capital, talent, and knowledge. Notwithstanding its clarity of vision and strategy, there are a number of practical complexities confronting Singapore’s plans to leapfrog into the value-added realms of knowledge- and innovation-related production. The chapter interrogates the state’s driving rationales which foregrounds the Global North in imagination and aspiration. An argument is made instead for reimagining the local as a space for the ideas and insights which support creativity and innovation.

Mean-Variance-CvaR Model of Multiportfolio Optimization via Linear Weighted Sum Method

We propose a new approach to optimizing portfolios to mean-variance-CVaR (MVC) model. Although of several researches have studied the optimal MVC model of portfolio, the linear weighted sum method (LWSM) was not implemented in the area. The aim of this paper is to investigate the optimal portfolio model based on MVC via LWSM. With this method, the solution of the MVC model of portfolio as the multiobjective problem is presented. In data analysis section, this approach in investing on two assets is investigated. An MVC model of the multiportfolio was implemented in MATLAB and tested on the presented problem. It is shown that, by using three objective functions, it helps the investors to manage their portfolio better and thereby minimize the risk and maximize the return of the portfolio. The main goal of this study is to modify the current models and simplify it by using LWSM to obtain better results.

New Model for Shariah-Compliant Portfolio Optimization under Fuzzy Environment

In this paper, a new fuzzy model is presented to improve methods for Shariah-compliant portfolio optimization, and provides an overview of faithcompliant portfolio optimization. The spread of fuzzy variable is adopted as criteria in practical risk management in Islamic finance. First, the effect of shariah-compliant strategy to portfolio optimization is covered in the literature review. After analyzing the literature, it was identified that the previous models lack the fuzzy environment so that they were unable to deal with uncertainty. Consequently, a new model is proposed to apply the fuzzy environment to Shariah-compliance portfolio optimization. In this research activity, an E-S model by the linear combination of the risk and reward for fuzzy shariah-compliant is proposed. Finally, some future works are presented along with a conclusion.

Modelling and optimization for palm oil plantation management

In this paper, the problem of palm oil plantation management is considered. A non-linear mathematical model is proposed considering two state variables as the density of the young palm oil trees and the part of biomass that can produce oil. In the modelling process, it is assumed that the rate of planting new young trees and the rate of felling inefficient trees can be controlled. It is further assumed that the oil production rate is directly proportional to the biomass of palm oil plantation. A system of delay differential equations is developed to study the behaviour of palm oil plantation. The resulting optimal control problem is also solved to estimate the control variables while the objective is to maintain the biomass at a certain level and maximize the oil palm production in a long period. Numerical simulations are given to illustrate the results.

Solving Optimal Control Problem Using Max-Min Ant System

An improved ant colony algorithm for optimal control problems with box constrain on control functions is presented. The hypercube of the feasible controls as well as the time interval are initially discretized to approximate control problem into a discrete parameter selection problem. Then, the ant colony algorithm is applied to search for optimum parameters of approximated problem while a proper local search is also introduced to iteratively enhance the quality of solution. The results of numerical simulation on MATLAB environment illustrate the effectiveness of this method.

The Stories They Tell: Understanding International Student Mobility Through Higher Education Policy:

The movement of students across borders has had profound impact on higher education policy development. This article seeks to unpack international student mobility through a discourse approach, using five policy documents on international student mobility from well-established recruiters of international students. Eight headline findings are presented in this article. It was found that there are many different types of international students. Higher education institutions are located at the heart of the action, and provide a broad range of services across four distinctive stages of the students’ sojourn. Governments reaffirm their commitment in providing good higher education experience to the international student population. However, there are signs that the students’ presence has shaped higher education policies to be more service-, market-, and reputation driven. The ethics of care concept is proposed to balance the present role of higher education as “wealth creation agents,” and to ensure both institutions and students reap the benefits of international higher education.

Smoothing Solution for Discrete-Time Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences

In this chapter, the performance of the integrated optimal control and parameter estimation (IOCPE) algorithm is improved using a modified fixed-interval smoothing scheme in order to solve the discrete-time nonlinear stochastic optimal control problem. In our approach, a linear model-based optimal control problem with adding the adjustable parameters into the model used is solved iteratively. The aim is to obtain the optimal solution of the original optimal control problem. In the presence of the random noise sequences in process plant and measurement channel, the state dynamics, which is estimated using Kalman filtering theory, is smoothed in a fixed interval. With such smoothed state estimate sequence that reduces the output residual, the feedback optimal control law is then designed. During the computation procedure, the optimal solution of the modified model-based optimal control problem can be updated at each iteration step. When convergence is achieved, the iterative solution approaches to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. Moreover, the convergence of the resulting algorithm is also given. For illustration, optimal control of a continuous stirred-tank reactor problem is studied and the result obtained shows the efficiency of the approach proposed.