Ong Yew Soon

Adaptive cellular memetic algorithms

A cellular genetic algorithm (CGA) is a decentralized form of GA where individuals in a population are usually arranged in a 2D grid and interactions among individuals are restricted to a set neighborhood. In this paper, we extend the notion of cellularity to memetic algorithms (MA), a configuration termed cellular memetic algorithm (CMA). In addition, we propose adaptive mechanisms that tailor the amount of exploration versus exploitation of local solutions carried out by the CMA. We systematically benchmark this adaptive mechanism and provide evidence that the resulting adaptive CMA outperforms other methods both in the quality of solutions obtained and the number of function evaluations for a range of continuous optimization problems.

Extreme learning machine for predicting HLA-Peptide binding

Machine learning techniques have been recognized as powerful tools for learning from data. One of the most popular learning techniques, the Back-Propagation (BP) Artificial Neural Networks, can be used as a computer model to predict peptides binding to the Human Leukocyte Antigens (HLA). The major advantage of computational screening is that it reduces the number of wet-lab experiments that need to be performed, significantly reducing the cost and time. A recently developed method, Extreme Learning Machine (ELM), which has superior properties over BP has been investigated to accomplish such tasks. In our work, we found that the ELM is as good as, if not better than, the BP in term of time complexity, accuracy deviations across experiments, and – most importantly – prevention from over-fitting for prediction of peptide binding to HLA.

Using classification for constrained memetic algorithm: A new paradigm

Regression has been successfully combined with the memetic algorithm (MA) for constructing surrogate models. It is essentially an attempt to approximate the objective or constraint landscape of a constrained optimization problem. Classification, on the other hand, has probably never been thought of being of any assistance to the MA. In fact, it can be used to approximate the feasibility boundary by means of some decision functions. The search effort can thus be focussed on the nearby region, recalling that many constrained optimization problems have their optimal solutions situated on the boundaries. This simply means that only potential individuals will undergo local refinements, reducing the number of function evaluations and accelerating the identification of the global optimum. Presented in this paper is a new approach that combines the support vector machine (SVM) with the MA to achieve this purpose.

Classification-assisted memetic algorithms for solving optimization problems with restricted equality constraint function mapping

The success of Memetic Algorithms (MAs) has driven many researchers to be more focused on the efficiency aspect of the algorithms such that it would be possible to effectively employ MAs to solve computationally expensive optimization problems where single evaluation of the objective and constraint functions may require minutes to hours of CPU time. One of the important design issues in MAs is the choice of the individuals upon which local search procedure should be applied. Selecting only some potential individuals lessens the demand for functional evaluations hence accelerates convergence to the global optimum. In recent years, advances have been made targeting optimization problems with single equality constraint h(x) = 0. The presence of previously evaluated candidate solutions with different signs of constraint values within some localities thus allows the estimation of the constraint boundary. An individual will undergo local search only if it is sufficiently close to the approximated boundary. Elegant as it may seem, the approach had unfortunately assumed that every constraint function maps the design variables to optimize into unbounded real values. This, however, may not always be the case in practice. In this paper, we present a strategy to efficiently solve constrained problems with a single equality constraint; the function of which maps the design variables into restricted (either strictly non-negative or strictly non-positive) real values only.

An Agent-Based Simulation to Quantify and Analyze Bullwhip Effects in Supply Chains

The paper presents an agent-based simulation to model supply chains (SCs) and evaluate the bullwhip effect, an important index to measure the stability of SCs, under the stochastic demand and lead time. We carried out a series of numerical experiments to measure the bull whip effects in two scenarios. In the first scenario with a simple 2-tier SC, the results of simulation were obtained and compared directly to the analytical counterparts in literature. From the comparison between simulated and analytical results, it was figured out that the assumption of non-negative demand/order quantities, which are usually not considered in analytical models, could affect the results of bullwhip effect measurement. In the second scenario, the bullwhip effect in a 4-tier SC was evaluated to observe quantitatively how the variances in demands and replenishment orders are amplified while moving upwards the SC.

Grid-based PSE for Engineering of Materials (GPEM)

The design and engineering of complex materials and products often requires intricate interactions between domain experts in science, material and engineering as well as the utilization of diverse software systems for discovery and optimization. If left as it is, design engineers would most likely be at a loss on how to engage the entire entourage of the multi- disciplinary processes as well as the compute-intensive and data-intensive nature of the activities involved. This paper describes a possible solution through the development of a Grid-based Problem Solving Environment for Engineering of Materials (GPEM). The GPEM aims to provide a one-stop platform where engineers will perform material discovery, design optimization and material characterization, with grid computing as the enabling technology. Upon describing the details of the process workflow and the adopted architecture design, the paper will present the current implementation of GPEM, in the design optimization of fractal structures.

Solving multi-vehicle profitable tour problem via knowledge adoption in evolutionary bi-level programming

Profitable tour problem (PTP) belongs to the class of vehicle routing problem (VRP) with profits seeking to maximize the difference between the total collected profit and the total cost incurred. Traditionally, PTP involves single vehicle. In this paper, we consider PTP with multiple vehicles. Unlike the classical VRP that seeks to serve all customers, PTP involves the strategic-level customer selection so as to maximize the total collected profit and the operational-level route optimization to minimize the total cost incurred. Therefore, PTP is essentially the knapsack problem at the strategic level with VRP at the operational level. That means the evolutionary bi-level programming would be a suitable choice of methodology for solving the NP-hard PTP. Employing some evolutionary method to solve the bi-level program naively would undoubtedly be prohibitively expensive. We thus present in this paper the notion of knowledge adoption to approximate the initial solution to the lower-level optimization problem for a given trial solution of the upper-level decision variables. One may consider the knowledge adoption as a special case of knowledge transfer in which the transfer takes place within the same problem domain. Refining the approximate initial solution with local search forces it to quickly converge to some locally optimal solution. The better the estimation of the initial solution, the closer the local optimum will be to the global one. PTP finds its important application in the fields of transportation and logistics. In addressing last-mile problem using auction at the urban consolidation center (UCC), PTP plays a significant role in the winner determination problem (WDP) that follows. Empirical study demonstrates the efficacy of the proposed approach in solving the PTP-based WDP, yielding significantly higher profit, utilization, and service level compared to when the UCC adopts the conventional WDP based on multiple knapsack problem (i.e. the MKP-based WDP).

A Review of Complex Systems Technologies for Supply Chain Risk Management

Supply chain disruptions and risks have been garnering more attention in recent years owing to the occurrence of several high-profile disruptions and many published works acknowledge the increasing vulnerability to disruptions in supply chains. Few, however, explicitly embrace complex systems (CS) approach as a holistic, system-wide perspective towards supply chain risk management (SCRM). This paper analyzes the current status and identifies research trends in applying CS approaches to SCRM by analyzing the Science Direct publication database. The paper then provides a further review of CS technologies for SCRM and discusses on the gaps and directions for future research in SCRM by CS technologies.