Meng-Hiot Lim

• Ph.D
• Nanyang Technological University

This book is a collection of selected high-quality research papers presented at International Conference on Paradigms of Communication, Computing and Data Analytics (PCCDA 2023), held at South Asian University, New Delhi, India, during 22–23 April 2023. It discusses cutting-edge research in the areas of advanced computing, communications and data s...

High-order problems pose significant challenges for evolutionary algorithms (EAs) to optimize. To mitigate this, a deep hybrid transfer learning EA (DHTL-EA) is proposed. DHTL-EA works by transferring both the model and the optima from a corresponding low-order problem. Here, a deep neural network is adopted to model both the low-order and high-ord...

As an emerging network model, spiking neural networks (SNNs) have aroused significant research attentions in recent years. However, the energy-efficient binary spikes do not augur well with gradient descent-based training approaches. Surrogate gradient (SG) strategy is investigated and applied to circumvent this issue and train SNNs from scratch. D...

Artificial Neural Network (ANN) has served as an important pillar of machine learning which played a crucial role in fueling the robust artificial intelligence (AI) revival experienced in the last few years. Inspired by the biological brain architecture of living things, ANN has shown widespread success in pattern recognition, data analysis and cla...

Indoor positioning is a key technology enabler for various smart systems that require location-based optimization and automation. In this paper, we present CO-LEELM, a continuous-output location fingerprinting method that combines two existing location fingerprinting methods to produce better accuracy in a dynamic environment where training data an...

Evolutionary algorithms (EAs) are usually required to solve problems based on domination relationship among solutions. Often, the domination relationship is almost the sole source of knowledge that EAs can utilize, especially when the problem solving engine concerned is taken as a black box. In this paper, the domination landscape (DL), onto which...

Memetic Computing is a subject in computer science which considers complex structures as the combination of simple agents, memes, whose evolutionary interactions lead to intelligent structures capable of problem-solving. This paper focuses on Memetic Computing optimization algorithms and proposes a counter-tendency approach for algorithmic design....

With the rapid growing market of wireless devices, positioning systems that make use of the signal strength of wireless devices are gaining more interest nowadays. Being able to track the location of a Wi-Fi or Radio Frequency Identification device could improve the quality of services in various sectors, including security, warehouse, logistic man...

Reacting to situational needs for disaster relief measures and deployment of limited resources is a two-pronged operational and procedural protocol. It involves pre-disaster scenario-based planning and subsequently real-time on-the-field execution. In view of the potential complexity in terms of scale and magnitude involved, the pre-disaster stage...

Indoor positioning is a key technology that enables the development of various smart and autonomous indoor applications. In recent years, wireless device is considered a promising technology enabler for indoor positioning despite facing various problems in the form of spatial, temporal, and device variation. Here we present a survey of problems in...

Wind power is becoming increasingly popular as a renewable source of energy. Being a non-dispatchable energy resource, wind power facilities entail efficient forecast mechanisms to estimate the production of various wind power utilities available. In an integrated grid system, a balance must be maintained between production and consumption. Given t...

Signal strength can be used to estimate location of a wireless device. As compared to other signal measures such as time-based and angle-based metrics, signal strength is normally embedded in wireless transceivers. This allows us to add location estimation feature on top of any wireless systems without requiring hardware modification. However, sign...

A method and system are presented for configuring a search algorithm for solving a combinatorial optimization problem. The search algorithm has a number of procedural components. Each procedural component is configured using a respective data structure. The data structure has a tree structure, including traversal split nodes, each of which represen...

Radio Frequency Identification (RFID) and its localization capability have played a significant role in the advancement of the internet-of-things in recent years. In this preliminary work, we examine a statistical approach to perform zone classification based on RFID signal strength. A one-dimensional experimental test was conducted in an outdoor a...

For many years, various neural network models have been used to solve regression, binary classification, and multi-class classification problems. Their performance has been extensively compared against each other in terms of testing accuracy and training time. For multi-class classification problem, testing accuracy does not always give comprehensi...

This paper is a preliminary work which seeks the possibilities of using Extreme Learning Machine (ELM) for location classification. We gathered signal strength data from Radio Frequency Identification (RFID) tags and fed the data into the ELM to find in which room a tag is located. We also investigated ELM configuration that results best accuracy f...

Band selection plays an important role in identifying the most useful and valuable information contained in the hyperspectral images for further data analysis such as classification, clustering, etc. Memetic algorithm (MA), among other metaheuristic search methods, has been shown to achieve competitive performances in solving the NP-hard band selec...

Forecasting electricity prices has been a widely investigated research issue in the deregulated power market scenario. High price volatilities, price spikes caused by a number of factors such as weather uncertainty, fluctuating fuel prices, transmission bottlenecks, etc., make the task of accurate price forecasting a formidable challenge for the ma...

In recent decades, a plethora of dedicated evolutionary algorithms (EAs) have been crafted to solve domain specific complex problems more efficiently. Many advanced EAs have relied on the incorporation of domain specific knowledge as inductive biases that is deemed to fit the problem of interest well. As such, the embedment of domain knowledge abou...

Feature selection is an important preprocessing step for many high-dimensional regression problems. One of the most common strategies is to select a relevant feature subset based on the mutual information criterion. However, no connection has been established ...

This letter points out that the main ideas and conclusions of the ''The No-Prop algorithm'' paper which has recently appeared in this journal were proposed earlier by G.-B. Huang et al. 10 years ago and intensively discussed and applied by other authors in the past 10 years.

In this paper, resource gathering problem is modeled as a cooperative pathfinding problem in which the game agent is only given the knowledge of its immediate surroundings and must gather knowledge about the dynamics of the navigation graph on which it explores by sharing information and cooperating with other agents in the game environment. This p...

Export Date: 28 July 2014

ELM is considered as a meme encapsulation engine for speeding up evolutionary search on vehicle routing problems (VRP). The ELM enhances the conventional EA by automating the learning of knowledge memes from previous vehicle routing experiences. The objective of the learning task assignment via the ELM is to create association lists of customers to...

This letter points out that the main ideas and conclusions of the "The No-Prop algorithm" paper which has recently appeared in this journal were proposed earlier by G.-B. Huang et al. 10 years ago and intensively discussed and applied by other authors in the past 10 years.

Unmanned aerial vehicles (UAVs) rely on global positioning system (GPS) information to ascertain its position for navigation during mission execution. In the absence of GPS information, the capability of a UAV to carry out its intended mission is hindered. In this paper, we learn alternative means for UAVs to derive real-time positional reference i...

The advancement in game technology has served to enrich player’s gaming experience in a substantial way. Nowadays, it is common to have blockbuster quality games, with realistic graphics and engaging stories. Despite this, the progress made in incorporating Artificial Intelligence has been slow, and realistic human-like intelligence in games is har...

In this paper, we propose a silicon implementation of extreme learning machines (ELM) using spiking neural circuits. The major components of a silicon spiking neural network, neuron, synapse and ‘Address Event Representation’ (AER) for asynchronous spike based communication, are described. The benefits of using this hardware to implement an ELM as...

The computation bandwidth offered by current integrated circuits IC provides the capacity for an IC chip to handle multiple tasks or applications. In this paper, we present the concept and design of a multi-task fuzzy inference processor MtFIP. Earlier, a reconfigurable fuzzy inference chip RcFIP with 17 mega fuzzy logic inference per second FLIPS...

Some papers and technical correspondence based on engineering application of memetic computing (MC) selected for publication in the IEEE transactions on systems, man, and cybernetics, are discussed. Li et al. presented a quantum memetic algorithm (QMA) that integrates the principles of quantum computing with the notions of the cultural evolution. S...

The application of specific learning schemes in memetic algorithms (MAs) can have significant impact on their performances. One main issue revolves around two different learning schemes, specifically, Lamarckian and Baldwinian. It has been shown that the two learning schemes are better suited for different types of problems and some previous studie...

Memetic computing is a subject in computer science which considers complex structures as the combination of simple agents, memes, whose evolutionary interactions lead to intelligent structures capable of problem-solving. This paper focuses on memetic computing optimization algorithms and proposes a counter-tendency approach for algorithmic design....

In recent years, researchers have become more aware of the significance and importance of memes in computational problem‐solving. It is now generally accepted that collectively, memes as a group or population undergo evolution just like genes, competition and collaboration. In this paper, we present a memes co‐evolutionary framework for solving the...

Memetic computation is a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem-solving. It covers a plethora of potentially rich meme-inspired computing methodologies, frameworks and operational algorithms including simple hybrids, adaptive hybrids and memetic automaton....

To date, algorithms that are designed for solving different Vehicle Routing Problem (VRP) benchmarks usually incorporate domain driven biases of various forms. This makes an algorithm effective and efficient for some VRP benchmark sets but not necessarily on others. This paper presents a memetic algorithm for Capacitated Vehicle Routing Problems (C...

A meme in the context of optimization represents a unit of algorithmic abstraction that dictates how solution search is carried out. At a higher level, a meta-meme serves as an encapsulation of the scheme of interplay between memes involved in the search process. This paper puts forth the notion of neural meta-memes to extend the collective capacit...

This research focuses on trajectory generation algorithms that take into account the stealthiness of autonomous UAVs; generating stealthy paths through a region laden with enemy radars. The algorithm is employed to estimate the risk cost of the navigational space and generate an optimized path based on the user-specified threshold altitude value. T...

To date, algorithms that are designed for solving different Vehicle Routing Problem (VRP) benchmarks usually incorporate domain driven biases of various forms. This makes an algorithm effective and efficient for some VRP benchmark sets but not necessarily on others. This paper presents a memetic algorithm for Capacitated Vehicle Routing Problems (C...

From nature, we observe swarming behavior in the form of ant colonies, bird flocking, animal herding, honey bees, swarming of bacteria, and many more. It is only in recent years that researchers have taken notice of such natural swarming systems as culmination of some form of innate collective intelligence, albeit swarm intelligence (SI) - a metaph...

In this paper, we present a Neural Meta-Memes Framework (NMMF) for combinatorial optimization. NMMF is a framework which models basic optimization algorithms as memes and manages them dynamically when solving combinatorial problems. NMMF encompasses neural networks which serve as the overall planner/coordinator to balance the workload between memes...

Path planning problem is one of core contents of UAV technology. This paper presents an improved heuristic algorithm to solve 3D path planning problem. In this study the path planning model is built based on digital map firstly, and then the virtual terrain is introduced to eliminate a significant amount of search space, from 3-Dimensions to 2-Dime...

Taking a lead from the multi-faceted definitions and roles of the term "meme" in memetics, a plethora of potentially rich memetic computing methodologies, frameworks and operational memeinspired algorithms have been developed with considerable success in several realworld domains in the last two decades. This article showcase several successful dep...

Lamarckian evolution and Baldwinian effect are two typical replacement schemes of the individual learning process in Memetic Computing. In this paper, we perform a comprehensive study of the behaviour of Lamarckian evolution and Baldwinian effect in noisy and noiseless continuous optimization problems. The output of this study shows that Lamarckian...

A generalized higher-order finite-difference method (in which the time difference utilizes the symplectic integrator propagator and the spatial difference utilizes the discrete singular convolution) is proposed to analyze electromagnetic scattering from multiple targets. Numerical test results have demonstrated the higher stability and enhanced eff...

First order saddle points have important applications in different fields of science and engineering. Some of their interesting applications include estimation of chemical reaction rate, image segmentation, path-planning and robotics navigation. Finding such points using evolutionary algorithms is a field that remains yet to be well investigated. I...

In region coverage, UAVs typically follow a certain pattern of combing through a region. In this paper, we present a turning time model for estimating the time cost for UAV. Based on this, we explore two strategies of scanning as a basis for a memetic computing search: the raster and circular scanning strategies. To our knowledge, there has been no...

Unmanned aerial vehicles (UAVs) are increasingly being used in real-world applications, mostly military. This paper presents an efficient algorithm for path planning in guidance of autonomous UAV; generating stealthy paths through a set of enemy radar sites of known locations. A preliminary path consisting of a series of straight-line segments is f...

In this paper, we present a scheme whereby diverse optimization algorithms are incorporated within a framework of selective reproduction according to fitness. By forming an ensemble of several populated optimization algorithms, it is shown that the exploitative traits can be extended across several search algorithms. Results of simulations on sever...

Memetic algorithms (MA) have recently been applied successfully to solve decision and optimization problems. However, selecting a suitable local search technique remains a critical issue of MA, as this significantly affects the performance of the algorithms. ...

Memetic algorithms (MAs) represent one of the recent growing areas in evolutionary algorithm (EA) research. The term MAs is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Bal...

In computational intelligence, the term 'memetic algorithm' has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a 'meme' has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate,...

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 addit...

A testbed with built-in data processing capability is presented in this paper. The built-in testbed is designed for a small form factor storage protocol. In the market, there are several dedicated test equipments supporting the protocol of the storage device. However, they are more suitable in the development stage. A built-in testbed which is suit...

The general problem of path planning can be modeled as a traveling salesman problem which assumes that a graph is fully connected. Such a scenario of full connectivity is however not always realistic. One such motivating example for us is the application of path planning for unmanned reconnaissance aerial vehicles (URAVs). URAVs are widely deployed...

In recent years, there has been an increase in research activities on Memetic Algorithm (MA). MA works with memes; a meme being defined as "the basic unit of cultural transmission, or imitation" [5]. In this respect, a Memetic Algorithm essentially refers to "an algorithm that mimics the mechanisms of cultural evolution". To date, there has been si...

We report our work on the algorithmic development of an evolutionary methodology for automatic configuration of metaheuristic algorithms for solving complex combinatorial optimization problems. We term it automatic configuration engine for metaheuristics (ACEM). We first propose a novel left variation-right property (LVRP) tree structure to manage...

Many deterministic algorithms in the context of constrained optimization require the first-order derivatives, or the gradient vectors, of the objective and constraint functions to determine the next feasible direction along which the search should progress. Although the second-order derivatives, or the Hessian matrices, are also required by some me...

In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown in the work reported here, such parameters tweaking based...

The general problem of path planning can be modeled as a travelling salesman problem which assumes a graph is fully connected. Full connectivity is however not realistic in many practical path planning problems. The graphs are typically sparse graphs such as for Unmanned Reconnaissance Aerial Vehicles (URAV). This paper describes an Ant Colony Syst...

Algorithm Development Environment for Permutation-based problems (ADEP) is a software environment for configuring meta-heuristics for solving combinatorial optimization problems. This paper describes the key features of ADEP and how the environment was used to generate a Memetic Algorithm (MA) solution for Hamiltonian Cycle Problems (HCP). The effe...

Distributed computing environments offer vast amounts of computational power for use in parallel memetic algorithms. However, they consist of heterogeneous computing nodes, in terms of computational power, operating platform, network connectivity and latency. The behavior of parallel memetic algorithms in such environment is poorly understood: the...

In this paper, we propose an evolvable fuzzy system for ATM cell scheduling. When the scenarios of cell flows in an ATM network change dramatically, traditional scheduling algorithms, first-in-first-out (FIFO) and static priority, which employ static switching scheme may see their efficiency deteriorate. An alternative is to use dynamically weighte...

Abstract In this paper, we present a Multi-Surrogates Assisted Memetic Algo- rithm (MSAMA) for solving optimization problems with computationally expen- sive fitness functions. The essential backbone,of our framework,is an evolutionary algorithm coupled with a local search solver that employs,multi-surrogates in the spirit of Lamarckian learning. I...

Evolvable fuzzy system (EFS) relies on dynamic adaptation of the heuristics which are coded as fuzzy rules. If the mechanisms for fuzzy inferencing are realized in hardware within the framework of the EFS in order to satisfy the time-critical requirement of the application, the system can be regarded as a form of evolvable fuzzy hardware. This chap...

Parallel Memetic Algorithms (PMAs) are a class of modern parallel meta-heuristics that combine evolutionary algorithms, local search, parallel and distributed computing technologies for global optimization. Recent studies on PMAs for large-scale complex combinatorial optimization problems have shown that they converge to high quality solutions sign...

Many existing works for handling uncertainty in problem-solving rely on some form of a priori knowledge of the uncertainty structure. However, in reality, one may not always possess the necessary expertise or sufficient knowledge to identify suitable bounds of the uncertainty involved. Rather, it is more likely that specifications of the realistic...

Given a planar workpiece R, the objective of region coverage is to find an ordered list of waypoints and the geometry of paths between consecutive waypoints along which the centroid of a sensor footprint can be moved to efficiently trace a minimal superset of R. We consider the problem of maximally parallelizing the coverage of a contiguous rectili...

This paper describes a novel design of a fuzzy inference chip that allows for real-time online context switching. A context refers to a situation or scenario of an application requiring specific domain knowledge. In particular, our focus is on the class of applications involving embedded fuzzy control. The domain knowledge therefore refers to fuzzy...

Terrain models that permit multiresolution access are essential for model predictive control of unmanned aerial vehicles in low-level flights. The authors present the extreme learning machine (ELM), a recently proposed learning paradigm, as a mechanism for learning the stored digital elevation information to allow multiresolution access. We give re...

In this paper, we propose the island model parallel memetic algorithm with diversity-based dynamic adaptive strategy (PMA- DLS) for controlling the local search frequency and demonstrate its utility in solving complex combinatorial optimization problems, in particular large-scale quadratic assignment problems (QAPs). The empirical results show that...

In recent decades, many meta-heuristics, including genetic algorithm (GA), ant colony optimization (ACO) and various local search (LS) procedures have been developed for solving a variety of NP-hard combinatorial optimization problems. Depending on the complexity of the optimization problem, a meta-heuristic method that may have proven to be succes...

Without Abstract

In the School of Electrical and Electronic Engineering (EEE), Nanyang Techno-logical University (NTU), allocation of subjects according to preferences indicated by students during registration is a complex optimization problem. We propose an evolutionary algorithm based system that attempts to maximize satisfaction for the overall student group. Th...

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive m...

The reconfigurable fuzzy inference chip (RFIC) is a novel concept of a hardware fuzzy inference processor. It supports online realtime context switching. Such a capability is significant, particularly for situations where the application scenarios are expected to change dynamically. Furthermore, the RFIC can be extended to support hardware that rec...

The problem of terrain modeling is basically a type of function approximation problem. This type of problem has been widely studied in the soft computing community. In recent years, neural networks have been successfully applied to surface reconstruction and classification problems involving scattered data. However, due to the iterative nature of t...

Low flying, small endurance UAVs are well-suited for region coverage over airbases or in urban zones since they are cheap, highly maneuverable and expendable. In this paper we consider the problem of minimizing the time needed to cover the region of interest, a contiguous rectilinear polygonal workspace, Pscr, using eta UAVs. Our approach is based...

The extent of the application of local searches in canonical memetic algo- rithm is typically based on the principle of "more is better". In the same spirit, the parallel memetic algorithm (PMA) is an important extension of the canonical memetic algorithm which applies local searches to every transitional solutions being considered. For PMA which a...

In packet switching network such as asynchronous transfer mode (ATM), the switching characteristics is important in delivering the guaranteed QoS (Quality of Service) level of the network. Many methods have been developed to control cell flow for shared bandwidth. The first-in first-out (FIFO), static priority (SPR), dynamically weighted priority s...

In recent decades, various metaheuristics, such as genetic algorithms (GA), have been proposed to solve the vehicle routing problem (VRP), a well-known class of combinatorial optimization problems. It is generally known that the scheme for genetic representation of the solution albeit the chromosome coding structure, can play a crucial role in GA....

Static routing and wavelength assignment (RWA) is usually formulated as an optimization problem with the objective of minimizing wavelength usage (MWU). Existing solution methodologies for the MWU problem are usually based on a two-step approach, where routing and wavelength assignment are done independently. Though this approach can reduce computa...

An unmanned reconnaissance aerial vehicle mounted sensor of footprint with a small area ω_s² is used to cover critical airbase structures for damage assessment. The region of coverage interest is modeled with a closed union of a minimal set of interior disjoint rectangles of width ω_i = ω_s. We wish to find...

Memetic algorithms have become to gain increasingly important for solving large scale combinatorial optimization problems. Typically, the extent of the application of local searches in canonical memetic algorithm is based on the principle of "more is better". In the same spirit, the island model parallel memetic algorithm (PMA) is an important exte...

This paper explores evolution search algorithm for solving the N-queen problem. It will be shown how simple mechanisms of selection, reproduction and mutation can be effective in solving the N-queen problem. Simulation of the search algorithm for N up to 2000 has been achieved on a personal computer. The algorithm is robust and is capable of explor...

One important aspect of supply chain management is the maintenance of an efficient and cost-effective system for the distribution of goods or products to retailing outlets. Nowadays, it is almost inevitable that an efficient distribution network can only be achieved with the support of computer based optimization software. We show in this paper how...

Our goal is to decompose an arbitrary pathwise continuous rectilinear polygon with holes into a fixed number of nonoverlapping pathwise continuous rectilinear open subsets. This goal involves two additional hard constraints – a. The area of each subset must equal an a priori value. b. The union of the closure of the subsets must be equal to the pol...

We give an algorithm to generate a coverage motion plan for a single unmanned reconnaissance aerial vehicle (URAV) over a holed rectilinear polygonal region. The URAV is equipped with a stabilized downward-looking sensor which has a square footprint of a fixed area. The algorithm is based on the principle of divide-and-conquer. In the first step, w...

A graph Sp,q,n refers to a signed graph with p nodes and q edges with n being the number of negative edges. We introduce two theorems to facilitate identification of the complete set of balanced signed graph configurations for any p-node Hamiltonian signed graph in terms of p, q and n. This allows for the development of computational procedures to...

To achieve real-time performance for some applications of fuzzy systems it is necessary to realise the inference module in hardware. By the same token, real-time applications of evolvable fuzzy hardware systems require the realisation of systems as hardware that can be conveniently reconfigured. A good example of such a scenario is illustrated for...