Ponnuthurai N. Suganthan

Ponnuthurai N. Suganthan
Nanyang Technological University | ntu · School of Electrical and Electronic Engineering

BA (U of Cambridge, UK), PhD

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561
Publications
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Citations

Publications

Publications (561)
Article
Energy disaggregation (ED), with minimal infrastructure, can create energy awareness and thus promote energy efficiency by providing appliance-level consumption information. However, ED is highly ill-posed and gets complicated with increase in number and type of devices, similarity between devices, measurement errors, etc. To design, test, and benc...
Preprint
Full-text available
The integration of Vector Quantised Variational AutoEncoder (VQ-VAE) with autoregressive models as generation part has yielded high-quality results on image generation. However, the autoregressive models will strictly follow the progressive scanning order during the sampling phase. This leads the existing VQ series models to hardly escape the trap...
Preprint
Full-text available
Transfer Optimization, understood as the exchange of information among solvers to improve their performance, has gained a remarkable attention from the Swarm and Evolutionary Computation community in the last years. This research area is young but grows at a fast pace, being at the core of a corpus of literature that expands day after day. It is un...
Preprint
Full-text available
An ensemble of decision trees is known as Random Forest. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models' core strength. In this paper, we propose two approaches for generating ensembles of double random forest. In the first approach, we propose a rotation based ensemble of double rand...
Article
Full-text available
Differential evolution has been a simple yet effective algorithm for global optimization problems. The performance of differential evolution (de) highly depends on its operators and parameter settings. In the last couple of decades, many advanced variants of de have been proposed by modifying the operators and introducing new parameter tuning metho...
Article
This article addresses the printed circuit board (PCB) grouping problem (PGP) in the electronic assembly industry. First, a mathematical model of PGP is presented. Then, four heuristics based on an iterative scheme are proposed. Two of them are constructive heuristics and the other two are composite heuristics. Based on the problem-specific charact...
Article
The flowshop sequence-dependent group scheduling problem (FSDGSP) with the production efficiency measures has been extensively studied due to its wide industrial applications. However, energy efficiency indicators are often ignored in the literature. This paper considers the FSDGSP to minimize makespan, total flow time and total energy consumption,...
Article
Full-text available
Effective constraint handling techniques are of great significance for evolutionary algorithms dealing with constrained optimization problems. To date, many constraint handling techniques, such as penalty function, superiority of feasible solutions, and -constraint, have been designed. However, different constraint handling techniques are usually s...
Article
The challenge of accurately forecasting a time series covers numerous disciplines, from economics to engineering. Among the thousands of machine learning models, random vector functional link (RVFL) is a robust and efficient model which has demonstrated its success in various challenging forecasting problems. RVFL is an efficient universal function...
Article
Random Vector Functional Link (RVFL) is popular among researchers in many areas of machine learning. RVFL is preferred by many researchers as RVFL can produce good performance with relatively little training time. Recent works extend RVFL into deep and ensemble versions. However, RVFL does not have effective feature extraction methods commonly used...
Article
Generally, Synthetic Benchmark Problems (SBPs) are utilized to assess the performance of metaheuristics. However, these SBPs may include various unrealistic properties. As a consequence, performance assessment may lead to underestimation or overestimation. To address this issue, few benchmark suites containing real-world problems have been proposed...
Preprint
Electricity load forecasting is crucial for the power systems' planning and maintenance. However, its un-stationary and non-linear characteristics impose significant difficulties in anticipating future demand. This paper proposes a novel ensemble deep Random Vector Functional Link (edRVFL) network for electricity load forecasting. The weights of hi...
Article
This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been withdrawn at the request of the editor and publisher. The publisher regrets that an error occurred which led to the premature publication of this paper. This error bears no reflection on the arti...
Article
Full-text available
Optimal power flow (OPF) is one of the challenging optimization problems in power domain. The complexity of the problem escalates with incorporation of uncertain and intermittent renewable sources into the electrical network. Flexible AC transmission system (FACTS) devices are also becoming more commonplace in modern power system to mitigate growin...
Article
Hybridization in context to Evolutionary Computation (EC) strives to combine operators, components, and the best merits of different EC paradigms, to form a new evolutionary algorithm that enjoys a statistically superior performance, compared to its ancestors, over a wide range of application-specific optimization problems. In this paper, we propos...
Poster
Full-text available
Handbook of Nature-Inspired Optimization Algorithms: The State of the Art Volume I: Solving Single Objective Bound-Constrained Real-Parameter Optimization Problems
Poster
Full-text available
Handbook of Nature-Inspired Optimization Algorithms: The State of the Art Volume II: Solving Single Objective Constrained Real-Parameter Optimization Problems
Article
Full-text available
The use of optimization algorithms for designing Infinite Impulse Response (IIR) filters has been considered in many studies. The concern in this area is the multimodal error surface of such filters and their fitting with filter coefficients. The order of the modeled system has a direct effect on the number of coefficients, complexities of the erro...
Article
Abstract. Breast cancer is the second leading cause of death for women, so accurate early detection can help decrease breast cancer mortality rates. Computer-aided detection allows radiologists to detect abnormalities efficiently. Medical images are sources of information relevant to the detection and diagnosis of various diseases and abnormalities...
Conference Paper
Full-text available
Ensemble learning methods, which combine sev- eral base classifiers, is a common technique to enhance the classification ability of ensemble models in the field of pattern recognition and machine learning. Rotation Forest, an ensemble algorithm, has been used widely in various fields with nice generalization performance. The main idea of Rotation F...
Conference Paper
Full-text available
Ensemble learning methods, which combine several base classifiers, is a common technique to enhance the classification ability of ensemble models in the field of pattern recognition and machine learning. Rotation Forest, an ensemble algorithm, has been used widely in various fields with nice generalization performance. The main idea of Rotation For...
Preprint
Full-text available
Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models...
Article
In this paper, we propose deep learning frameworks based on the randomized neural network. Inspired by the principles of Random Vector Functional Link (RVFL) network, we present a deep RVFL network (dRVFL) with stacked layers. The parameters of the hidden layers of the dRVFL are randomly generated within a suitable range and kept fixed while the ou...
Article
In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algori...
Preprint
Full-text available
Probabilistic load forecasting (PLF) has become necessary for power system operators to do efficient planning across power transmission and distribution systems. However, there are not many PLF models, and those that exist take a lot of computation time and are not efficient, especially in multiple loads. This paper proposes a novel algorithm for s...
Article
In this study, we propose an efficient optimization algorithm that is a hybrid of the iterated greedy and simulated annealing algorithms (hereinafter, referred to as IGSA) to solve the flexible job shop scheduling problem with crane transportation processes (CFJSP). Two objectives are simultaneously considered, namely, the minimization of the maxim...
Article
Over the ages, nature has constantly been a rich source of inspiration for science, with much still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial intelligence, was rendered to model the collective behavior of social swarms in nature. Ultimately, Particle Swarm Optimization algorithm (PSO) is arguably one of...
Data
To implemet the code for optimizing the fraxtional order model. The user should click on fomcon-1.21b right click and select add to path ( then select folders and subfolders) to let all the inside files are readeable. Then use main to implement the optimization process
Article
This letter identifies original independent works in the domain of randomization-based feedforward neural networks. In the most common approach, only the output layer weights require training while the hidden layer weights and biases are randomly assigned and kept fixed. The output layer weights are obtained using either iterative techniques or non...
Article
Ensemble classifiers with random vector functional link network have shown improved performance in classification problems. In this paper, we propose two approaches to solve the classification problems. In the first approach, the original input space’s data points are mapped explicitly into a randomized feature space via neural network wherein the...
Book
Full-text available
Call for Book Chapters Handbook of Nature-Inspired Optimization Algorithms: The State of the Art Volume II: Solving Single Objective Constrained Real-Parameter Optimization Problems
Book
Full-text available
Call for Book Chapters Handbook of Nature-Inspired Optimization Algorithms: The State of the Art Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems
Article
For many-objective optimization problems (MaOPs), the proportion of non-dominated solutions in a population scales up sharply with the increase in the number of objectives. Besides, for an MaOP with a fixed number of objectives, the proportion of non-dominated solutions may also grow to a high level with the progressing of the evolutionary process,...
Article
In multiobjective optimization, the relationship between decision space and objective space is generally assumed to be a one-to-one mapping, but it is not always the case. In some problems, different variables have the same or similar objective value, which means a many-to-one mapping. In this situation, there is more than one Pareto Set (PS) mappi...
Article
Cloud computing is a recently looming-evoked paradigm, the aim of which is to provide on-demand, pay-as-you-go, internet-based access to shared computing resources (hardware and software) in a metered, self-service, dynamically scalable fashion. A related hot topic at the moment is task scheduling, which is well known for delivering critical cloud...
Book
Full-text available
This book includes the original, peer reviewed research articles from the 2nd International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA 2020), held in August, 2020 at Goa, India. It covers the latest research trends or developments in areas of data science, artificial intelligence, neural networks, cognitive scie...
Article
This paper presents a new method to determine the optimal driving strategies of the train using an improved brain storm optimisation (IBSO) algorithm. In the proposed method, the idea of successful-parent-selecting frame is applied to improve the original brain storm optimisation (BSO) algorithm avoiding premature convergence in evolutionary proces...
Article
This paper considers a distributed assembly permutation flowshop scheduling problem (DAPFSP) with total flowtime (TF) criterion, which is of great significance to both industry and research community. We propose an improved iterative greedy algorithm based on the groupthink (gIGA) for solving the problem. Firstly, based on the solution representati...
Article
Sorting solutions play a key role in using evolutionary algorithms (EAs) to solve many-objective optimization problems (MaOPs). Generally, different solution-sorting methods possess different advantages in dealing with distinct MaOPs. Focusing on this characteristic, this article proposes a general voting-mechanism-based ensemble framework (VMEF),...
Book
Full-text available
Call for Book Chapters Handbook of Nature-Inspired Optimization Algorithms: The State of the Art Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems
Poster
Full-text available
Call for Book Chapters Handbook of Nature-Inspired Optimization Algorithms: The State of the Art Volume II: Solving Single Objective Constrained Real-Parameter Optimization Problems
Poster
Full-text available
Call for Book Chapters Handbook of Nature-Inspired Optimization Algorithms: The State of the Art Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems
Chapter
Time series are usually complicated in nature and contains many complex patterns. As such, many researchers work on different ways to pick up such patterns. In this paper, we explore using Residual Networks (a Convolutional Neural Network) as a feature extractor for Oblique Random Forest. Here, we extract features using Residual Networks, and pass...
Article
Unmanned aerial vehicle (UAV) path planning enables UAVs to avoid obstacles and reach the target efficiently. To generate high-quality paths without obstacle collision for UAVs, this article proposes a novel autonomous path planning algorithm based on a tangent intersection and target guidance strategy (APPATT). Guided by a target, the elliptic tan...
Chapter
In this paper, we propose a novel adaptive ensemble variant of random vector functional link (RVFL) networks. Adaptive ensemble RVFL networks assign different weights to the sub-classifiers according to prediction performance of single RVFL network. Generic Adaptive Ensemble RVFL is composed of a series of unrelated, independent weak classifiers. W...
Chapter
In this paper, we propose an improved randomized based feed forward neural networks, known as Total variance minimization based random vector functional link network (Total-Var-RVFL) and intraclass variance minimization based random vector functional link network (Class-Var-RVFL). Total-Var-RVFL exploits the training data dispersion by minimizing t...
Article
Based on mating and environmental selections employed, multi-objective evolutionary algorithms (MOEAs) are classified as Pareto-based, decomposition-based and indicator-based approaches that are associated with their own advantages and disadvantages. To benefit from the advantages of different MOEAs, we propose an ensemble framework (ENMOEA) in whi...
Conference Paper
Full-text available
In this paper, we propose an improved randomized based feed forward neural networks, known as Total variance minimization based random vector functional link network (Total-Var-RVFL) and in-traclass variance minimization based random vector functional link network (Class-Var-RVFL). Total-Var-RVFL exploits the training data dispersion by minimizing...
Poster
Full-text available
Call for Book Chapter: Handbook of Nature-Inspired Optimization Algorithms: The State of the Art Volume II: Solving Single Objective Constrained Real-Parameter Optimization Problems
Poster
Full-text available
Call for Book Chapter: Handbook of Nature-Inspired Optimization Algorithms: The State of the Art. Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems
Conference Paper
Full-text available
The single objective multimodal bound-constrained optimization problems in CEC (IEEE Congress on Evolutionary Computation) competitions pose tremendous challenges to the researchers in finding the global optimum. This paper introduces the orthogonal array-based initialization of population and neighborhood search strategy in LSHADE (linear populati...
Preprint
Unmanned aerial vehicle (UAV) path planning enables UAVs to avoid obstacles and reach the target efficiently. To generate high-quality paths without obstacle collision for UAVs, this paper proposes a novel autonomous path planning algorithm based on a tangent intersection and target guidance strategy (APPATT). Guided by a target, the elliptic tange...
Article
Recently, meta-heuristics have been employed and improved for solving various scheduling and combinational optimization problems. Disassembly line balancing, planning and scheduling problems (DLBPSP) are typical examples since the high complexity (NP-Hard). Since 2000s, numerous articles have represented the applications of meta-heuristics for solv...
Conference Paper
Full-text available
Twin support vector machines (TWSVM) have been successfully applied to the classification problems. TWSVM is computationally efficient model of support vector machines (SVM). However, in real world classification problems issues of class imbalance and noise provide great challenges. Due to this, models lead to the inaccurate classification either d...
Article
K-nearest neighbor (KNN) based weighted multi-class twin support vector machines (KWMTSVM) is a novel multi-class classification method. In this paper, we propose a novel least squares version of KWMTSVM called LS-KWMTSVM by replacing the inequality constraints with equality constraints and minimized the slack variables using squares of 2-norm inst...
Article
Real-world optimization problems have been comparatively difficult to solve due to the complex nature of the objective function with a substantial number of constraints. To deal with such problems, several metaheuristics as well as constraint handling approaches have been suggested. To validate the effectiveness and strength, performance of a newly...
Article
Full-text available
In power systems, optimal power flow (OPF) is a complex and constrained optimization problem in which quite often multiple and conflicting objectives are required to be optimized. The traditional way of dealing with multi-objective OPF (MOOPF) is the weighted sum method which converts the multi-objective OPF into a single-objective problem and prov...
Chapter
Predicting the trend of stock price movement accurately allows investors to maximize their profits from investments. However, due to the complexity of the stock data, classifiers often make errors, which cause the investors to lose money from failed investments. This study attempts to reduce such risks by focusing on easy-to-classify cases that hav...
Book
This volume constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2019, and 5th International Conference on Fuzzy and Neural Computing, FANCCO 2019, held in Maribor, Slovenia, in July 2019. The 18 full papers presented in this volume were carefully...
Book
Full-text available
This book provides a collection of selected papers presented at the International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA 2019), which was held in Goa, India, on 16–17 August 2019. It covers the latest research trends and advances in the areas of data science, artificial intelligence, neural networks, cogniti...