Raymond Chiong

Hybrid filter-wrapper feature selection for short-term load forecasting

Selection of input features plays an important role in developing models for short-term load forecasting (STLF). Previous studies along this line of research have focused pre-dominantly on filter and wrapper methods. Given the potential value of a hybrid selection scheme that includes both filter and wrapper methods in constructing an appropriate pool of features, coupled with the general lack of success in employing filter or wrapper methods individually, in this study we propose a hybrid filter-wrapper approach for STLF feature selection. This proposed approach, which is believed to have taken full advantage of the strengths of both filter and wrapper methods, first uses the Partial Mutual Information based filter method to filter out most of the irrelevant and redundant features, and subsequently applies a wrapper method, implemented via a firefly algorithm, to further reduce the redundant features without degrading the forecasting accuracy. The well-established support vector regression is selected as the modeler to implement the proposed hybrid feature selection scheme. Real-world electricity load datasets from a North-American electric utility and the Global Energy Forecasting Competition 2012 have been used to test the performance of the proposed approach, and the experimental results show its superiority over selected counterparts. A flowchart of the proposed hybrid feature selection method.Display Omitted We propose a filter-wrapper feature selection method for STLF.PMI is first used to filter irrelevant and redundant features.A wrapper method is then used to further reduce the remaining redundant features.The proposed hybrid method can identify less inputs with relatively shorter time.Better forecasting results are obtained based on the selected features.

Evolutionary Optimization: Pitfalls and Booby Traps

Evolutionary computation (EC), a collective name for a range of metaheuristic black-box optimization algorithms, is one of the fastest-growing areas in computer science. Many manuals and “how-to”s on the use of different EC methods as well as a variety of free or commercial software libraries are widely available nowadays. However, when one of these methods is applied to a real-world task, there can be many pitfalls and booby traps lurking — certain aspects of the optimization problem that may lead to unsatisfactory results even if the algorithm appears to be correctly implemented and executed. These include the convergence issues, ruggedness, deceptiveness, and neutrality in the fitness landscape, epistasis, non-separability, noise leading to the need for robustness, as well as dimensionality and scalability issues, among others. In this article, we systematically discuss these related hindrances and present some possible remedies. The goal is to equip practitioners and researchers alike with a clear picture and understanding of what kind of problems can render EC applications unsuccessful and how to avoid them from the start.

Variants of Evolutionary Algorithms for Real-World Applications

Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in recent years. This book Variants of Evolutionary Algorithms for Real-World Applications aims to promote the practitioners view on EAs by providing a comprehensive discussion of how EAs can be adapted to the requirements of various applications in the real-world domains. It comprises 14 chapters, including an introductory chapter re-visiting the fundamental question of what an EA is and other chapters addressing a range of real-world problems such as production process planning, inventory system and supply chain network optimisation, task-based jobs assignment, planning for CNC-based work piece construction, mechanical/ship design tasks that involve runtime-intense simulations, data mining for the prediction of soil properties, automated tissue classification for MRI images, and database query optimisation, among others. These chapters demonstrate how different types of problems can be successfully solved using variants of EAs and how the solution approaches are constructed, in a way that can be understood and reproduced with little prior knowledge on optimisation.

Why Is Optimization Difficult

This chapter aims to address some of the fundamental issues that are often encountered in optimization problems, making them difficult to solve. These issues include premature convergence, ruggedness, causality, deceptiveness, neutrality, epistasis, robustness, overfitting, oversimplification, multi-objectivity, dynamic fitness, the No Free Lunch Theorem, etc. We explain why these issues make optimization problems hard to solve and present some possible countermeasures for dealing with them. By doing this, we hope to help both practitioners and fellow researchers to create more efficient optimization applications and novel algorithms.

An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem

Graphical abstractDisplay Omitted HighlightsWe propose an improved IG algorithm for the no-wait flowshop scheduling problem.The proposed algorithm is incorporated with a Tabu-based reconstruction strategy.Simulation results confirm the advantages of utilizing the new reconstruction scheme.Our algorithm is more effective than other competitive algorithms in the literature.43 new upper bound solutions for the problem have been made available. This paper proposes a Tabu-mechanism improved iterated greedy (TMIIG) algorithm to solve the no-wait flowshop scheduling problem with a makespan criterion. The idea of seeking further improvement in the iterated greedy (IG) algorithm framework is based on the observation that the construction phase of the original IG algorithm may not achieve good performance in escaping from local minima when incorporating the insertion neighborhood search. To overcome this limitation, we have modified the IG algorithm by utilizing a Tabu-based reconstruction strategy to enhance its exploration ability. A powerful neighborhood search method that involves insert, swap, and double-insert moves is then applied to obtain better solutions from the reconstructed solution in the previous step. Empirical results on several benchmark problem instances and those generated randomly confirm the advantages of utilizing the new reconstruction scheme. In addition, our results also show that the proposed TMIIG algorithm is relatively more effective in minimizing the makespan than other existing well-performing heuristic algorithms.

Effects of Iterated Interactions in Multiplayer Spatial Evolutionary Games

Mechanisms promoting the evolution of cooperation in two players and two strategies (22) evolutionary games have been investigated in great detail over the past decades. Understanding the effects of repeated interactions in multiplayer spatial games, however, is a formidable challenge. In this paper, we present a multiplayer evolutionary game model in which agents play iterative games in spatial populations. -player versions of the well-known Prisoners Dilemma and the Snowdrift games are used as the basis of the investigation. These games were chosen as they have emerged as the most promising mathematical metaphors for studying cooperative phenomena. Here, we have adopted an experimental approach to study the emergent behavior, exploring different parameter configurations via numerical simulations. Key model parameters include the cost-to-benefit ratio, the size of groups, the number of repeated encounters, and the interaction topology. Our simulation results reveal that, while the introduction of iterated interactions does promote higher levels of cooperative behavior across a wide range of parameter settings, the cost-to-benefit ratio and group size are important factors in determining the appropriate length of beneficial repeated interactions. In particular, increasing the number of iterated interactions may have a detrimental effect when the cost-to-benefit ratio and group size are small.

Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms

We propose a novel interval time series (ITS) forecasting approach.A fully complex-valued RBF neural network is extended to address ITS forecasting.DPSO/PSO are used to jointly optimize the structure and parameters of the model.Results on simulated and real-world ITS data confirm the efficacy of the approach. Interval time series prediction is one of the most challenging research topics in the field of time series modeling and prediction. In view of the remarkable function approximation capability of fully complex-valued radial basis function neural networks (FCRBFNNs), we set out to investigate the possibility of forecasting interval time series by denoting the lower and upper bounds of the interval as real and imaginary parts of a complex number, respectively. This results in a complex-valued interval. We then model the resulted complex-valued interval time series via a FCRBFNN. Furthermore, we propose to evolve the FCRBFNN by using particle swarm optimization (PSO) and discrete PSO for joint optimization of the structure and parameters. Finally, the proposed interval time series prediction approach is tested with simulated interval time series data as well as real interval stock price time series data from the New York Stock Exchange. Our experimental results indicate that it is a promising alternative for interval time series forecasting.

Collaborative Learning in Online Study Groups: An Evolutionary Game Theory Perspective.

Educational benefits of online collaborative group work have been confirmed in numerous research studies. Most frequently cited advantages include the development of skills of critical thinking and problem solving as well as skills of self-reflection and co-construction of knowledge and meaning. However, the establishment and maintenance of active collaboration in online study groups is a challenging task, primarily due to students’ inability (e.g., owing to time constraints or lack of collaboration skills) or reluctance (e.g., due to the lack of or low participation of other group members) to participate actively in the group work. Aiming to better understand and contribute to the resolution of the problems of effective online group work, we followed a novel approach based on Evolutionary Game Theory (EGT). While EGT has been used extensively as a framework for studying the emergence and maintenance of cooperation in many disciplines, to the best of our knowledge, it has not yet been applied to understanding and facilitating group collaboration in online learning settings. In this paper, we present a study we have conducted in order to investigate whether, and to what extent, EGT can be applied to explain students’ participation in collaborative study groups.

Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem

We introduce an experimentation procedure for evaluating and comparing optimization algorithms based on the Traveling Salesman Problem (TSP). We argue that end-of-run results alone do not give sufficient information about an algorithms performance, so our approach analyzes the algorithms progress over time. Comparisons of performance curves in diagrams can be formalized by comparing the areas under them. Algorithms can be ranked according to a performance metric. Rankings based on different metrics can then be aggregated into a global ranking, which provides a quick overview of the quality of algorithms in comparison. An open source software framework, the TSP Suite, applies this experimental procedure to the TSP. The framework can support researchers in implementing TSP solvers, unit testing them, and running experiments in a parallel and distributed fashion. It also has an evaluator component, which implements the proposed evaluation process and produces detailed reports. We test the approach by using the TSP Suite to benchmark several local search and evolutionary computation methods. This results in a large set of baseline data, which will be made available to the research community. Our experiments show that the tested pure global optimization algorithms are outperformed by local search, but the best results come from hybrid algorithms.

Parallel Machine Scheduling Under Time-of-Use Electricity Prices: New Models and Optimization Approaches

The industrial sector is one of the largest energy consumers in the world. To alleviate the grids burden during peak hours, time-of-use (TOU) electricity pricing has been implemented in many countries around the globe to encourage manufacturers to shift their electricity usage from peak periods to off-peak periods. In this paper, we study the unrelated parallel machine scheduling problem under a TOU pricing scheme. The objective is to minimize the total electricity cost by appropriately scheduling the jobs such that the overall completion time does not exceed a predetermined production deadline. To solve this problem, two solution approaches are presented. The first approach models the problem with a new time-interval-based mixed integer linear programming formulation. In the second approach, we reformulate the problem using Dantzig-Wolfe decomposition and propose a column generation heuristic to solve it. Computational experiments are conducted under different TOU settings and the results confirm the effectiveness of the proposed methods. Based on the numerical results, we provide some practical suggestions for decision makers to help them in achieving a good balance between the productivity objective and the energy cost objective.