Constrained multi-objective particle swarm optimization with application in power generation

This thesis is devoted to the study of metaheuristic optimization algorithms and their application in power generation. The study focuses on constrained multi-objective optimization using Particle Swarm Optimization algorithm. A multi-objective constraint-handling method incorporating a dynamic neighbourhood PSO algorithm is proposed for tackling single objective constrained optimization problems. The benchmark simulation results demonstrate the proposed approach is effective and efficient in finding the consistent quality solutions. Compared with the recent research results, the proposed approach is able to provide better or similar good results in most benchmark functions. The proposed performance-based dynamic neighbourhood topology has proved to be able to help make convergence faster than the static neighbourhood topology. The thesis also presents a modified PSO algorithm for solving multi-objective constrained optimization problems. Based on the constraint dominance concept, the proposed approach defines two sets of rules for determining the cognitive and social components of the PSO algorithm. Simulation results for the four numerical optimization problems demonstrate the proposed approach is effective. The proposed approach has a number of advantages such as being applicable to any number of objective functions and computationally inexpensive. As applications, three engineering design optimization problems and the power generation loading optimization problem are investigated. The simulation results for the engineering design optimization problems and the power generation loading optimization problem reveal the capability, effectiveness and efficiency of the proposed approaches. The methodology can be readily applicable to a broad range of applications

Considerations for online course delivery from educators' perspective

With the rapid development of information technology, and market demands, distance education is becoming increasingly popular for both students and educators because of its flexibility and convenience. The Internet plays a key role for delivering online courses. Operation of online courses involves many players such as administrators, software facilitators, students and instructors. However, what should an academic educator consider when offering an online course? In what forms can the communication between instructors and students most effectively take place? What kinds of assessment are better suited for online course? Based on the authors’ experiences with online course delivery, this paper explores key issues regarding the above questions from an educator’s point of view. It briefly points out the characteristics of online education. Considerations for online course delivery are particularly discussed. It describes what an educator should consider during the four stages known as planning, designing, developing and delivery for an online course. Suggestions are provided as to considerations for online course delivery

A Multi-objective constraint-handling method with PSO algorithm for constrained engineering optimization problems

This paper presents a multi-objective constraint handling method incorporating the Particle Swarm Optimization (PSO) algorithm. The proposed approach adopts a concept of Pareto domination from multi-objective optimization, and uses a few selection rules to determine particles’ behaviors to guide the search direction. A goal-oriented programming concept is adopted to improve efficiency. Diversity is maintained by perturbing particles with a small probability. The simulation results on the three engineering benchmark problems demonstrate the proposed approach is highly competitive

A novel method of curve fitting based on optimized extreme learning machine

In this article, we present a new method based on extreme learning machine (ELM) algorithm for solving nonlinear curve fitting problems. Curve fitting is a computational problem in which we seek an underlying target function with a set of data points given. We proposed that the unknown target function is realized by an ELM with introducing an additional linear neuron to correct the localized behavior caused by Gaussian type neurons. The number of hidden layer neurons of ELM is a crucial factor to achieve a good performance. An evolutionary computation algorithm–particle swarm optimization (PSO) technique is applied to determine the optimal number of hidden nodes. Several numerical experiments with benchmark datasets, simulated spectral data and measured data from high energy physics experiments have been conducted to test the proposed method. Accurate fitting has been accomplished for various tough curve fitting tasks. Comparing with the results of other methods, the proposed method outperforms the traditional numerical-based technique. This work clearly demonstrates that the classical numerical analysis problem-curve fitting can be satisfactorily resolved via the approach of artificial intelligence. © 2020, © 2020 Taylor & Francis

Evolutionary optimisation for power generation unit loading application

Power generation unit loading optimisation is a practically viable tool for efficiency improvement. The objectives for the coal-fired power generation loading optimisation are to minimize fuel consumption and to minimize emissions for a given load demand. This paper presents two models for this significant industrial application. Depending on the environmental regulation, either a single objective constrained model or a multi-objective constrained model can be chosen in practice. A multi-objective constraint-handling method incorporating the constraint dominance concept via Particle Swarm Optimisation (PSO) algorithm has been adopted for problem solving. The simulation results based on a coal-fired power plant demonstrates the capability, effectiveness and efficiency of using the proposed approach in a large scale industrial application

Power generation loading optimization using a multi-objective constraint-handling method via PSO algorithm

Power generation loading optimization problem will be of practical importance in the coming carbon constrained power industry. A major objective for the coal-fired power generation loading optimization is to minimize fuel consumption to achieve output demand and to maintain NOx emissions within the environmental license limit. This paper presents a multi-objective constraint-handling method incorporating the Particle Swarm Optimization (PSO) algorithm for the power generation loading optimization application. The proposed approach adopts the concept of Pare to dominance from multi-objective optimization, and uses several selection rules to determine particles’ behaviors to guide the search direction. The simulation results of the power generation loading optimization based on a coal-fired power plant demonstrates the capability, effectiveness and efficiency of using a multi-objective constraint-handling method with PSO algorithm in solving significant industrial problems

Assessment of the accuracy of representing a helical vortex by straight segments

This paper considers the accuracy of representing a helical vortex, as found in the wakes of helicopters, wind turbines, and propellers, by a sequence of straight segments. The accuracy is assessed by comparison with recent results for the induced velocity of a helix of constant pitch and radius. This comparison is motivated by the small values of the vortex pitch behind wind turbines and hovering rotors; small pitch leads to errors associated with the proximity of subsequent turns of the helix to the control point at which the velocity is required. Three cases are considered. The first, the velocity on the helix axis, has an analytic solution which is used to demonstrate that the general accuracy of the straight segment approximation is second order, as has been found in previous comparisons with the velocity field of a vortex ring. For the second case, where the control point has the same radius as the vortex, the segments aligned with the control point are mainly responsible for the error. The error varies from first to third order as the number of segments per revolution of the helix is increased. Thirdly, the self-induced velocity is determined to within an accuracy comparable with the effects of the vortex structure, of which little is presently known in general. The effects of vortex curvature are not significant and easily dealt with

A survey of computational intelligence in educational timetabling

Timetabling problems have been widely studied, of which Educational Timetabling Problem (ETP) is the biggest section. Generally, ETP can be divided into three modules, namely, course timetabling, school timetabling, and examination timetabling. For solving ETP, many techniques have been developed including conventional algorithms and computational intelligence approaches. Several surveys have been conducted focusing on those methods. Some surveys target on particular categories; some tend to cover all types of approaches. However, there are lack of reviews specifically focusing on computational intelligence in ETP. Therefore, this paper aims at providing a reference of selecting a method for the applications of ETP by reviewing popular computational intelligent algorithms, such as meta-heuristics, hyper-heuristics, hybrid methods, fuzzy logic, and multi-agent systems. The application would be categorised and described into the three types of ETP respectively

Developing an online examination timetabling system using artificial bee colony algorithm in higher education

Educational timetabling is a fundamental problem impacting schools and universities’ effective operation in many aspects. Different priorities for constraints in different educational institutions result in the scarcity of universal approaches to the problems. Recently, COVID-19 crisis causes the transformation of traditional classroom teaching protocols, which challenge traditional educational timetabling. Especially for examination timetabling problems, as the major hard constraints change, such as unlimited room capacity, non-invigilator and diverse exam durations, the problem circumstance varies. Based on a scenario of a local university, this research proposes a conceptual model of the online examination timetabling problem and presents a conflict table for constraint handling. A modified Artificial Bee Colony algorithm is applied to the proposed model. The proposed approach is simulated with a real case containing 16,246 exam items covering 9,366 students and 209 courses. The experimental results indicate that the proposed approach can satisfy every hard constraint and minimise the soft constraint violation. Compared to the traditional constraint programming method, the proposed approach is more effective and can provide more balanced solutions for the online examination timetabling problems

School timetabling optimisation using artificial bee colony algorithm based on a virtual searching space method

Although educational timetabling problems have been studied for decades, one instance of this, the school timetabling problem (STP), has not developed as quickly as examination timetabling and course timetabling problems due to its diversity and complexity. In addition, most STP research has only focused on the educators’ availabilities when studying the educator aspect, and the educators’ preferences and expertise have not been taken into consideration. To fill in this gap, this paper proposes a conceptual model for the school timetabling problem considering educators’ availabilities, preferences and expertise as a whole. Based on a common real-world school timetabling scenario, the artificial bee colony (ABC) algorithm is adapted to this study, as research shows its applicability in solving examination and course timetabling problems. A virtual search space for dealing with the large search space is introduced to the proposed model. The proposed approach is simulated with a large, randomly generated dataset. The experimental results demonstrate that the proposed approach is able to solve the STP and handle a large dataset in an ordinary computing hardware environment, which significantly reduces computational costs. Compared to the traditional constraint programming method, the proposed approach is more effective and can provide more satisfactory solutions by considering educators’ availabilities, preferences, and expertise levels