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

P-expert : integrated expert advisory system for control and management of parthenium weed infestation

This paper discusses the problem of parthenium weed infestation in Queensland, Australia and describes P-Expert, an expert advisory system designed to provide expert knowledge in control and management strategies of parthenium weed. P-Expert is fundamentally a hybrid fuzzy expert system incorporating technologies from fuzzy logic, relational database and multimedia systems. The primary topic of this paper will be a description of the framework of P-Expert and its overall infrastructure, focusing on five main areas: (1) layered component architecture, (2) discourse semantics (explanatory capabilities), (3) meta-consequent fuzzy functions, (4) fuzzy forecasting of weed infestation, and (5) deployment of large scale expert advisory system

A large-scale agro decision support system : framework for (physical) fusion of a multi-input and multi-output hybrid system

This paper explores a large-scale agro decision support system (DSS) framework to facilitate the fusion of (physical) components to allow integration of multi-input and multi-output of a hybrid system. Decision support system in agriculture sectors are often dependant on multiple input sources such as remote sensing, satellite imagery and external databases, requiring different techniques to consolidate the fragmented pieces of data into a cohesive output. The challenge of this is also often compounded by the requirement that the output from such a decision support system to provide multiple outputs. This paper will detail the framework that allows multi-input and multi-output sub-systems to coexist

Thematic fuzzy prediction of weed dispersal using spatial dataset

This paper demonstrates the framework and methodology of how weed population dynamics can be predicted using rule-base fuzzy logic as applied to GIS spatial image. Parthenium weed (parthenium hysterophorus L.) infestation in the Central Queensland region poses a serious threat to the environment and to the economic viability of the infested areas. Government agencies have taken steps to control and manage existing infestation and to curb future spread of this noxious weed. One of the tools used in these strategies is the prediction of parthenium weed population. Conventional weed forecasting methods utilises discrete values in exponential models and linear algorithms extensively. Attempts at predicting weed dispersal relied heavily on accuracy of the original charts or images to yield reasonable results. Using these methods, results of weed population forecasting are only as reliable as the data originally provided. This paper demonstrates that by using GIS spatial image categorised into themes, a fuzzy logic based forecasting methodology can be performed. Fuzzy logic is best suited to this type of problem because of its ability to handle approximate data

Prediction of weed dispersal using fuzzy logic on spatial image

This paper demonstrates that by using GIS spatial image categorised into themes, a fuzzy logic based forecasting methodology can be performed. Fuzzy logic is best suited to this type of problem as its requisite is in handling approximate data. The methodology would allow us to: 1) predict weed population reasonably well based on approximate data, 2) take into consideration additional parameters without re-writing the algorithm, 3) refine large-scale forecasts to suit localised situations, 4) allow users to determine and inspect individual infestation factors, and 5) adapt the methodology to other weed or plant species. This paper also briefly introduces a new fuzzy If-Then operand, Case-Of, specifically used in this methodology

Fast terminal sliding-mode control design for nonlinear dynamical systems

In this brief, a fast terminal dynamics is proposed and used in the design of the sliding-mode control for single-input single-output nonlinear dynamical systems. The inherent dynamic properties of the fast terminal sliding modes are explored and conditions to ensure its applicability for control designs are obtained

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

P-expert: Framework for fuzzy expert advisory system for the control and management of parthenium weed

This paper discusses the problem of parthenium weed infestation in Queensland and describes P-expert, a fuzzy expert advisory system designed to provide expert knowledge in control and management strategies of parthenium weed. P-Expert is fundamentally a hybrid fuzzy expert system incorporating technologies from fuzzy logic, relational database and multimedia systems. The primary topic of this paper will be a description of the framework of P-Expert

Applied decision support with soft computing

Soft computing has provided sophisticated methodologies for the development of intelligent decision support systems. Fast advances in soft computing technologies, such as fuzzy logic and systems, artificial neural networks and evolutionary computation, have made available powerful problem representation and modelling paradigms, and learning and optimisation mechanisms for addressing modern decision making issues. This book provides a comprehensive coverage of up-to-date conceptual frameworks in broadly perceived decision support systems and successful applications. Different from other existing books, this volume predominately focuses on applied decision support with soft computing. Areas covered include planning, management finance and administration in both the private and public sectors. --Back cover

Remote sensing in decision support systems : using fuzzy post adjustment in localisation of weed prediction

This paper explores the post adjustment of input data from a remote source to fit localised weed prediction for the control and management of weed infestation. The deployment of decision support systems in agricultural sectors often require refinement of its results to adapt to data that has been acquired externally via remote sensing. This paper will detail the fuzzy meta-consequent functions to facilitate the post adjustment. A case study is presented to demonstrate the workability of such fuzzy post-adjustment in the prediction of weed infestation