Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Дуальная оптимизация тоновой аппроксимации монохромных изображений параллельным эволюционно-генетическим поиском

The paper considers the optimization problem of tone approximation for monochrome (for example: in grayscale palette) images. The procedure of tone approximation implies the reduction of approximated image’s number of tones, which are used in image displaying, compared to number of tones in the original image. The point of the procedure optimization consists of minimization of visual quality loses that estimated according to total or mean deviation between the same pixels of original image and approximated one. As a tool of the optimization the hybrid algorithm is used. It was developed and investigated by authors. The hybrid algorithm combines heuristic and deterministic algorithms of searching the best structure of approximating palette according to criterion of deviations minimization. The heuristic algorithm is based on evolutionarily-genetic paradigm. The main goal of heuristic stage is the reduction of search area of approximating palette’s structures that are the closest to optimum. Such role for heuristic stage was defined according to its fast computational time. The goal of deterministic algorithm of directed exhaustive search is to find the nearest extreme for the result that was obtained by previous algorithm. The developed hybrid algorithm allows to provide dual optimization of tone approximation. It means that the algorithm provides a result, in which two different criteria become optimal relative to each other. The current investigation is devoted to consideration of possibility to increase the effectiveness of hybrid algorithm on the level of heuristic stage. The possibility of implementation the parallel model of evolutionarily-genetic algorithm with different settings is considered. The results of initial experiments are discussed and compared with known algorithm of tone approximation.В статье рассматривается оптимизация процедуры тоновой аппроксимации полутоновых (например, в палитре серого цвета) изображений. Процедура тоновой аппроксимации подразумевает сокращение в палитре аппроксимированного изображения количества используемых тонов по сравнению с количеством тонов в палитре исходного изображения. Оптимизация этой процедуры заключается в минимизации потери качества передачи графической информации, которая оценивается суммарным или усредненным по изображению отклонением тонов координатно-идентичных пикселей аппроксимированного изображения от тонов исходного. В качестве инструмента оптимизации предлагается гибридный алгоритм, который совмещает эвристический и детерминированный алгоритмы поиска наилучшей по критерию минимизации ошибки аппроксимации структуры аппроксимирующей палитры. Эвристический алгоритм реализован на основе эволюционно-генетической парадигмы. Его задачей является поиск области тоновых структур аппроксимирующей палитры, максимально близких к оптимальной. Цель детерминированного алгоритма направленного перебора — найти ближайший к полученному предыдущим поиском результату экстремум критерия качества аппроксимации. Эвристический алгоритм, как более быстродействующий, нацелен на оперативное сокращение области поиска, а детерминированный, как более затратный, — на нахождение хотя бы локального экстремума (а, возможно, и глобального) по максимально сокращенному предыдущим алгоритмом пути. Совместная работа этих алгоритмов позволяет обеспечить процессу тоновой аппроксимации эффект оптимизации, названный в статье дуальной. Под этим термином подразумевается получение результата, при котором достигается экстремум критерия качества аппроксимации при минимизации времени его достижения. Описываемое в статье исследование посвящено повышению результативности гибридного алгоритма на эвристическом этапе, в качестве которого используется модифицированный эволюционно-генетический алгоритм. Рассматриваются перспективы разработки и оценки эффективности внедрения модели параллельного использования алгоритмов с различными параметрами настройки. Обсуждаются первичные эксперименты, а их результаты сравниваются с известным алгоритмом решения поставленной задачи

The enhanced best performance algorithm for global optimization with applications.

Doctor of Philosophy in Computer Science. University of KwaZulu-Natal, Durban, 2016.Abstract available in PDF file

Renewables and energy storage to optimise the amount of electrical energy consumed in a building.

Masters Degree. University of KwaZulu-Natal, Durban.The rising cost of electricity and fuel, along with the looming threat of load shedding has frustrated not only the business owner but the homeowner as well. The need to reduce costs, and the growing pressure for companies and individuals to become more environmentally friendly, is becoming more apparent. To reduce costs and the effects of load shedding, and to become more sustainable, the integration of renewable energy is a clear solution. The solution has led to the investigation of a hybrid system that uses grid-supplied power and renewable energy supplied power which will achieve an effective and efficient optimization of cost. This dissertation is centred on minimising the total cost of ownership over twenty years. This is done by comparing different optimisation algorithms and identifying a cost-effective way of integrating a source of renewable energy, specifically solar energy, with an existing grid-supplied building. The zoning of buildings was found to have an impact on the total cost of ownership as the tariffs were different. By developing a function, the efficiency of a system was quantified based on the load, and what type of building it was. The load has a direct impact on the total cost of ownership. The electrical energy used in a building, and the property type, whether industrial, commercial or residential zone, affects the optimisation algorithm that is used. To minimise the total cost of ownership over twenty years, consideration was given to trade-offs between the available solar, oversizing the PV installation, the cost of electricity at different hours and the use of a storage system. To ensure that the total cost of ownership was correct, financial equations for growing annuity and the prescribed rates for assets, maintenance, and electricity were used. Further to this, South African energy tariffs, actual prices of inverters, solar panels, batteries and solar data of South Africa was used. MATLAB was the application of choice of software due to its optimisation capabilities. Examples of each type of building were analysed to find the optimisation that returned the lowest TCO. Particle Swarm Optimisation, when used for industrial buildings produced the lowest TCO, while smaller loads from commercial buildings and a residential housing, showed that the lowest TCO came from Teaching-Learning Based Optimisation. In each case, the fastest and slowest optimisation technique was Pattern Search and Firefly Optimisation respectively

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Studies in heuristics for the annual crop planning problem.

M. Sc. University of KwaZulu-Natal, Durban 2012.Increase in the costs associated with agricultural production and the limited availability of resources have amplified the need for optimized solutions to the problem of crop planning. The increased costs have imparted negatively on both the cost of production as well as the sale prices of finished products to consumers, with the resultant effects on the socio-economic livelihoods of people around the world. This has increased the burden of poverty, malnutrition, diseases and other types of social problems. The limited availability of land, irrigated water and other resources in crop planning therefore demand optimal solutions to the problem of crop planning, in order to maintain the desired level of profitable outputs that do not strain available resources while still meeting the demands of consumers. Incidentally, the current situation is such that crop producers are required to generate more output per area of crops cultivated within the ambit of the available resources for crop production. This creates a great challenge both for farmers and researchers. Interesting, the problem is essentially an optimization problem hence a challenge to researchers in mathematical and computing science. Notably within the agricultural sector, achieving efficient use of irrigated water demands that optimized solutions be found for its usage during crop planning and production. Incidentally, increase in population growth and limited availability of fresh water has increased the demand of fresh water supply from all sectors of the economy. This has increased the pressure on the agricultural sector as being one of the primary users of fresh water supply to use irrigated water more efficiently. This is to minimize excessive water wastage. It has therefore become very important that optimized solutions be found to the allocation and use of the irrigated water, for water conservational purposes. This is also a very essential key to crop planning decisions. Therefore, in order to determine good solutions to crop planning decisions, this study dwells on a fairly new but important area of agricultural planning, namely the Annual Crop Planning (ACP) problem which essentially focuses at the level of an irrigation scheme. The study presents a model of the ACP problem that helps to determine solutions to resource allocations amongst the various competing crops that are required to be grown at an irrigation scheme within a year. Both new and existing irrigation schemes are considered. Determining solutions for an ACP problem requires that the requirements and constraints presented by crop characteristics, climatic conditions, market demand conditions and the variable costs associated with agricultural production are observed. The objective is to maximize the total gross profits that can be earned in producing the various crops within a production year. Due to the complexity involved in determining solutions for an ACP problem, exact methods are not researched in this study. Rather, to determine near-optimal solutions for this -Hard optimization problem, this research introduces three new Local Search (LS) metaheuristic algorithms. These algorithms are called the Best Performance Algorithm (BPA), the Iterative Best Performance Algorithm (IBPA) and the Largest Absolute Difference Algorithm (LADA). The motivation for implementing these algorithms is to investigate techniques that can be used to determine effective solutions to difficult optimization problems at low computational costs. This study also investigates the performances of three recently introduced swarm intelligence (SI) metaheuristic algorithms in determining solutions to the ACP problems studies. These algorithms have shown great strength in providing competitive solutions to similar optimization problems in literature, hence their use in this work. To the best of the researchers’ knowledge, this is the first work that reports comparative study of the performances of these particular SI algorithms in determining solutions to a crop planning problem. Interesting results obtained and reported herein show the viability, effectiveness and efficiency of incorporation proven metaheuristic techniques into any decision support system that will help determine solutions to the ACP problem

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Bat echolocation-inspired algorithms for global optimisation problems

Optimisation according to the definition of Merriam-Webster Dictionary is an act, process, or methodology of making something (as a design, system, or decision) as fully perfect, functional, or effective as possible. In general, optimisation is the process of obtaining either the best minimum or maximum result under specific circumstance. The optimisation process engages with defining and examining objective or fitness function that suits some parameters and constraints. Nowadays, a vast range of business, management and engineering applications utilise the optimisation approach to save time, cost and resources while gaining better profit, output, performance and efficienc

Genetic Engineering

Leading scientists from different countries around the world contributed valuable essays on the basic applications and safety, as well as the ethical and moral considerations, of the powerful genetic engineering tools now available for modifying the molecules, pathways, and phenotypes of species of agricultural, industrial and even medical importance. After three decades of perfecting such tools, we now see a refined technology, surprisingly unexpected applications, and matured guidelines to avoid unintentional damage to our and other species, as well as the environment, while trying to contribute to solve the biological, medical and technical challenges of society and industry. Chapters on thermo-stabilization of luciferase, engineering of the phenylpropanoid pathway in a species of high demand for the paper industry, more efficient regeneration of transgenic soybean, viral resistant plants, and a novel approach for rapidly screening properties of newly discovered animal growth hormones, illustrate the state-of-the-art science and technology of genetic engineering, but also serve to raise public awareness of the pros and cons that this young scientific discipline has to offer to mankind