Hybrid tabu search – strawberry algorithm for multidimensional knapsack problem

Multidimensional Knapsack Problem (MKP) has been widely used to model real-life combinatorial problems. It is also used extensively in experiments to test the performances of metaheuristic algorithms and their hybrids. For example, Tabu Search (TS) has been successfully hybridized with other techniques, including particle swarm optimization (PSO) algorithm and the two-stage TS algorithm to solve MKP. In 2011, a new metaheuristic known as Strawberry algorithm (SBA) was initiated. Since then, it has been vastly applied to solve engineering problems. However, SBA has never been deployed to solve MKP. Therefore, a new hybrid of TS-SBA is proposed in this study to solve MKP with the objective of maximizing the total profit. The Greedy heuristics by ratio was employed to construct an initial solution. Next, the solution was enhanced by using the hybrid TS-SBA. The parameters setting to run the hybrid TS-SBA was determined by using a combination of Factorial Design of Experiments and Decision Tree Data Mining methods. Finally, the hybrid TS-SBA was evaluated using an MKP benchmark problem. It consisted of 270 test problems with different sizes of constraints and decision variables. The findings revealed that on average the hybrid TS-SBA was able to increase 1.97% profit of the initial solution. However, the best-known solution from past studies seemed to outperform the hybrid TS-SBA with an average difference of 3.69%. Notably, the novel hybrid TS-SBA proposed in this study may facilitate decisionmakers to solve real applications of MKP. It may also be applied to solve other variants of knapsack problems (KPs) with minor modifications

Residential Demand Side Management model, optimization and future perspective: A review

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Asymmetrical three-phase fault evaluation in a distribution network using the genetic algorithm and the particle swarm optimisation

Abstract: Modern electric power systems are made up of three main sub-systems: generation; transmission; and distribution. The most common faults in distribution sub-systems are asymmetrical three-phase short circuit faults due to the fact that asymmetrical three-phase faults can be: line-to-line faults; two lines-to-earth faults; and single line-to-earth faults. This increases their probability of occurrence, unlike symmetrical three-phase faults which can only occur when all the three phases have been simultaneously shorted. Standard IEC 60909 and IEC 61363 provide all the basic information that is used for the detection of short circuit faults. However, the two standards use numerous estimates in their faults evaluation procedures. They estimate voltage factors (c), impedance correction factors (k), resistance to reactance ratios (R/X), resistance to impedance ratios (R/Z) and various other scaling factors for rotating machines. These IEC estimates are not evenly distributed throughout the 550kV and as such, they do not sufficiently cater for every nominal voltage. When the need arises, the user has to estimate these values accordingly. This research presents a genetic algorithm (GA) and a particle swarm optimisation (PSO) for the detection of asymmetrical three-phase short circuit faults within electric distribution networks of power systems with nominal voltages less than 550kV. GA and PSO are nature-inspired optimisation techniques. Although PSO has quick convergence, it suffers from partial optimism and premature stagnation. Some innovative coding adjustments were made in the creation of initial positions and particle distribution within the swarm. The GA struggles with: survival rates of individuals; stalling during optimisation; and proper gene replacements. Coding adjustments were also made to GA with regards to: strategic gene replacements; crossover when combining the properties of parents; and the arrangement of scores and expectation. Pattern search and Fmincon algorithms were also added to both algorithms as minimisation functions that commence after the evolutionary algorithms (EAs) terminate. The EAs were initially tested on the Rastrigin and Rosenbrock functions to ensure their efficiencies. During fault detection, the developed EAs were used to stochastically determine some of the most crucial estimates (R/X and R/Z ratios). The proposed methodology would compute these values on a case-to-case basis for every optimisation case with regards to the parameters and unique specifications of the power system. The EAs were tested on a nominal voltage that is properly catered for by Standard IEC. They obtained ratios, impedances and currents that were within an approximate range to the IEC values for that nominal voltage. This further implies that EAs can be reliably used to: stochastically determine these ratios; compute impedances; and detect fault currents for all the nominal voltages including those that are not sufficiently catered for by Standard IEC. Since R/X and R/Z ratios play a key role in determining the upstream and fault point impedances, the proposed methodology can be used to compute much more precise fault magnitudes at various network levels thereby setting up and repairing power systems sufficiently.M.Ing. (Electrical and Electronic Engineering Science