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

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

Analysis of techniques for mapping environments for fauna survey

A discussion of environmental land classification is presented for the purpose of surveying avifaunal communities. Surveying and mapping land uses the term environment in a special sense. Environment can be mapped into regions whose components are interacting climate terrain, geology, soils and biota. The problem of how to describe the environment for sampling fauna requires an approach which samples the inherent and known variability of all environmental regions present. Discovery of patterns between fauna and environment provide the basis for understanding species/habitat relationships and provides a valuable basis for management or more detailed studies. Two environmental mapping methods commonly employed in faunal survey and management are systematic grids and natural landscape patterns; these were compared to determine their effectiveness for classifying the environment for sampling avifaunal communities. A detailed study was undertaken between 1982-84 in a plot of 8km2 in the Tianjara area. The plot was chosen to encompass a representative sample of the wide range of environments described by Gunn (1985). Analysis of the systematic grids involved sampling a diverse set of environmental attributes into six different grid sizes, including 100m2, 200m2, 300m2, 400m2, 500m2 and 1000m2. Topographic maps and aerial photos provided the sources for measuring the attributes. Results of several analyses showed the 300m2 grid was the most appropriate for the Tianjara area. Analysis of natural landscape patterns involved adoption of the work done by Gunn et al (1984) and led to the preparation of a land unit map for the study plot. Detailed patterns were delineated in 1:27,000 scale air photos and described using the land unit descriptions in Gunn (1985). Results from ground site samples taken to verify the two mapping bases showed that the correspondence between map and ground data was better for sites in systematic grids than for natural landscape patterns. Notwithstanding this, a better understanding of the effects of sampling specific patches of environment was gained from examining sites in natural landscape patterns because it employed a stratified representative sampling strategy, while the systematic grids used a centric systematic sampling strategy. The effect of this was large uniform patches of habitat tended to be more oversampled by sites in systematic grids than was observed for sites in natural landscape patterns. Examination of the relationships between the sampling bases using analyses of environment was not possible because of the lack of sufficient sites in common between the two sampling bases. Comparison of the two sampling bases was, however, possible by using avifaunal data common to both sampling bases. Analysis of the relationships between avifaunal data and environmental groups showed only minor differences between the effectiveness of the two sampling bases to provide practical and realistic descriptions of environment for describing discrete assemblages of birds. The overall conclusion of this study is that any environmental classification, so long as it is based on relevant attributes known to be important for environmental structure and processes, will provide a valuable basis for sampling fauna. A number of points need to be stressed regarding analyses of this type; care needs to be exercised in choosing surrogate environmental attributes between the mapping and ground site data and caution is required when allocating sampling sites to avoid overemphasising area of environmental groups as more important than the inherent variability of the attributes within the environmental groups. An understanding of this problem will greatly improve the nature of sampling fauna in environmental regions

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Leading ethical leaders : higher education institutions, business schools and the sustainable development goals

This volume provides unique and profound insights from within educational institutions in diverse regions of the world on how ‘learning outside’ and ‘learning inside’ can be holistically integrated, so that the sustainable development agenda does not remain static and programmatic, but a creative and permeable framework. The shared hope across the thirteen chapters, which constitute complete original essays on the theme, is to develop meaningful, interdisciplinary curricula and research projects which serve the human community as a whole. The aim of the editors is directed towards a similar United Nations’ valuable ideal: to advance knowledge in respect of the earth and the future generations who will inherit it

Performance analysis for wireless G (IEEE 802.11G) and wireless N (IEEE 802.11N) in outdoor environment

This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. The comparison consider on coverage area (mobility), throughput and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g