Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm

The decision support model of mean-lower semi-absolute deviation (MLSAD) and the optimization algorithm of group search optimizer with intraspecific competition and lévy walk (GSOICLW) are presented to solve wind-thermal power system dispatch. MLSAD model takes the profit and downside risk into account simultaneously brought by uncertain wind power. Using a risk tolerance parameter, the model can be converted to a single-optimization problem, which is solved by an improved optimization algorithm, GSOICLW. Afterwards, both the model and the algorithm are tested on a modified IEEE 30-bus power system. Simulation results demonstrate that the MLSAD model can well solve wind-thermal power system dispatch. The study also verifies GSOICLW obtains better convergent dispatching solutions, in comparison with other evolutionary algorithms, such as group search optimizer and particle swarm optimizer.NRF (Natl Research Foundation, S’pore)EDB (Economic Devt. Board, S’pore)Accepted versio

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature

A Harris Hawks Optimization Based Single- and Multi-Objective Optimal Power Flow Considering Environmental Emission

The electric sector is majorly concerned about the greenhouse and non-greenhouse gas emissions generated from both conventional and renewable energy sources, as this is becoming a major issue globally. Thus, the utilities must adhere to certain environmental guidelines for sustainable power generation. Therefore, this paper presents a novel nature-inspired and population-based Harris Hawks Optimization (HHO) methodology for controlling the emissions from thermal generating sources by solving single and multi-objective Optimal Power Flow (OPF) problems. The OPF is a non-linear, non-convex, constrained optimization problem that primarily aims to minimize the fitness function by satisfying the equality and inequality constraints of the system. The cooperative behavior and dynamic chasing patterns of hawks to pounce on escaping prey is modeled mathematically to minimize the objective function. In this paper, fuel cost, real power loss and environment emissions are regarded as single and multi-objective functions for optimal adjustments of power system control variables. The different conflicting framed multi-objective functions have been solved using weighted sums using a no-preference method. The presented method is coded using MATLAB software and an IEEE (Institute of Electrical and Electronics Engineers) 30-bus. The system was used to demonstrate the effectiveness of selective objectives. The obtained results are compared with the other Artificial Intelligence (AI) techniques such as the Whale Optimization Algorithm (WOA), the Salp Swarm Algorithm (SSA), Moth Flame (MF) and Glow Warm Optimization (GWO). Additionally, the study on placement of Distributed Generation (DG) reveals that the system losses and emissions are reduced by an amount of 9.8355% and 26.2%, respectively

USING VIRTUAL REALITY TO INVESTIGATE ‘PROTEAN’ ANTI-PREDATOR BEHAVIOUR

Prey animals have evolved a wide variety of behaviours to combat the threat of predation, many of which have received considerable empirical and theoretical attention and are generally well understood in terms of their function and mechanistic underpinning. However, one of the most commonly observed and taxonomically widespread antipredator behaviours of all has, remarkably, received almost no experimental investigation: so-called ‘protean’ behaviour. This is defined as ‘behaviour that is sufficiently unpredictable to prevent a predator anticipating in detail the future position or actions of its prey’. In this thesis, I have elucidated the mechanisms that allow protean behaviour to be an effective anti-predatory response. This was explored with two approaches. Firstly, through the novel and extremely timely use of virtual reality to allow human ‘predators’ to attack and chase virtual prey in three-dimensions from a first-person perspective, thereby bringing the realism that has been missing from previous studies on predator-prey dynamics. Secondly through the three-dimensional tracking of protean behaviour in a highly tractable model species, the painted lady butterfly (Vanessa cardui). I explored this phenomenon in multiple contexts. Firstly, I simulated individual protean prey and explored the effects of unpredictability in their movement rules with respect to targeting accuracy of human ‘predators’ in virtual reality. Next, I examined the concept of ‘protean insurance’ via digitised movements of the painted lady butterfly, exploring the qualities of this animals’ movement paths related to human targeting ability. I then explored how the dynamics of animal groupings affected protean movement. Specifically, I investigated how increasing movement path complexity interacted with the well-documented ‘confusion effect’. I explored this question using both an experimental study and a VR citizen science game disseminated to the general public via the video game digital distribution service ‘Steam’. Subsequently, I explored another phenomenon associated with groupings of prey items; the ‘oddity effect’, which describes the preferential targeting of phenotypically odd individuals by predators. Typically, this phenomenon is associated with oddity of colouration or size. In this case, I investigated whether oddity of protean movement patterns relative to other group members could induce a ‘behavioural oddity effect’. Finally, I used a specialised genetic algorithm (GA) that was driven by human performance with respect to targeting prey items. I investigated the emergent protean movement paths that resulted from sustained predation pressure from humans. Specifically, I examined the qualities of the most fit movement paths with respect to control evolutions that were not under the selection pressure of human performance (randomised evolution). In the course of this thesis, I have gained a deeper understanding of a near ubiquitous component of predator prey interactions that has until recently been the subject of little empirical study. These findings provide important insights into the understudied phenomenon of protean movement, which are directly applicable to predator –prey dynamics within a broad range of taxa

Nutrient drivers and movement ecology of wild Bornean orangutan (Pongo pygmaeus wurmbii) foraging choices

Understanding why animals make the foraging choices they do has been an interdisciplinary research goal for decades. This question is especially salient in biological anthropology, as we seek to understand how the human diet evolved by looking to non-human primate models. Optimal Foraging Theory (OFT), and more recently the Geometric Framework for Nutrition (GF) and Movement Ecology paradigms provide models and heuristics that aid in our understanding of what drives the foraging decisions of animals. Yet until recently, most research examining the foraging decisions of frugivorous herbivores has focused on the OFT based strategy of maximizing and obtaining fruit foods. My research examines alternative nutrient priorities in a frugivorous primate, the Bornean orangutan (Pongo pygmaeus wurmbii), to determine whether fruit-seeking and energy maximization form a sufficient explanation of foraging behavior. Using behavioral and geospatial data from full-day focal animal follows collected in 2015-2016 in Gunung Palung National Park, I first demonstrate that orangutans leave available fruit resources to eat non-fruit foods, suggesting that orangutans are intentionally seeking out non-fruit foods, and clearing the way for a foraging model beyond strict energy maximization via fruit seeking. Further, I find that orangutans do consume non-fruit food when fruit is in visual proximity. I next test whether the Marginal Value Theorem (MVT) of OFT can account for these fruit patch departures by testing one critical assumption of MVT – that feeding rates in a patch decrease over time. Feeding rates did not decrease over patch residence, thus MVT does not explain orangutan fruit patch departures. Instead, I find that orangutans maintain an average 10:1 ratio of non-protein energy to protein, and prioritize protein intake. These findings could explain fruit departure. Geospatial data suggest that fruit is not the only goal of orangutan foraging and that these apes navigate to other food types, in particular, leaves. Taken together, these findings suggest that GF provides a good explanation of orangutan foraging in tandem with OFT energy maximization. I discuss the similarities in nutrient goals between orangutans, modern humans, and extinct hominins, and the conservation implications of my research.2021-06-19T00:00:00