Application Of Optimisation Methods For Mri Data Segmentation

The presented paper describes a use of metaheuristic algorithm for medical image segmentation. First section is dedicated to a brief introduction to the principles of this kind of segmentation and second section is used for description of used algorithms and used approaches to segmentation. Next sections are used for presentations of achieved results

К планированию маршрутов в 3D-среде с многовариантной моделью

We present the results of the research on the planning of routes of unmanned vehicles (autonomous moving objects). The routing is based on the multivariant route model (MRM) formed a priori as a set of alternative paths from an initial point to the target one.. The MRM construction is done using the computer method of functional voxel modeling, combining the analytical form of describing a 3D-environment with the voxel representation of its local geometrical characteristics. Synthesis of the motion control and stabilization of the path trajectory are done by representing the control object as a multimode model and applying the reduction method to it.В данной статье излагаются результаты исследований по планированию маршрутов автономных подвижных объектов на априорно сформированной многовариантной модели маршрута (МММ) как множестве альтернативных путей из начальной точки в целевую. Построение МММ основывается на компьютерном методе функционально-воксельного моделирования, сочетающем аналитическую форму описания 3D-сцены с воксельным представлением ее локальных геометрических характеристик. Синтез управления движением и стабилизация траектории движения обеспечиваются представлением объекта управления в форме многорежимной модели и применением к ней метода редукции

Anti-Islanding Protection of Distributed Generation Based on Social Spider Optimization Technique

Anti-islanding protection is one of the most important requirements for the connection of Distributed Generators in power systems. This paper proposes a Social Spider Optimization (SSO) algorithm to detect unintentional islanding in power systems with distributed generation. The SSO algorithm is employed to differentiate frequency oscillations in synchronous generator those caused by non-islanding events. The SSO algorithm is based on the forging strategy of social spiders, which generated vibrations spread over the spider web to determine the positions of preys or any other disturbances. The vibrations from the spider are used to detect the occurrence of islanding in the synchronous generator. The SSO algorithm has superior performance when tested with IEEE 34 bus distribution system. The taken test system is evaluated for different scenarios and load distribution. The proposed SSO algorithm detects the islanding and prevents the system from undue tripping and outages. Furthermore, this technique may apply to prevent the system from islanding and maintains the future Indian Distributed Generation (DG) system reliability

Spider Search Algorithms for MIMO System and Assessment Using Simatic PCS7

This paper shows two optimization methods that are built on a spider optimization algorithm to enhance the proportional integral and derivative (PID) gain values for multiple-input-multiple-output (MIMO) arrangement which is automated with SIMATIC PCS7 Distributed Control System (SDCS). The leading methodologies are the Spider Search Algorithm (SSA) and Social Spider Optimization (SSO) which is meant primarily for optimizing PID gain values. The SSA is based on foraging strategy of colonial spiders and SSO works on the combined plan of the male and female spiders that removes the episodes of local optimization and exploration elusion. Thus, SSA and SSO are contrived for the ideal fine-tuning of PID conditions in the benchmark MIMO procedure. The system performance is understood by minimizing the integral absolute error (IAE) and the integral square error (ISE) as its objective functions. The time-domain features are examined for the aforesaid methods and thereafter compared with the previous genetic algorithm (GA). The settling time is 60s for the proposed method which is lesser than the other techniques. For illustrating the implemented controller\u27s strength, interference is manually presented in the real-time system. Findings indicate that the SSO surpasses output measures and performance indices beyond the presupposed SSA and GA intervals

2D Swarm Meerkats Behavior Modelling

Animal behavior is the connection or link between the molecular and physiological aspects of biology and the ecological. Behavior is the bridge between organisms and environment also between the nervous system and the ecosystem. Besides that, behavior is generally the animal's "first line of defense" in response to environmental change. Therefore, careful observation of the behavior can provide us a great information. Behavior is one of the most important features of animal life. As a human, behavior plays a critical role in our lives. This is because behavior is the part of an organism that interacts with its environment. Many problems occur in human society are often related to the interaction between environment or genetics with behavior. The fields of socioecology and animal behavior deal with the issue of environment behavioral interactions at an accurate level and a proximate level. Therefore, social scientists are turning to animal behavior as a framework to interpret human society and to find out possible sources of societal problems. In this study, the foraging behavior of Meerkat will be studied. In this thesis, the foraging behavior of Meerkat will be studied and the parameters for simulation of Meerkats foraging behavior are designed. The designed parameters including the number of agents, number of group, range of perception and number of food. However, there are not much works done on Meerkats therefore, survey form is used in designing these 14 sets of parameters. Only the choices that have higher percentage is focused in designing the 14 sets of parameters for simulation. The performance of each 14 sets of simulation are compared based on the result obtained from the simulations such as the highest mean quality the simulation can achieve and the number of ticks required to reach the highest mean quality. The higher the mean quality the better the performance. The smaller the number of ticks required to reach the highest mean quality the better the performance

Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition

Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). In this research, SSO-FS is used in the EEG-based emotion recognition model as searching method to find optimal feature set to maximize classification performance and mimics the cooperative behaviour and mechanism of social spiders in nature. This proposed feature selection method has been tested on DEAP EEG dataset with six subjects and compared with the most popular heuristic algorithms such as GA, PSO and ABC. The results show that the SSO-FS provides a remarkable and comparable performance compared to other existing methods. Whereby, the max accuracy obtained is 66.66% and 70.83%, the mean accuracy obtained is 55.51±7.17 and 60.97±8.38 for 3-level of valence emotions and 3-level of arousal emotions classification respectively

Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm

In this study, a new swarm intelligence-based algorithm called Social Spider Algorithm (SSA), which is based on a simulation of the collaborative behaviours of spiders, was adapted for the first time for sentiment analysis (SA) within data obtained from Twitter. The SA problem was modelled as a search problem, with datasets considered as the search space and SSA modelled as a search strategy by determining an appropriate encoding scheme and objective function. The success of the SSA was compared with different Machine Learning (ML) algorithms within the same real datasets based on different metrics. Although this study is the first usage of SSA for the SA problem and there is no optimization for it, the attained results were promising and could provide new direction to related research about the use of optimized different artificial intelligence search algorithms for these types of online social network analysis problems. This study also introduced a new application domain for the optimization algorithms

Perfectionism Search Algorithm (PSA): An Efficient Meta-Heuristic Optimization Approach

This paper proposes a novel population-based meta-heuristic optimization algorithm, called Perfectionism Search Algorithm (PSA), which is based on the psychological aspects of perfectionism. The PSA algorithm takes inspiration from one of the most popular model of perfectionism, which was proposed by Hewitt and Flett. During each iteration of the PSA algorithm, new solutions are generated by mimicking different types and aspects of perfectionistic behavior. In order to have a complete perspective on the performance of PSA, the proposed algorithm is tested with various nonlinear optimization problems, through selection of 35 benchmark functions from the literature. The generated solutions for these problems, were also compared with 11 well-known meta-heuristics which had been applied to many complex and practical engineering optimization problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other well-known algorithms