Penerapan Algoritma Genetika pada Pencarian Solusi Eight Puzzle

Algoritma Genetika (GA) dapat dikatakan sebagai metode untuk mengoptimalkan alat pencarian untuk masalah sulit berdasarkan prinsip seleksi genetika. Selain optimasi juga melayani tujuan pembelajaran mesin dan untuk. Penelitian dan pengembangan. Kecerdasan Buatan banyak digunakan dalam permainan atau games. Salah satu kegunaan dari aplikasi kecerdasan buatan adalah untuk mencari solusi dari suatu permasalahan dalam permainan Penerapan aplikasi kecerdasan salah satunya melalui pencarian solusi dalam eight puzzle. Pada penelitian kali ini dibahas salah satu algoritma yang sering digunakan di kecerdasan buatan yaitu algoritma genetika. Penggunaan algoritma genetika ini, penelitian ini bertujuan untuk mengaplikasikan algoritma tersebut untuk mencari solusi dalam eight puzzle. Teknik penggunaan algoritma genetika dapat diimplementasikan pada kasus eight puzzle

Offine/online Optimum Routing of a UAV using Auxiliary Points

This paper presents a method to determine the route of a three-dimensional UAV. Three criteria; the height, the length of flight path and the unauthorized areas are used as the constraints and combined in a fuzzy function as the evaluation function. The article aimed to discover a minimum cost route from source to destination considering the constrains. In this paper a new searching method is proposed, with use of auxiliary points. The auxiliary point method iteratively divides a straight line to two shorter lines with less cost of evaluation function. Implementation results show that the proposed method dramatically decreases the calculations; meanwhile the ight route is sub-optimum

RSCCGA: Resource Scheduling for Cloud Computing by Genetic Algorithm

Cloud computing, also known as on-the-line computing, is a kind of Internet-based computing that provides shared processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources, which can be rapidly provisioned and released with minimal management effort. Cloud computing and storage solutions provide users and enterprises with various capabilities to store and process their data in third-party data centers. It relies on sharing of resources to achieve coherence and economy of scale, similar to a utility (like the electricity grid) over a network. the scheduling problem is an important issue in the management of resources in the cloud, because despite many requests the data center there is the possibility of scheduling manually. Therefore, the scheduling algorithms play an important role in cloud computing, because the goal of scheduling is to reduce response times and improve resource utilization. The computing resources, either software or hardware, are virtualized and allocated as services from providers to users. The computing resources can be allocated dynamically upon the requirements and preferences of consumers. Traditional system-centric resource management architecture cannot process the resource assignment task and dynamically allocate the available resources in a cloud computing environment. This paper proposed a resource scheduling model for cloud computing based on the genetic algorithm. Experiments show that proposed method has more performance than other methods.Keywords: Cloud Computing, Resource Management, Scheduling, Bandwidth Consumption, Waiting Time, Genetic algorith

D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments

Finding the optimum path for a robot for moving from start to the goal position through obstacles is still a challenging issue. This paper presents a novel path planning method, named D-point trigonometric, based on Q-learning algorithm for dynamic and uncertain environments, in which all the obstacles and the target are moving. We define a new state, action and reward functions for the Q-learning by which the agent can find the best action in every state to reach the goal in the most appropriate path. The D-point approach minimizes the possible number of states. Moreover, the experiments in Unity3D confirmed the high convergence speed, the high hit rate, as well as the low dependency on environmental parameters of the proposed method compared with an opponent approach

A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation

Verification Method for Area Optimization of Mixed - Polarity Reed - Muller Logic Circuits

Area minimization of mixed-polarity Reed-Muller (MPRM) logic circuits is an important step in logic synthesis. While previous studies are mainly based on various artificial intelligence algorithms and not comparable with the results from the mainstream electronics design automation (EDA) tool. Furthermore, it is hard to verify the superiority of intelligence algorithms to the EDA tool on area optimization. To address these problems, a multi-step novel verification method was proposed. First, a hybrid simulated annealing (SA) and discrete particle swarm optimization (DPSO) approach (SADPSO) was applied to optimize the area of the MPRM logic circuit. Second, a Design Compiler (DC) algorithm was used to optimize the area of the same MPRM logic circuit under certain settings and constraints. Finally, the area optimization results of the two algorithms were compared based on MCNC benchmark circuits. Results demonstrate that the SADPSO algorithm outperforms the DC algorithm in the area optimization for MPRM logic circuits. The SADPSO algorithm saves approximately 9.1% equivalent logic gates compared with the DC algorithm. Our proposed verification method illustrates the efficacy of the intelligence algorithm in area optimization compared with DC algorithm. Conclusions in this study provide guidance for the improvement of EDA tools in relation to the area optimization of combinational logic circuits