31 research outputs found
Chaos embedded opposition based learning for gravitational search algorithm
Due to its robust search mechanism, Gravitational search algorithm (GSA) has
achieved lots of popularity from different research communities. However,
stagnation reduces its searchability towards global optima for rigid and
complex multi-modal problems. This paper proposes a GSA variant that
incorporates chaos-embedded opposition-based learning into the basic GSA for
the stagnation-free search. Additionally, a sine-cosine based chaotic
gravitational constant is introduced to balance the trade-off between
exploration and exploitation capabilities more effectively. The proposed
variant is tested over 23 classical benchmark problems, 15 test problems of CEC
2015 test suite, and 15 test problems of CEC 2014 test suite. Different
graphical, as well as empirical analyses, reveal the superiority of the
proposed algorithm over conventional meta-heuristics and most recent GSA
variants.Comment: 33 pages, 5 Figure
Deep reinforcement learning for attacking wireless sensor networks
Recent advances in Deep Reinforcement Learning allow solving increasingly complex problems. In this work, we show how current defense mechanisms in Wireless Sensor Networks are vulnerable to attacks that use these advances. We use a Deep Reinforcement Learning attacker architecture that allows having one or more attacking agents that can learn to attack using only partial observations. Then, we subject our architecture to a test-bench consisting of two defense mechanisms against a distributed spectrum sensing attack and a backoff attack. Our simulations show that our attacker learns to exploit these systems without having a priori information about the defense mechanism used nor its concrete parameters. Since our attacker requires minimal hyper-parameter tuning, scales with the number of attackers, and learns only by interacting with the defense mechanism, it poses a significant threat to current defense procedures
Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms
Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
Multi-objective swarm intelligence-based (MOSI-based) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) with conflicting objectives. Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. The reference point is determined by the decision maker to guide the search process to a particular region in the true Pareto front. However, HHMO algorithm produces a poor approximation to the Pareto front because lack of information sharing in its population update strategy, equal division of convergence parameter and randomly generated
initial population. A two-step enhanced non-dominated sorting HHMO (2SENDSHHMO) algorithm has been proposed to solve this problem. The algorithm includes (i) a population update strategy which improves the movement of hawks in
the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. The population update strategy calculates a new position of hawks based on the flush-and-ambush technique of Harris’s hawks, and selects the best hawks based on the non-dominated sorting approach. The adjustment strategy enables the parameter to adaptively changed based on the state of the search space. The initial population is produced by generating quasi-random numbers using Rsequence followed by adapting the partial opposition-based learning concept to improve the diversity of the worst half in the population of hawks. The performance of the 2S-ENDSHHMO has been evaluated using 12 MOPs and three engineering MOPs. The obtained results were compared with the results of eight state-of-the-art
multi-objective optimization algorithms. The 2S-ENDSHHMO algorithm was able to generate non-dominated solutions with greater convergence and diversity in solving most MOPs and showed a great ability in jumping out of local optima. This indicates the capability of the algorithm in exploring the search space. The 2S-ENDSHHMO algorithm can be used to improve the search process of other MOSI-based algorithms and can be applied to solve MOPs in applications such as structural design and signal processing
Computational Optimizations for Machine Learning
The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity
Non-determinism in the narrative structure of video games
PhD ThesisAt the present time, computer games represent a finite interactive system. Even in their more experimental forms, the number of possible interactions between player and NPCs (non-player characters) and among NPCs and the game world has a finite number and is led by a deterministic system in which events can therefore be predicted. This implies that the story itself, seen as the series of events that will unfold during gameplay, is a closed system that can be predicted a priori. This study looks beyond this limitation, and identifies the elements needed for the emergence of a non-finite, emergent narrative structure. Two major contributions are offered through this research. The first contribution comes in the form of a clear categorization of the narrative structures embracing all video game production since the inception of the medium. In order to look for ways to generate a non-deterministic narrative in games, it is necessary to first gain a clear understanding of the current narrative structures implemented and how their impact on users’ experiencing of the story. While many studies have observed the storytelling aspect, no attempt has been made to systematically distinguish among the different ways designers decide how stories are told in games. The second contribution is guided by the following research question: Is it possible to incorporate non-determinism into the narrative structure of computer games? The hypothesis offered is that non-determinism can be incorporated by means of nonlinear dynamical systems in general and Cellular Automata in particular
Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation
Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases
Program and Proceedings: The Nebraska Academy of Sciences 1880-2010
PROGRAM
FRIDAY, APRIL 23, 2010
REGISTRATION FOR ACADEMY, Lobby of Lecture wing, Olin Hall
Aeronautics and Space Science, Session A, Olin 249
Aeronautics and Space Science, Session B, Olin 224
Chemistry and Physics, Section A, Chemistry, Olin A
Collegiate Academy, Biology Session A, Olin B
Collegiate Academy, Chemistry and Physics, Session A, Olin 324
Biological and Medical Sciences, Session A, Olin 112
Biological and Medical Sciences, Session B, Smith Callen Conference Center
Chemistry and Physics, Section B, Physics, Planetarium
History and Philosophy of Science, Olin 325
Junior Academy, Judges Check-In, Olin 219
Junior Academy, Senior High REGISTRATION, Olin Hall Lobby
NWU Health and Sciences Graduate School Fair, Olin and Smith Curtiss Halls
Junior Academy, Senior High Competition, Olin 124, Olin 131
Aeronautics and Space Science, Poster Session, Olin 249
Teaching of Science and Math, Olin 325
MAIBEN MEMORIAL LECTURE, OLIN B
Dr. Mark Greip, Vice-Chair, Department of Chemistry, University of Nebraska-Lincoln
LUNCH, PATIO ROOM, STORY STUDENT CENTER
(pay and carry tray through cafeteria line, or pay at NAS registration desk)
Aeronautics Group, Conestoga Room
Anthropology, Olin 111
Biological and Medical Sciences, Session C, Olin 112
Biological and Medical Sciences, Session D, Smith Callen Conference Center
Chemistry and Physics, Section A, Chemistry, Olin A
Chemistry and Physics, Section B, Physics, Planetarium
Collegiate Academy, Biology Session A, Olin B
Collegiate Academy, Biology Session B, Olin 249
Collegiate Academy, Chemistry and Physics, Session A, Olin 324
Junior Academy, Judges Check-In, Olin 219
Junior Academy, Junior High REGISTRATION, Olin Hall Lobby
Junior Academy, Senior High Competition, (Final), Olin 110
Earth Science, Olin 224
Junior Academy, Junior High Competition, Olin 124, Olin 131
NJAS Board/Teacher Meeting, Olin 219
Junior Academy, General Awards Presentations, Smith Callen Conference Center
BUSINESS MEETING, OLIN B
SOCIAL HOUR for Members, Spouses, and Guests
First United Methodist Church, 2723 N 50th Street, Lincoln, NE
ANNUAL BANQUET and Presentation of Awards and Scholarships
First United Methodist Church, 2723 N 50th Street, Lincoln, N