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Power optimization based on intelligent algorithm draws more and more attention. This article presents a novel low power optimization strategy based on the high level software power management employing Markov Process for charactering the real running workload. This article formulates workload characterization and selection with stochastic process method, and solves the formula using dynamic voltage...
A connection has recently been drawn between Dynamic Optimization Problems (DOPs) and Reinforcement Learning Problems (RLPs) where they can be seen as subsets of a broader class of Sequential Decision-Making Problems (SDMPs). SDMPs require new decisions on an ongoing basis. Typically the underlying environment changes between decisions. The SDMP view is useful as it allows the unified space to be...
Many real-world applications are characterized by multiple conflicting objectives. In such problems, optimality is replaced by Pareto optimality and the goal is to find the Pareto frontier, a set of solutions representing different compromises among the objectives. Despite recent advances in multi-objective optimization, the selection, given the Pareto frontier, of a Pareto-optimal policy is still...
Combinatorial optimization problems are often very difficult to solve and the choice of a search strategy has a tremendous influence over the solver's performance. A search strategy is said to be adaptive when it dynamically adapts to the structure of the problem instance and identifies the areas of the search space that contain good solutions. We introduce an algorithm (RLBS) that learns to efficiently...
The mobility robustness optimization can significantly enhance the quality of service in scenarios characterized by dense uncoordinated deployment of small cells, as targeted by future 5th generation (5G) radio access technology. Current solutions mostly rely on priori knowledge and rule based algorithms, these solutions do have achieved good performance. There is still, however, a lot of room for...
In an optimization based control approach for solar microgrid energy management, consumer as an agent continuously interacts with the environment and learns to take optimal actions autonomously to reduce the power consumption from grid. Learning is built in directly into the consumer's behaviour so that he can decide and act in his own interest for optimal scheduling. The consumer evolves by interacting...
With the emergence of Self-Organizing Network (SON) functions network operators are faced with a practical problem: coordination of SON instances. The SON functions are usually designed in a standalone manner, i.e. they do not take into account the possibility that other instances of the same or different SON functions may be running in the network. This creates the risk of conflicts and network instability...
We present a framework for reinforcement learning (RL) in a scenario where multiple simulators are available with decreasing amounts of fidelity to the real-world learning scenario. Our framework is designed to limit the number of samples used in each successively higher-fidelity/cost simulator by allowing the agent to choose to run trajectories at the lowest level that will still provide it with...
In this paper we analyze the use of Reinforcement Learning (RL) in control optimization within dynamic multiagent systems. RL is an effective algorithm for single agent optimization but performs less well in dynamic multi-agent environments. We investigate this principle based upon three of the most common RL algorithms. We also introduce a novel RL algorithm that excels in both single agent optimization...
This Paper presents an Oppositional Biogeography-Based Optimization algorithm to solve complex Economic Emission Load Dispatch (EELD) problems of thermal power systems. Emission of NOx and SOx are considered for case studies. The proposed method is a modification over Biogeography-Based Optimization technique, designed to accelerate its convergence rate and to improve the quality of solution. This...
This paper presents a PBIL algorithm based on adaptive theory-giving that the traditional reduction of rough set is not unique and the process lasts for a long time. The learn probability and mutation rate of traditional PBIL algorithm can change adaptively by introducing the Systemic Entropy, then a self-learning and adaptive variability PBIL algorithm (APBIL) is formed. When it is applied to attributes...
Flight Parameters stage classification is the premise of the fault diagnosis and trend forecast based on flight parameters. Stage classification belongs to the classification optimization problem of multi-attribute data through analysis the flight data. This paper carried out the research for the two-class classification based on the semi-supervised learning methods PTSVM (Progressive Transductive...
Probability Collective (PC) is an extension of conventional game theory for distributed optimization by sampling an explicitly parameterized probability distributions over the space of solutions. This parameterization introduces more effective computational models to solve complex systems-level optimization problems. In this paper we present a study of using this collective learning model for adaptive...
This paper presents a distributed algorithm for mobile sensor networks to monitor the environment. With this algorithm, multiple mobile sensor nodes can collectively sample the environmental field and recover the environmental field function via machine learning approaches. The mobile sensor nodes are able to self-organise so that the distribution of mobile sensor nodes matches to the estimated environmental...
The problem of construction the neuronetworking systems for non-stationary information adaptive processing at various practical applications is formulated. The developed methods and algorithms of neural network training subset formation allow to take into account the conditions of information transfer, variation of statistical parameters and dynamic properties of data. The controlling algorithms which...
A new amelioration Particle Swarm Optimization (SARPSO) based on simulated annealing (SA), asynchronously changed learning genes (ACLG) and roulette strategy was proposed because the classical Particle Swarm Optimization (PSO) algorithm was easily plunged into local minimums. SA had the ability of probability mutation in the search process, by which the search processes of PSO plunging into local...
This paper presents an agent-based evolutionary search algorithm (AES) for solving dynamic travelling salesman problem (DTSP). The proposed algorithm uses the principal of collaborative endeavor learning mechanism in which all the agents within the current population co-evolve to track dynamic optima. Moreover, a local updating rule which is much the same of permutation enforcement learning scheme...
The incremental updating of classifiers implies that their internal parameter values can vary according to incoming data. As a result, in order to achieve high performance, incremental learner systems should not only consider the integration of knowledge from new data, but also maintain an optimum set of parameters. In this paper, we propose an approach for performing incremental learning in an adaptive...
Probability Collective (PC) is a methodology for distributed optimization by sampling an explicitly parameterized probability distribution over the space of solutions. This parameterization effectively utilizes granules of probability distributions to construct computational models for solving complex systems-level optimization problems. In this paper we present a study of using this probabilistic...
A key feature in population based optimization algorithms is the ability to explore a search space and make a decision based on multiple solutions. In this paper, an incremental learning strategy based on a dynamic particle swarm optimization (DPSO) algorithm allows to produce heterogeneous ensembles of classifiers for video-based face recognition. This strategy is applied to an adaptive classification...
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