9,227 research outputs found
Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations
The large number of possible configurations of modern software-based systems,
combined with the large number of possible environmental situations of such
systems, prohibits enumerating all adaptation options at design time and
necessitates planning at run time to dynamically identify an appropriate
configuration for a situation. While numerous planning techniques exist, they
typically assume a detailed state-based model of the system and that the
situations that warrant adaptations are known. Both of these assumptions can be
violated in complex, real-world systems. As a result, adaptation planning must
rely on simple models that capture what can be changed (input parameters) and
observed in the system and environment (output and context parameters). We
therefore propose planning as optimization: the use of optimization strategies
to discover optimal system configurations at runtime for each distinct
situation that is also dynamically identified at runtime. We apply our approach
to CrowdNav, an open-source traffic routing system with the characteristics of
a real-world system. We identify situations via clustering and conduct an
empirical study that compares Bayesian optimization and two types of
evolutionary optimization (NSGA-II and novelty search) in CrowdNav
ARSH-FATI a Novel Metaheuristic for Cluster Head Selection in Wireless Sensor Networks
Wireless sensor network (WSN) consists of a large number of sensor nodes distributed over a certain target area. The WSN plays a vital role in surveillance, advanced healthcare, and commercialized industrial automation. Enhancing energy-efficiency of the WSN is a prime concern because higher energy consumption restricts the lifetime (LT) of the network. Clustering is a powerful technique widely adopted to increase LT of the network and reduce the transmission energy consumption. In this article (LT) we develop a novel ARSH-FATI-based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called novel ranked-based clustering (NRC) to reduce the communication energy consumption of the sensor nodes while efficiently enhancing LT of the network. Unlike other population-based algorithms ARSH-FATI-CHS dynamically switches between exploration and exploitation of the search process during run-time to achieve higher performance trade-off and significantly increase LT of the network. ARSH-FATI-CHS considers the residual energy, communication distance parameters, and workload during cluster heads (CHs) selection. We simulate our proposed ARSH-FATI-CHS and generate various results to determine the performance of the WSN in terms of LT. We compare our results with state-of-the-art particle swarm optimization (PSO) and prove that ARSH-FATI-CHS approach improves the LT of the network by ∼25%
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