752 publications from this institution
By analyzing the similarity of a self-organizing system and an optimization process, we highlight that optimization can be considered as self-organization. We analyze the characteristics of some popular met heuristic algorithms such as firefly algorithm and cuckoo search for applications in self-organizing systems.
No abstract is provided for this article.
No abstract is provided for this article.
Optimization is often subject to complex constraints. Constraint-handling techniques form an important area of problem solving in optimization. There many constraint-handling methods that allow the algorithms to solve unconstrained optimization problems to solve constrained optimization problems efficiently. This chapter provides a basic overview of the common techniques used with nature-inspired algorithms.
Based on Universal Mobile Telecommunications System (UMTS) Wideband Code Division Multiple Access (WCDMA) technology, the Mobile User Objective System (MUOS) provides a wide range of wireless telecommunications services to the war fighter over geosynchronous satellites. As MUOS is a multiple beam system, it is ofparamount importance for the User Entry (UE) to select the best beam, which in turn requires accurate transmit powerfrom the satellite. To minimize the satellite path gain uncertaintyfrom the base station to the UE, the Enhanced Gain Variation Reduction (EGVR) algorithm is proposed. Implemented on the basis of the measurements of the received signal code power (RSCP) ofthe pilot channel from the UEand the measurements ofthe received signal code power at the terrestrial base station, the EGVR process generates the transmission power estimate and integrates it into the overall gain adjust system to keep the transmission power levels as desired. Thefeatures ofthe algorithm include the weighted gain adjusts generated from the estimation error, appropriate timers to control the measurementflow, and differential measurements to eliminate the common errors between satellite beams. The preliminary study shows that the EGVR can reduce the power uncertaintyfrom 2.4 dB to 0.4 dB, in addition to reducing any differential errors encountered. With the blending ofthe telemetryfeedback into the algorithm, it can make the transmitpower uncertainty even smaller.
No abstract is provided for this article.
All nature-inspired algorithms have algorithm-dependent parameters. Tuning such parameters can be a challenging task. This chapter introduces the objective of parameter tuning and highlights the challenges in this area.
No abstract is provided for this article.
Real-world optimization problems often have multiple and potentially conflicting objectives. The algorithms that work for single objective optimization require some modifications before they can be used to solve multi-objective optimization problems. In addition, new concepts such as Pareto optimality need to be introduced. This chapter introduces the commonly used methods to deal with multi-objective optimization.
This chapter introduces the basic methods for data fitting, focusing on the regression-based curve-fitting, methods of least squares, and various regularization methods. Information criteria are also introduced.
Social Algorithms• Algorithm: An algorithm is a step-by-step, computational procedure or a set of rules to be followed by a computer in calculations or computing an answer to a problem.• Ant colony optimization: Ant colony optimization (ACO) is an algorithm for solving optimization problems such as routing problems using multiple agents.ACO mimics the local interactions of social ant colonies and the use of chemical messenger -pheromone to mark paths.No centralized control is used and the system evolves according to simple local interaction rules.• Bat algorithm: Bat algorithm (BA) is an algorithm for optimization, which uses frequencytuning to mimic the basic behaviour of echolocation of microbats.BA also uses the variations of loudness and pulse emission rates and a solution vector to a problem corresponds to a position vector of a bat in the search space.Evolution of solutions follow two algorithmic equations for positions and frequencies.• Bees-inspired algorithms: Bees-inspired algorithms are a class of algorithms for optimization using the foraging characteristics of honeybees and their labour division to carry out search.Pheromone may also be used in some variants of bees-inspired algorithms.
No abstract is provided for this article.
We present a model for the study of the effect of rheological hysteresis on compaction in sedimentary basins, when surface loading and unloading occurs. When compaction is slow (sedimentation rate is large and/or permeability is small), the hysteresis has little effect on the basal compaction layer, but in the more realistic case where compaction is fast (i.e., sedimentation is slow and/or permeability is large), surface unloading leads to downward propagation of a decompaction front, across which the vertical porosity gradient jumps, while subsequent surface reloading leads to downward propagation of a discontinuity in porosity, despite the fact that the porosity is governed by a diffusive equation of Richards type.
Mathematical models of compaction in sedimentary basins typically assume a relationship between effective pressure p e and porosity ϕ, which is of a non‐linear type; that is, p e = p e (ϕ). However, at depths greater than a kilometer, pressure solution becomes important and this relationship approaches a viscous one. We derive a mathematical model for viscous compaction in sedimentary basins and show how the model suggests different styles of behavior in the limits of slow and fast compaction.
