Real world” decision-making applications generally contain multifaceted performance requirements riddled with incongruent performance specifications. There are invariably unmodelled elements, not apparent during model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, it is preferable to generate numerous alternatives that provide dissimilar approaches to the problem. These alternatives should possess near-optimal objective measures with respect to all known objective(s), but be maximally different from each other in terms of their decision variables. This maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). This study demonstrates how the Firefly Algorithm can concurrently create multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces. This new approach is computationally efficient, since it permits the concurrent generation of multiple, good solution alternatives in a single computational run rather than the multiple implementations required in previous MGA procedures.
The complicated mechanisms governing hydrocarbon expulsion (primary migration) have not as yet been fully understood, though understanding these mechanisms is paramount in basin modeling. This paper presents (a) a simple oil-expulsion model based on the principles of sedimentary compaction and the flow difference between oil and water, and (b) a simple gas-expulsion model via mass balance. These two models do not consider more factors or mechanisms than some existing models, but we verify that the predicated expelled amount is proper by using it as input data for secondary migration and confirming that the modeled volume of accumulated hydrocarbons is close to that determined by exploration. The hydrocarbon migration–accumulation (secondary migration) history is the most important component in basin modeling, but it is also one of the most challenging to achieve. In general, a sectional 2-D simulator cannot predict the amount of hydrocarbon accumulated, whereas a 3-D simulator takes considerable computing time. This paper presents a simplified pseudo 3-D model based on the principles of buoyancy drive and Darcy's Law in combination with the effects of faulting and lithology on migration pathways. Comparing with the existing four models currently in use (multi-phase Darcy flow, flowpath, hybrid method combining the former two approaches together, and invasion percolation), the proposed model is mainly targeted at calculating the quantities and locations of petroleum accumulations for oil and gas assessment, and so the factors taken into account and the functions needed to be executed in software are less than in other models. Application of the aforementioned three models in the Kuqa Depression of the Tarim Basin in western China shows that the simplified pseudo 3-D model is very efficient and less computationally extensive, and the predicted quantities and locations of petroleum accumulations approach those determined by exploration, showing these models could form a useful prospecting method.
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Both the bat algorithm and the cuckoo search algorithm are nature-inspired optimization algorithms, and they have been shown to be flexible and effective in solving various optimization problems. This chapter introduces both algorithms in detail, with an emphasis on the main ideas and implementations. Examples are also given to show how these algorithms work.
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This paper introduces a dual-function radar-communication (DFRC) system with cognitive radio capability to tackle the spectral scarcity problem in wireless communications. Particularly, a cognitive DFRC system operates on a spectrum owned by a primary system to simultaneously perform data communication and target tracking with the condition that its interference to the primary users (PUs) is below a certain threshold. To achieve this, an optimization problem is formulated to jointly design the beamforming vectors for both the radar and communication functions in such a way that the mean square error (MSE) of the beam pattern between the designed and desired waveforms is minimized. The optimization problem has the following three constraints: i) the signal-to-interference-plus-noise ratio (SINR) at each data communication user is above a predetermined level; ii) the per-antenna transmit power is maintained at a given level; iii) the interference imposed on each PU is below a certain threshold. Both the semidefinite relaxation and nature-inspired firefly algorithms are proposed in order to search for the optimal solutions to the optimization problem. The simulation results indicate that our proposed algorithms can enable the DFRC system to protect the PUs while simultaneously performing its communication and radar functions.
The strong need and global interests for green communications necessitate the maximum reduction of energy consumption. Hence, one of the major challenges is to optimize the resource allocation and to improve the energy efficiency in an orthogonal frequency division multiple access system. In this paper, we analyze the energy‐efficient resource allocation problem mathematically. Our analytical results show that the optimal resource allocation can be achieved by primarily assigning the user with either the best channel gain or the least optimal power. Furthermore, our detailed case studies show that such analytical results are consistent with the best result obtained from the exhaustive search method. This means that the proposed approach can provide quick and better resource allocation. It can be expected that real‐time in situ optimal resource allocation can be achieved by extending the current methodology. Copyright © 2014 John Wiley & Sons, Ltd.
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So, automatic control systems, based on software tools, are becoming more desirable in distribution power systems.Primarily, such schemes are expected to manage system voltage fluctuations, network power flows and fault levels.Functionalities include also power balancing, system frequency control and management of demand side resources for the primary system constraints.
This chapter introduces the essence of the finite difference method for solving ordinary different equations. It starts with the Euler scheme, followed by the analysis of numerical stability. Then higher-order methods are also covered, including the leap-frog and Runge–Kutta methods for initial value problems. The shooting method is then introduced for solving two-point boundary problems.
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