Integrating flexibly-operated carbon capture and storage (CCS) into the existing power plants has operational benefits for the future low carbon power systems. This paper proposes an improved formulation for flexible ...
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Integrating flexibly-operated carbon capture and storage (CCS) into the existing power plants has operational benefits for the future low carbon power systems. This paper proposes an improved formulation for flexible operation of carbon capture power plants (CCPPs) within the conventional economic dispatch (ED) problem. The main contribution of this work is the simplification and the practicality of the variables used for the flexible operation control of the facility. The optimal ED problem of thermal power generation portfolio with CCPPs within the mix are computed using a chaos-enhanced Cuckoo Search optimization algorithm. To test the proposed formulations, an IEEE 30 bus test system was used. The impact of varying carbon prices on the system dispatch was investigated. The results reveal the potentiality of decoupling the generation and emission outputs of the thermal power plants.
Wireless sensor networks have many applications and accordingly represent an active research area. Coverage maximization of the area of interest by sensors that have limited sensing radius is an important hard optimiz...
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ISBN:
(纸本)9781509045914
Wireless sensor networks have many applications and accordingly represent an active research area. Coverage maximization of the area of interest by sensors that have limited sensing radius is an important hard optimization problem. Since sensors are initially often deployed randomly one way of solving maximal coverage problem is by using mobile sensors that move to optimal positions. Since power in sensor nodes is limited, minimization of the sensor nodes movement is secondary optimization goal. In this paper we propose use of recent swarm intelligence algorithm, firefly algorithm, for optimization of that hard multiobjective problem. We tested our approach on standard benchmark data and compared results with other techniques from literature. Our proposed approach was better considering all quality measures: coverage, energy consumption, robustness and convergence speed.
The prediction of aerodynamic coefficients of missiles that have air-breathing components is a challenging task during the preliminary design phase. It is considerably important to estimate missile aerodynamic coeffic...
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The prediction of aerodynamic coefficients of missiles that have air-breathing components is a challenging task during the preliminary design phase. It is considerably important to estimate missile aerodynamic coefficients accurately at the beginning of design phase to avoid poor designs which could lead to redesign at later stages of the design process. In this study, firstly an improved method is developed to predict the aerodynamic coefficients of missile configurations with air-breathing components more accurately compared to engineering level fast prediction tools. Then, optimization studies comprising of reaching global minimum value of the Beale Function and inverse design optimization of the ramjet missile configuration are done through implementing different meta-heuristic optimization techniques which are "Genetic Algorithm", "Differential Evolution" and "Modified Cuckoo Search". In the inverse design optimization studies, two different methods, which are engineering level fast prediction tool Missile DATCOM and the improved method, are used to calculate aerodynamic coefficients of the candidate missile configurations throughout the design optimization process. Results of the inverse design optimization studies show that preliminary design phase of the missiles including air-breathing components can be enhanced significantly since accuracy of the prediction of aerodynamic coefficients is improved by methods applied in this study.
Feature selection algorithms select the most relevant features of a data set to improve the classification performance of the machine learning classifiers trained using the data set. This paper proposes a feature sele...
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ISBN:
(纸本)9781509055104
Feature selection algorithms select the most relevant features of a data set to improve the classification performance of the machine learning classifiers trained using the data set. This paper proposes a feature selection algorithm called multi-objective genetic local search (MOGLS) which integrates a 3-objective genetic algorithm with a local search heuristic to find feature subsets with the maximum prediction accuracy, the smallest sizes and the minimum redundancy. The performance of MOGLS is compared with 4 algorithms: a wrapper genetic algorithm, correlation-based feature selection, mutual information ranking and C4.5 on 8 datasets from the UCI machine learning repository. MOGLS performs better than or as good as the 4 algorithms on the 8 datasets.
Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with...
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ISBN:
(纸本)9781509003044
Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with number of definitions, depending on the assumed conditions. In this paper we consider hard optimization area coverage problem with the goal of finding optimal sensor nodes positions that maximize probabilistic coverage of the area of interest. For such type of optimization problem swarm intelligence stochastic metaheuristics have been successfully used. In this paper we propose a modified enhanced fireworks algorithm for wireless sensor network coverage problem and compare it with other approaches from literature, where our algorithm proved to be very robust and better, considering all conducted tests.
Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with...
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ISBN:
(纸本)9781509003051
Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with number of definitions, depending on the assumed conditions. In this paper we consider hard optimization area coverage problem with the goal of finding optimal sensor nodes positions that maximize probabilistic coverage of the area of interest. For such type of optimization problem swarm intelligence stochastic metaheuristics have been successfully used. In this paper we propose a modified enhanced fireworks algorithm for wireless sensor network coverage problem and compare it with other approaches from literature, where our algorithm proved to be very robust and better, considering all conducted tests.
Development of efficient and robust optimization methods for structural design is one of the most active research fields in structural engineering. Imperialist Competitive Algorithm (ICA) is one of the recent meta-heu...
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ISBN:
(纸本)9780996043717
Development of efficient and robust optimization methods for structural design is one of the most active research fields in structural engineering. Imperialist Competitive Algorithm (ICA) is one of the recent meta-heuristic algorithms proposed to solve optimization problems. In this paper, an Enhanced Imperialist Competitive Algorithm (EICA) is proposed which increases the search space and enables the ICA algorithm to escape from local optima in a fast time. In this algorithm added value is given to a slightly unfeasible solution, based on its distance from the relative imperialist. The performance of the proposed EICA algorithm in optimum design of side sway frames is investigated by comparing the EICA optimum designs of two benchmark side sway frames with the best designs obtained using a number of other meta-heuristic solutions. Results indicate that, in terms of both the design quality and the solution speed, EICA compares favorably with a number of other meta-heuristic optimizers, including the basic ICA.
In this paper, multiplier-less nearly perfect reconstruction tree structured non-uniform filter banks (NUFB) are proposed. When sharp transition width filter banks are to be implemented, the order of the filters and h...
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In this paper, multiplier-less nearly perfect reconstruction tree structured non-uniform filter banks (NUFB) are proposed. When sharp transition width filter banks are to be implemented, the order of the filters and hence the complexity will become very high. The filter banks employ an iterative algorithm which adjusts the cut off frequencies of the prototype filter, to reduce the amplitude distortion. It is found that the proposed design method, in which the prototype filter is designed by the frequency response masking method, gives better results when compared to the earlier reported results, in terms of the number of multipliers when sharp transition width filter banks are needed. To reduce the complexity and power consumption for hardware realization, a design method which makes the NUFB totally multiplier-less is also proposed in this paper. The NUFB is made multiplier-less by converting the continuous filter bank coefficients to finite precision coefficients in the signed power of two space. The filter bank with finite precision coefficients may lead to performance degradation. This calls for the use of suitable optimization techniques. The classical gradient based optimization techniques cannot be deployed here, because the search space consists of only integers. In this context, meta-heuristic algorithm is a good choice as it can be tailor made to suit the problem under consideration. Thus, this design method results in near perfect NUFBs which are simple and multiplier-less and have linear phase and sharp transition width with very low aliasing. Also, different non-uniform bands can be obtained from the tree structured filter bank by rearranging the branches.
An enhanced harmony search (EMS) algorithm is developed enabling the HS algorithm to quickly escape from local optima. For this purpose, the harmony memory updating phase is enhanced by considering also designs that a...
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An enhanced harmony search (EMS) algorithm is developed enabling the HS algorithm to quickly escape from local optima. For this purpose, the harmony memory updating phase is enhanced by considering also designs that are worse than the worst design stored in the harmony memory but are far enough from local optima. The proposed EHS algorithm is utilized to solve four classical weight minimization problems of steel frames. Results indicate that, as far as the quality of optimum design and convergence behavior are concerned, EHS is significantly superior or definitely competitive with other meta-heuristic optimizationalgorithms including the classical HS. (C) 2014 Elsevier Ltd. All rights reserved.
Krill Herd optimization algorithm which is a new metaheuristic search algorithm mimics the herding behavior of krill individuals. The significant characteristic of meta heuristic algorithms is their ability in combina...
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ISBN:
(纸本)9781479954865
Krill Herd optimization algorithm which is a new metaheuristic search algorithm mimics the herding behavior of krill individuals. The significant characteristic of meta heuristic algorithms is their ability in combination of local search and global search. This property can adjust the contribution of local search and global search in initial step and during searching process and plays crucial role in the algorithm performance. One hazard which threats the meta heuristic algorithms is getting stuck in local optimum traps. An appropriate solution to deal with this problem is using chaos theory which brings dynamism and instability properties to the algorithm so that by strengthening the performance of random search helps the algorithm to escape from local optimum traps. In this paper, we propose a new method called chaotic Krill Herd optimization algorithm which by adopting chaos theory in Krill Herd optimization algorithm heighten its performance in dealing with various optimization problems. The obtained results by the proposed method in comparison with those of the standard Krill Herd optimization algorithm indicates the higher performance of the proposed algorithm.
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