Energy system modelling supports decision-makers in the development of short and long-term energy strategies. In the field of bottom-up short-term energy system models, high resolution in time and space, the implement...
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Energy system modelling supports decision-makers in the development of short and long-term energy strategies. In the field of bottom-up short-term energy system models, high resolution in time and space, the implementation of sector coupling and the adoption of a multi-objective investment optimization have never been achieved simultaneously because of the high computational effort. Within this paper, such a bottom-up short-term model which simultaneously implements (i) hourly temporal resolution, (ii) multi-node approach thus high spatial resolution, (iii) integrates the electric, thermal and transport sectors and (iv) implements a multi-objective investment optimization method is proposed. The developed method is applied to the Italian energy system at 2050 to test and show its main features. The model allows the evaluation of the hourly curtailments for each node. The optimization highlights that the cheapest solutions work towards high curtailments and low investments in flexibility options. In order to further reduce the CO2 emissions the investments in flexibility options like electric storage batteries and reinforcement and enlargement of the transmission grid become relevant.
作者：
Durmus, AliKayseri Univ
Vocat Coll Dept Elect & Energy 15 Temmuz Yerleskesi TR-38280 Kayseri Turkey
In this paper, thinned concentric circular antenna arrays (CCAAs) with low sidelobe levels and fixed half-power beamwidths are synthesized by using the Slime Mold Algorithm (SMA). SMA is a novel stochastic optimizatio...
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In this paper, thinned concentric circular antenna arrays (CCAAs) with low sidelobe levels and fixed half-power beamwidths are synthesized by using the Slime Mold Algorithm (SMA). SMA is a novel stochastic optimization method inspired by the oscillation mode of the slime mold in nature. Entering the literature as a new optimization technique, SMA is based on a matchless mathematical model that finds the most suitable way to find food in the search space thanks to the negative and positive oscillations of the slime mold. SMA is also used to obtain thinned CCAAs having different ring numbers. It is seen that the results obtained by SMA are very good. Besides, the flexibility of SMA in different values of parameters is a promising feature for the other antenna array synthesis problems. The results obtained by SMA are compared with several meta-heuristic algorithms in the literature. SMA shows a better performance in synthesizing CCAAs than the other compared algorithms.
Breast cancer is one of the leading causes of death for women around the world. Its early diagnosis can significantly enhance the survival rate of the patient. Image processing techniques are used to help to diagnose ...
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Breast cancer is one of the leading causes of death for women around the world. Its early diagnosis can significantly enhance the survival rate of the patient. Image processing techniques are used to help to diagnose this disease. The analysis of breast tissue samples on histological images is a challenging task where computer vision techniques can contribute. In this paper, the Stochastic Fractal Search (SFS) algorithm is applied to the image thresholding problem on breast histology imagery using as objective function three different entropies. The SFS is a new evolutionary Algorithm (EA) which emulates the growth mechanism of fractals. SFS has been successfully applied to other applications, but its performance on image thresholding is unknown. The implementation of SFS is conducted using as objective function three of the most representative entropies being Kapur, Minimum Cross Entropy, and Tsallis. To provide a comparison point, two EAs commonly used for image thresholding are selected;the Artificial Bee Colony (ABC) and the Differential Evolution (DE). In this context, the resulting nine combinations are evaluated concerning the quality of the segmented images. Since the nine evaluated methods share either EA or entropy, the nonparametric test of Kruskal-Wallis is conducted to analyze the similarity of the results among methods. Results indicate that the combination of SFS and Minimum Cross Entropy yields the best results for the segmentation of histological imagery. (C) 2018 Elsevier B.V. All rights reserved.
The use of Unmanned Aerial Vehicles (UAVs) in delivery logistics has become an efficient solution with the advancement of autonomous robotics. This paper proposes a novel mechanism that synchronizes drones and deliver...
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The use of Unmanned Aerial Vehicles (UAVs) in delivery logistics has become an efficient solution with the advancement of autonomous robotics. This paper proposes a novel mechanism that synchronizes drones and delivery trucks;particularly the case where trucks can work as mobile launching and retrieval sites. The problem is a Vehicle Routing Problem with Time Windows and Synchronized Drones. A multi-objective optimization model is developed with two conflicting objectives, minimizing the travel costs and maximizing the customer service level in terms of timely deliveries. A novel Collaborative Pareto Ant Colony Optimization algorithm is proposed to solve the model and Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to compare and validate the proposed algorithm. The experimental results indicate that the proposed mechanism is an efficient solution to parcel delivery logistics.
There exists a wide variety of network problems where several connection requests occur simultaneously. In general, each request is attended by finding a route in the network, where the origin and destination of such ...
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There exists a wide variety of network problems where several connection requests occur simultaneously. In general, each request is attended by finding a route in the network, where the origin and destination of such a route are those hosts that wish to establish a connection for information exchange. As is well documented in the related literature, the exchange of information through disjoint routes increases the effective bandwidth, velocity, and the probability of receiving the corresponding information. The definition of disjoint paths may refer to nodes, edges, or both. One of the most studied variants is the one where disjointness implies not to share edges. This optimization problem is usually known as the maximum edge-disjoint paths problem. This NP-hard optimization problem has applications in real-time communications, very large scale integration design, scheduling, bin packing, or load balancing. The proposed approach hybridizes an integer linear programming formulation of the problem with an evolutionary algorithm. Empirical results using 174 previously reported instances show that the proposed procedure compares favorably to previous metaheuristics for this problem. We confirm the significance of the results by conducting nonparametric statistical tests.
Microwave broadband absorber design for a desired frequency and angle range is presented. The design technique is based on a self-adaptive Differential Evolution (DE) algorithm. Numerical examples are compared with th...
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Microwave broadband absorber design for a desired frequency and angle range is presented. The design technique is based on a self-adaptive Differential Evolution (DE) algorithm. Numerical examples are compared with the existing in the literature and with those found by the other evolutionary algorithms. The results show that the new DE algorithm version outer performs other global optimizers like Particle swarm optimization (PSO) variants and the classical DE algorithm. (c) 2008 Wiley Periodicals. Inc. Int J RF and Microwave CAE 19: 364-372, 2009.
We have used unbiased global optimization to fit a reactive force field to a given set of reference data. Specifically, we have employed genetic algorithms (GA) to fit ReaxFF to SiOH data, using an in-house GA code th...
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We have used unbiased global optimization to fit a reactive force field to a given set of reference data. Specifically, we have employed genetic algorithms (GA) to fit ReaxFF to SiOH data, using an in-house GA code that is parallelized across reference data items via the message-passing interface (MPI). Details of GA tuning turn-ed out to be far less important for global optimization efficiency than using suitable ranges within which the parameters are varied. To establish these ranges, either prior knowledge can be used or successive stages of GA optimizations, each building upon the best parameter vectors and ranges found in the previous stage. We have finally arrive-ed at optimized force fields with smaller error measures than those published previously. Hence, this optimization approach will contribute to converting force-field fitting from a specialist task to an everyday commodity, even for the more difficult case of reactive force fields. (c) 2013 Wiley Periodicals, Inc.
Genetic Network Programming (GNP) is a relatively recently proposed evolutionary algorithm which is an extension of Genetic Programming (GP). However, individuals in GNP have graph structures. This algorithm is mainly...
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Genetic Network Programming (GNP) is a relatively recently proposed evolutionary algorithm which is an extension of Genetic Programming (GP). However, individuals in GNP have graph structures. This algorithm is mainly used in decision making process of agent control problems. It uses a graph to make a flowchart and use this flowchart as a decision making strategy that an agent must follow to achieve the goal. One of the most important weaknesses of this algorithm is that crossover and mutation break the structures of individuals during the evolution process. Although it can lead to better structures, this may break suitable ones and increase the time needed to achieve optimal solutions. Meanwhile, all the researches in this field are dedicated to test GNP in deterministic environments. However, most of the real-world problems are stochastic and this is another issue that should be addressed. In this research, we try to find a mechanism that GNP shows better performance in stochastic environments. In order to achieve this goal, the evolution process of GNP was modified. In the proposed method, the experience of promising individuals was saved in consecutive generations. Then, to generate offspring in some predefined number of generations, the saved experiences were used instead of crossover and mutation. The experimental results of the proposed method were compared with GNP and some of its versions in both deterministic and stochastic environments. The results demonstrate the superiority of our proposed method in both deterministic and stochastic environments.
We study the behavior of a population-based EA and the Max-Min Ant System (MMAS) on a family of deterministically-changing fitness functions, where, in order to find the global optimum, the algorithms have to find spe...
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We study the behavior of a population-based EA and the Max-Min Ant System (MMAS) on a family of deterministically-changing fitness functions, where, in order to find the global optimum, the algorithms have to find specific local optima within each of a series of phases. In particular, we prove that a (2+1) EA with genotype diversity is able to find the global optimum of the Maze function, previously considered by Kotzing and Molter[9], in polynomial time. This is then generalized to a hierarchy result stating that for every mu, a (mu + 1) EA with genotype diversity is able to track a Maze function extended over a finite alphabet of mu symbols, whereas population size mu-1 is not sufficient. Furthermore, we show that MMAS does not require additional modifications to track the optimum of the finite-alphabet Maze functions, and, using a novel drift statement to simplify the analysis, reduce the required phase length of the Maze function.
This paper presents a method that combines a greedy and an evolutionary algorithm to assign papers submitted to a conference to reviewers. The evolutionary algorithm tries to maximize match between the referee experti...
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