The multiple sequence alignment problem (MSA) can be reformulated as the problem of finding a maximum weight trace in an alignment graph, which is derived from all pairwise alignments. We improve the alignment graph b...
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In this paper, a procedure for system decompositon is developed for decentralized multivariable systems. Optimal input-output pairing techniques are used to rearrange a large multivariable system into a structure that...
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We consider the problem of finding small Golomb rulers, a hard combinatorial optimization task. This problem is here tackled by means of a hybrid evolutionary algorithm (EA). This EA incorporates ideas from greedy ran...
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We describe and critique the convergence properties of filterbased evolutionary pattern search algorithms (F-EPSAs). F-EPSAs implicitly use a filter to perform a multi-objective search for constrained problems such th...
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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.
Nowadays, the growth of available data, known as big data, and machine learning techniques are changing our lives. The extraction of insights related to the underlying phenomena in data is key in order to improve deci...
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Nowadays, the growth of available data, known as big data, and machine learning techniques are changing our lives. The extraction of insights related to the underlying phenomena in data is key in order to improve decision-making processes. These underlying phenomena are described in emerging pattern mining by means of the description of the discriminative characteristics between the outputs of interest, which is a very important characteristic in machine learning. However, emerging pattern mining algorithms for big data environments have not been widely developed yet. This paper presents the first multi-objective evolutionary algorithm for emerging pattern mining in big data environments called BD-EFEP. BD-EFEP implements novelties for emerging pattern mining such as the MapReduce approach to improve the efficiency of the evaluation of the individuals, or the use of a token-competition-based procedure in order to boost the extraction of simple, general and reliable emerging pattern models. The experimental study performed using datasets with high number of examples shows the advantages of the algorithm proposed for the emerging pattern mining task in big data problems. Results show that the approach used by BD-EFEP opens new research lines for the extraction of high descriptive emerging patterns in big data environments.
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.
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.
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.
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.
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