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The proposed method applies genetic algorithms (GA) to optimize the weight coefficients of the power spectrum of the Fourier-transformed signal of lateral acceleration of a moving car. The evolved weighted power spectrum detects the steering oscillations caused by the delayed steering response of a human driver in normal, routine driving situations - traffic-less driving on straight and curved roads...
In this paper, a novel centralized traffic network model is proposed to describe the urban traffic light scheduling problem (UTLSP) in a traffic network. The objective is to minimize the network-wise total delay time of all vehicles in a fixed time window. To overcome the potentially high computational complexity involved in UTLSP, an improved artificial bee colony (IABC) algorithm is proposed. A...
Artificial Immune Systems (AIS) bear two complementary radical insights for building immune properties into technical systems: AIS algorithms, which have proved their efficiency for anomaly detection, and what we call Artificial Immune Ecosystems, i.e. distributed architectures capable of decentralized sensing, analysis and reactions to these anomalies. In this paper, we propose the AWA model for...
A many-objective optimization algorithm for community detection in multi-layer networks is proposed. The method exploits the modularity concept as function to be simultaneously optimized on all the network layers to uncover multi-layer communities. In addition, three different strategies to choice the best solution from the set of solutions of the Pareto front are presented. Simulations on several...
Public-key cryptography is a fundamental component of modern electronic communication that can be constructed with many different mathematical processes. Presently, cryptosystems based on elliptic curves are becoming popular due to strong cryptographic strength per small key size. At the heart of these schemes is the intractability of the elliptic curve discrete logarithm problem (ECDLP). Pollard's...
In traditional many-objective evolutionary algorithms (MaOEAs), solutions survived to the next generation are individually selected which leads to the favorable quality upon the population composed of these selected solutions not necessarily to be gained. However, MaOEAs which are widely used in solving many-objective optimization problems (MaOPs) are considerably preferred due to their population-based...
The weapon target assignment (WTA) problem is a fundamental problem arising in defense-related applications of operations research. The multi-stage weapon target assignment (MWTA) problem is the basis of the dynamic weapon target assignment (DWTA) problem which commonly exists in practice. The MWTA problem considered in this paper is formulated as a multi-objective constrained combinatorial optimization...
The detection of shared community structure in multilayer network is an interesting and important issue that has attracted many researches. Traditional methods for community detection of single layer networks are not suitable for that of multilayer networks. In a previous work, the authors modeled the community discovery problem in multilayer network as a multiobjective one and devised a genetic algorithm...
Image change detection is to recognize the changes between two images that are taken over the same scene but at different times, which has been applied broadly in many fields. Fuzzy clustering is a frequently-used technique for unsupervised change detection. However, traditional fuzzy clustering algorithms are easy to be trapped into a local optimum due to the limits of their optimization processes...
Endmember extraction is a critical step of spectral unmixing. In this paper, a novel endmember extraction algorithm based on evolutionary multi-objective optimization is proposed for hyperspectral remote sensing images. In the proposed method, endmember extraction is modeled as a multi-objective optimization problem. Then the root mean square error between the original image and its remixed image...
Neural networks are currently popular learning models to represent and analyze data. We address two issues about that in this paper. On the one hand, the parameters between neurons are often restricted to be constants, which greatly limits the learning ability and reduces the robustness of the neural network. For that, it is necessary to make the parameters fuzzy. In this paper, we introduce the fuzzy...
Evolutionary multi-tasking is a novel concept where algorithms utilize the implicit parallelism of population-based search to solve several tasks efficiently. In last decades, multi-task learning, which harnesses the underlying similarity of the learning tasks, has proved efficient in many applications. Extreme learning machine is a distinctive learning algorithm for feed-forward neural networks....
This work focuses on wide-scale freight transportation logistics motivated by the sharp increase of on-line shopping stores and the upsurge of Internet as the most frequently utilized selling channel during the last decade. This huge ecosystem of one-click-away catalogs has ultimately unleashed the need for efficient algorithms aimed at properly scheduling the underlying transportation resources in...
The aim of influence maximization problem is to mine a small set of influential individuals in a complex network which could reach the maximum influence spread. In this paper, an efficient fitness function based on local influence is designed to estimate the influence spread. Then, we propose a discrete particle swarm optimization based algorithm to find the final set with the maximum value of the...
Band selection is a crucial preprocessing step for hyperspectral image classification, which is a classic feature selection method. Feature selection is designed to select feature subsets to represent the whole feature space. For feature selection, two crucial issues need to be handled: preserving information and redundancy reducing. In this paper, a novel feature selection method for hyperspectral...
When using a fixed number of neighbors for training a local surrogate model in surrogate assisted evolutionary optimization algorithms, it may suffer from the large uncertainty because the actual distribution of candidate's neighborhood may be neglected. In this paper, we propose to firstly analyze the distribution characteristics of candidate's neighborhood through a modified overlapping clustering...
Recently, inspired by the human brainstorming process, a new kind of metaheuristic algorithm, called brain storm optimization (BSO) algorithm was proposed for global optimization. Experimental results have shown its excellent performance when solving optimization problems. In order to further improve the search ability of the BSO, this paper proposes an improved BSO (IBSO) algorithm by introducing...
Combinatorial optimization problems such as the Travelling Salesman Problem, Quadratic Assignment Problem and Sliding-Tile Puzzle have structure that can be described algebraically and exploited to improve search quality. In particular, heuristic functions for these problems can be described in terms of symmetric groups. By employing techniques from the emerging field of algebraic machine learning,...
Meta-heuristics have emerged as an efficient way to solve NP-hard problems even without the guaranteed of optimal values. The main issue of meta-heuristics is that they are built using domain-specific knowledge. Therefore, they require a great effort to be adapted to a new domain. The concept of Hyper-heuristic was proposed to solve this problem. Hyper-heuristics are search methods that aim to solve...
Image registration (IR) is an extended and important problem in computer vision. It involves the transformation of different sets of image data having a shared content into a common coordinate system. Specifically, we will deal with the 3D intensity-based medical IR problem where the intensity distribution of the images is considered, one of the most complex and time consuming variants. The limitations...
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