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In the Team Orienteering Problem (TOP) a set of locations is given, each with a score. The objective is to determine a fixed number of routes (teams), limited in length, that visit some locations and maximize the sum of the collected scores. For the first time we introduce bi-objective TOP which has a second objective, to balance all team's scores for the purpose of obtaining fair teams. So the second...
Voltage quality is an essential requirement in the operation of electricity distribution systems. At consumer busbars, the deviation from the nominal voltage has specific ranges, prescribed in technical regulations. A widely chosen voltage correction approach is to use reactive power compensation. Capacitor banks are used for this purpose in highly loaded networks, but their placement and sizing require...
Diabetes Mellitus is a dreadful disease characterized by increased levels of glucose in the blood, termed as the condition of hyperglycemia. As this disease is prominent among the tropical countries like India, an intense research is being carried out to deliver a machine learning model that could learn from previous patient records in order to deliver smart diagnosis. This research work aims to improve...
The paper introduces a proposal for an automated magnetic resonance (MR) image segmentation called Case-Based Genetic Algorithm Location-Dependent Image Classification (CBGA-LDIC) and presents its evaluation results. This method finds an appropriate cell set towards efficient image segmentation. It uses location-dependent image classification (LDIC), which is integrated by genetic algorithm (GA) combined...
This paper introduces a new initialization method of individuals for genetic algorithm (GA) in portfolio optimization problems. In our approach, first a set of assets, variables, composing the portfolio is selected, and then combination of real-valued weights of the portfolio is optimized by GA. In the asset selection, a pairwise asset selection which is an iterative greedy scheme based on the bordered...
In automated test pattern generation (ATPG), test patterns are automatically generated and tested against all specific modeled faults. In this work, three optimization algorithms, namely: genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE), were studied for the purpose of generating optimized test sequence sets. Furthermore, this paper investigated the broad use...
The multidimensional assignment problem (MAP) is a natural extension of the well known assignment problem. A problem with s dimensions is called a SAP. The most studied NP-hard case of the MAP is the 3AP. Memetic algorithms have been proven to be the most effective technique to solve MAP. The use of powerful local search heuristics in combination with a genetic algorithm, even if it has a simple structure,...
Human life can be seriously affected by unusual high solar flare. It causes serious problems such as destroying satellites, damaging electric power plants, etc. Predicting solar flare peaks is indispensable. Support Vector Machine (SVM) was used to predict the solar flare intensity based on data of the past. However, such prediction is an extremely difficult imbalanced classification problem causing...
Existing clustering techniques primarily rely on prior knowledge about the data, such as the number of clusters and radii. However, in real applications, the number of clusters and the radii of clusters are usually unknown. Therefore, the performance of clustering methods with overlapping data is degraded due to their limitations in finding all cluster centers with uneven density values. Hence, a...
In this paper, an open source C++ Genetic Algorithm library is proposed called openGA. This library is capable of optimization in each of single objective, multi-objective and interactive modes. The main motivation for proposing this library is to provide freedom to users for designing their custom solution data model without limitations which many currently available software/libraries suffer from...
Based on the existing algorithm of fault-sectin location in distributed network containing distributed generation(DG), the effect of localization is not ideal, especially premature convergence problem in the original genetic algorithm, a new fault location method of chaotic optimization based on multiple-population genetic algorithm is proposed. Firstly, the introduction of a number of population...
The paper introduces the principle of traditional PID algorithm, analyzes its advantages and disadvantages, and proposes a new control scheme — variable structure fuzzy neural network for such a nonlinear and complex system of automatic Gauge control (AGC). Variable structure fuzzy neural network combines the advantages of neural network and fuzzy control, and also adds a genetic algorithm to optimize...
This paper first establishes the kinematic model and dynamic model of Selective Compliance Assembly Robot Arm(SCARA) robot based on Denavit-Hartenberg method and Lagrange equation. Then the model is simplified to reduce the computation, the kinetic equation is transformed into a linear form to get the observation matrix and the parameters to be identified. An incentive trajectory is designed to finish...
In recent years, with the high frequency of the infectious diseases outbreak, the prediction of the infectious diseases has become more and more important, so effective prediction of the infectious diseases can safeguard social stability and promote national economic prosperity. In order to improve the predictive accuracy of infectious diseases, the weight and threshold of BP neural network was optimized...
A approach using the adaptive genetic algorithm (AGA) is proposed for array failure correction in digital beam forming of arbitrary arrays. In this article we use adaptive genetic algorithm (AGA) change the excitation of elements to improve the performance of array with failed elements. Numerical examples are presented to show the effectiveness of the approach.
Evolutionary algorithms are optimization methods inspired by natural evolution. They usually search for the optimal solution in large space areas. In Evolutionary Algorithms it is very important to select an appropriate balance between the ability of the algorithm to explore and exploit the search space. The paper presents a hybrid system consisting of a Genetic Algorithm and an Evolutionary Strategy...
This paper presents multiple variances of selection operator used in Non-dominated Sorting Genetic Algorithm II applied to solving Bi-Objective Multi-Skill Resource Constrained Project Scheduling Problem. A hybrid Differential Evolution with Greedy Algorithm has been proven to work very well on the researched problem and so it is used to probe the multi-objective solution space. It is then determined...
Finding an optimal configuration for the number of worker processes and executors for a Storm topology is imperative for maximizing its performance. However, this process is both tedious and time-consuming due to the vast number of parameters to tune, their intertwined relationship with each other, and the temporal overhead of simply rebalancing a topology with a new set of configuration parameters...
Model matching is a fundamental operation for various model management aspects such as model retrieval, evolution, and merging. An accurate matching between the elements of the matched models results in a better model management. This paper presents a Hybridized Greedy-Genetic algorithm for matching UML class diagrams, considering their lexical, internal, and structural similarity. Additionally, using...
In the area of product design, adding user's kansei (feelings) on the products is important for emphasizing its value. Interactive Evolutionary Computation (IEC) is well known method that searches optimal or better media contents suited for each user's kansei. Some previous studies applied IEC into problem of multiple users. Purpose of the present study is to compare Distributed Interactive Genetic...
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