Average Fitness Value Research Articles

Fodder composition optimization using modified genetic algorithm

Determination of the fodder composition is a difficult process because it should simultaneously consider several constraints, such as minimizing the total cost of feed ingredients and maximizing the nutrient needs required by livestock. This study uses a modified genetic algorithm to solve the problem in order to obtain better results. The modification is done by applying numerical methods in generating an initial population of the genetic algorithm. Testing results show that the optimal parameters that can be used to produce the optimal solution are as follows: population size (popsize) is 300, generation number is 400, crossover rate (cr) value is 0.2, and mutation rate (mr) value is 0.6. The modified genetic algorithm provides an average fitness value of 0.142357, while the classical genetic algorithm provides an average fitness value of 0.094354. With additional computational time equal to 110 ms, the use of modified genetic algorithm offered has proven to provide a better result, with a higher fitness value compared with classical genetic algorithm.

Kapur's Entropy for Color Image Segmentation Based on a Hybrid Whale Optimization Algorithm.

In this paper, a new hybrid whale optimization algorithm (WOA) called WOA-DE is proposed to better balance the exploitation and exploration phases of optimization. Differential evolution (DE) is adopted as a local search strategy with the purpose of enhancing exploitation capability. The WOA-DE algorithm is then utilized to solve the problem of multilevel color image segmentation that can be considered as a challenging optimization task. Kapur’s entropy is used to obtain an efficient image segmentation method. In order to evaluate the performance of proposed algorithm, different images are selected for experiments, including natural images, satellite images and magnetic resonance (MR) images. The experimental results are compared with state-of-the-art meta-heuristic algorithms as well as conventional approaches. Several performance measures have been used such as average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), Wilcoxon’s rank sum test, and Friedman test. The experimental results indicate that the WOA-DE algorithm is superior to the other meta-heuristic algorithms. In addition, to show the effectiveness of the proposed technique, the Otsu method is used for comparison.

Automated face retrieval using bag-of-features and sigmoidal grey wolf optimization

A content based image retrieval system is one of the prime research fields due to the exponential increasing of multimedia data over Internet especially images. Although, a number of content based image retrieval methods have been introduced, it is still a challenging task specially for face recognition. Therefore, this work presents an automated face retrieval system using an enhanced bag-of-features framework. The bag-of-features framework has been modified by incorporating a new sigmoidal grey wolf optimization algorithm. The sigmoidal grey wolf optimization algorithm uses a sigmoid decreasing function to escape it from local optima. The efficiency of the proposed sigmoidal grey wolf optimization algorithm has been analyzed over various standard benchmark functions for average fitness values and convergence behavior. Furthermore, it has successfully been used to generate the codewords in bag-of-features framework. The modified bag-of-features has been utilized in content based image retrieval for Oracle Research Laboratory (ORL) face database. The simulation results represent that the proposed method effectively retrieves the faces as compared to other nature-inspired based methods.

Modified dragonfly algorithm based multilevel thresholding method for color images segmentation.

Accurate image segmentation is the preprocessing step of image processing. Multi-level threshold segmentation has important research value in image segmentation, which can effectively solve the problem of region analysis of complex images, but the computational complexity increases accordingly. In order to overcome this problem, an modified Dragonfly algorithm (MDA) is proposed to determine the optimal combination of different levels of thresholds for color images. Chaotic mapping and elite opposition-based learning strategies (EOBL) are used to improve the randomness of the initial population. The hybrid algorithm of Dragonfly Algorithms (DA) and Differential Evolution (DE) is used to balance the two basic stages of optimization: exploration and development. Kapur entropy, minimum cross-entropy and Otsu method are used as fitness functions of image segmentation. The performance of 10 test color images is evaluated and compared with 9 different meta-heuristic algorithms. The results show that the color image segmentation method based on MDA is more effective and accurate than other competitors in average fitness value (AF), standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and feature similarity index (FSIM). Friedman test and Wilcoxon's rank sum test are also performed to assess the significant difference between the algorithms.

A Novel Hybrid Harris Hawks Optimization for Color Image Multilevel Thresholding Segmentation

Multilevel thresholding has got more attention in recent years with various successful applications. However, the implementation becomes more and more complex and time-consuming when the number of thresholds is high, and color images which contain more information are even worse. Therefore, this paper proposes an alternative hybrid algorithm for color image segmentation, the advantages of which lie in extracting the best features from the high performance of two algorithms and overcoming the limitations of each algorithm to some extent. Two techniques, Otsu's method, and Kapur's entropy, are used as fitness function to determine the segmentation threshold values. Harris hawks optimization (HHO) is a novel and general-purpose algorithm, and the hybridization of HHO is fulfilled by adding another powerful algorithm-differential evolution (DE), which is known as HHO-DE. More specifically, the whole population is divided into two equal subpopulations which will be assigned to HHO and DE algorithms, respectively. Then both algorithms operate in parallel to update the positions of each subpopulation during the iterative process. In order to fully demonstrate the superior performance of HHO-DE, the proposed method is compared with the seven state-of-the-art algorithms by an array of experiments on ten benchmark images. Meanwhile, five measures, including the average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM), are used to evaluate the performance of each algorithm. In addition, Wilcoxon's rank sum test for statistical analysis and the comparison with the super-pixel method are also conducted to verify the superiority of HHO-DE. The experimental results reveal that the proposed method significantly outperforms other algorithms. Hence, the HHO-DE algorithm is a remarkable and promising tool for multilevel thresholding color image segmentation.

A Novel Method for Multilevel Color Image Segmentation Based on Dragonfly Algorithm and Differential Evolution

Multilevel thresholding is a simple and powerful image segmentation method that has received widespread attention in recent years. However, the accuracy and stability of thresholding techniques are still not ideal and cannot meet the needs of engineering problems. Therefore, this paper presents a memetic algorithm of dragonfly algorithm (DA) and differential evolution (DE) for color image segmentation, which is known as improved DA (IDA). On the one hand, DA algorithm has a satisfied capability of avoiding convergence to the local optimum, thus it is served as a global search technique. On the other hand, the DE algorithm is adopted as a local search technique, which can increase the precision of solutions. In this paper, two thresholding techniques, namely, Otsu and minimum cross entropy (MCE) methods are used to determine the optimal threshold values. In order to evaluate the performance of the proposed method, we conduct a series of experiments on color images from the Berkeley database and the results are compared with five state-of-the-art meta-heuristic algorithms. Besides, a non-parametric Wilcoxon's rank sum test is also included for statistical analysis. From the experimental results, it is found that IDA-based method outperforms other compared methods in terms of average fitness values, standard deviation, peak signal to noise ratio, structural similarity index, and feature similarity index. The promising results indicate that the application of IDA-based thresholding technique is potential and meaningful.

A fast simulated annealing algorithm for the examination timetabling problem

The timetabling problem involves the scheduling of a set of entities (e.g., lectures, exams, vehicles, or people) to a set of resources in a limited number of time slots, while satisfying a set of constraints. In this paper, a new variant of the simulated annealing (SA) algorithm, named FastSA, is proposed for solving the examination timetabling problem. In the FastSA’s acceptance criterion, each exam selected for scheduling is only moved (and the associated move is evaluated) if that exam had any accepted moves in the immediately preceding temperature bin. Ten temperature bins were formed, ensuring that an equal number of evaluations is performed by the FastSA, in each bin. It was observed empirically that if an exam had zero accepted movements in the preceding temperature bin, it is likely to have few or zero accepted movements in the future, as it is becoming crystallised. Hence, the moves of all exams that are fixed along the way are not evaluated no more, yielding a lower number of evaluations compared to the reference algorithm, the standard SA. A saturation degree-based heuristic, coupled with Conflict-Based Statistics in order to avoid any exam assignment looping effect, is used to construct the initial solution. The proposed FastSA and the standard SA approaches were tested on the 2nd International Timetabling Competition (ITC 2007) benchmark set. Compared to the SA, the FastSA uses 17% less evaluations, on average, and a maximum of 41% less evaluations on one instance. In terms of solution cost, the FastSA is competitive with the SA algorithm attaining the best average fitness value in four out of twelve instances, while requiring less time to execute. In terms of average comparison with the state-of-the-art approaches, the FastSA improves on one out of twelve instances, and ranks third among the five best algorithms.The article’s main impact comprises the points: (i) proposal of a new algorithm (FastSA) which is able to attain a reduced computation time (and number of evaluations computed) compared to the standard SA, (ii) demonstration of the FastSA capabilities on a NP-Complete timetabling problem, (iii) comparison with state-of-the-art approaches where the FastSA is able to improve the best known result on a benchmark instance. Due to the variety of problems solved by expert and intelligent systems using SA-based algorithms, we believe that the proposed approach will open new research paths with the creation of new algorithms that explore the space in a more efficient way.

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO.

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

Backtracking search algorithm for color image multilevel thresholding

Multilevel thresholding of the color images such as natural and satellite images becomes a challenging task due to the inherent fuzziness and ambiguity in such images. To address this issue, a modified fuzzy entropy (MFE) function is proposed in this paper. MFE function is the difference of adjacent entropies, which is optimized to provide thresholding levels such that all regions have almost equal entropies. To improve the performance of MFE, backtracking search algorithm is used. The numerical and statistical results indicate that MFE-BSA has higher peak signal-to-noise ratio, lower mean square error for all the images at different thresholding levels. Moreover, structural and feature similarity indices for MFE-BSA are closer to unity and the average fitness value obtained using MFE-BSA is minimum (lesser than 0.5). Overall, MFE-BSA shows very good segmentation results in terms of preciseness, robustness, and stability.

Toward a Steady-State Analysis of an Evolution Strategy on a Robust Optimization Problem With Noise-Induced Multimodality

A steady state analysis of the optimization quality of a classical self-adaptive evolution strategy (ES) on a class of robust optimization problems is presented. A novel technique for calculating progress rates for nonquadratic noisy fitness landscapes is presented. This technique yields asymptotically exact results in the infinite population size limit. This technique is applied to a class of functions with noise-induced multimodality. The resulting progress rate formulas are compared with high-precision experiments. The influence of fitness resampling is considered and the steady state behavior of the ES is derived and compared with simulations. The questions whether one should sample and average fitness values and how to choose the truncation ratio are discussed giving rise to further research perspectives.

Fuzzy immune particle swarm optimization algorithm and its application in scheduling of MVB periodic information1

This paper addresses the scheduling problem of periodic information in multifunction-vehicle-bus networked control system (MNCS). To deal with this issue, a novel fuzzy immune particle swarm optimization (FIPSO) algorithm is proposed to eliminate the premature convergence of standard PSO algorithms in solving complex problem, which uses the fuzzy particle swarm optimization (FPSO) algorithm and the immune particle swarm optimization (IPSO) algorithm for reference. Through designing the fuzzy logic controller (FLC), the inertia weight and the immune-execute factor are regulated dynamically on the basis of the evolutionary time, the variation of average fitness value and the population diversity. The FLC also guides evolutionary directions and decides whether to execute immune operations, making the algorithm converge for many times. The simulation and calculation results of the scheduling example show that FIPSO algorithm has strong global search ability, good stability and better optimization performance. Particularly, based on the variable individual periods, the co-design of scheduling and control in MNCS is implemented by applying the FIPSO algorithm.

Multi-agent decision support system for missile defense based on improved PSO algorithm

Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as developing a real one requires lots of manpower and resources. BMDS is a typical complex system for its nonlinear, adaptive and uncertainty characteristics. The agent-based modeling method is well suited for the complex system whose overall behaviors are determined by interactions among individual elements. A multi-agent decision support system (DSS), which includes missile agent, radar agent and command center agent, is established based on the studies of structure and function of BMDS. Considering the constraints brought by radar, intercept missile, offensive missile and commander, the objective function of DSS is established. In order to dynamically generate the optimal interception plan, the variable neighborhood negative selection particle swarm optimization (VNNSPSO) algorithm is proposed to support the decision making of DSS. The proposed algorithm is compared with the standard PSO, constriction factor PSO (CFPSO), inertia weight linear decrease PSO (LDPSO), variable neighborhood PSO (VNPSO) algorithm from the aspects of convergence rate, iteration number, average fitness value and standard deviation. The simulation results verify the efficiency of the proposed algorithm. The multi-agent DSS is developed through the Repast simulation platform and the constructed DSS can generate intercept plans automatically and support three-dimensional dynamic display of missile defense process.

Implementasi Algoritma Genetika Pada Penempatan Tugas Asisten Laboratorium Berbasis Web

On education system in University, the given subject is not only theoretics subject, but practicum also. In teaching practical subjects, the lecturer often experiences a difficulty to answer and handle every question that the students have in laboratory. Therefore, the assistant laboratory is needed here. One of the division in Universitas Multimedia Nusantara, ICT LAB still arranges the assignment of the assistant laboratory manually. It is difficult and takes some times. This thing becomes the reason for the development of an application ICT LAB that can do the assignment process automatically. This research is using Genetic algorithm that can calculate fitness value which show the matching level of the assignment that has been created. Based on the testing, using one point crossover, mutation on bit level, and rank selection, the average fitness value is 68.571% from all of the laboratory assistant vacancy. Based on the data used, there are 22 assignment slots on certain subjects that don’t have any applicant. So if that 22 slots are not used in the fitness calculation, there are only 118 assignment slot and the fitness value becomes 80,5%. This fitness value has problem with the quantity of student that didn’t fit to all of the assignment slot.
 Index Terms - Genetic Algorithm, rank selection, one point crossover, assignment, laboratory assistant.

Effects of different torque levels on the implant-abutment interface in a conical internal connection.

The fit of the implant-abutment interface was assessed by the metallographic technique and by scanning electron microscopy (SEM), using solid abutment types at different torque levels. Forty Morse taper connections and forty solid abutments were used at different torque levels (repeated after 10 minutes) in the following groups (n = 10): 25 Ncm (group g1), 30 Ncm (group g2), 35 Ncm (group g3), and 40 Ncm (group g4). The samples were embedded in a metallographic resin, sectioned lengthwise, and polished. SEM images were used to measure the linear contacts and the fits between abutments and the internal walls of the implant. The overall mean gap and standard deviation were as follows: 9.0 ± 1.36 µm for group g1, 7.9 ± 2.81 µm for group g2, 2.0 ± 0.76 µm for group g3, and 0.3 ± 0.40 µm for group g4. A significant difference was observed in the average fit values between the groups (p < 0.05). The linear area of contact between the abutment and the implant increased as torque augmented. This study demonstrated that higher insertion torque values in a conical internal connection increase the fit (contact) of the implant-abutment interface.

Contribution of microsatellites markers in the clarification of the origin, genetic risk factors, and implications for conservation of Tunisian native sheep breeds.

The genetic diversity and genetic relationship of the two main groups of African sheep, thin-tailed and fat-tailed sheep, represented by the indigenous Tunisian sheep breeds "Barbarine" (BAR, fat-tailed) and "Queue Fine de l'Ouest" (QFO, thin-tailed) were investigated. The genotypes of 110 animals belonging to these two breeds and their crossbreed (CRO) were assessed using 17 microsatellite markers. The results showed high levels of genetic diversity and a total of 256 alleles were identified in the whole population. The mean values of observed and expected heterozygosity were 0.719 and 0.789, respectively, and the mean allelic richness estimate was 10.89. The average FIS (0.112) and FIT (0.118) values over all loci indicated a notable level of inbreeding within the whole population. However, the FST value (0.007) showed a low level of genetic differentiation between these two native breeds. The high level of both gene flow and molecular coancestry coefficient detected between the two breeds and their CRO revealed an old miscegenation between the BAR and QFO breeds. The clustering analysis performed with the STRUCTURE software confirmed gene flow between these two breeds. Results arising from this study provide evidence regarding the genetic structure and variability of the two main local sheep breeds, and the implications of their actual management, which indicates the need for an urgent conservation strategy in order to prevent significant gene flow and preserve the remaining breed specificity for future generations.

Optimal Configuration of PMU in Power System

In order to use the least number of phasor measurement unit (PMU) to guarantee the power system completely observable, an optimal PMU configuration method in power system was put forward. The pre-configuration of PMU was done considering the actual situation of power grid. The genetic algorithm (GA) was used for PMU configuration. Modify the formulae of crossover probability and mutation probability in traditional genetic algorithm to overcome the evolutionary stagnation when the maximum fitness value and the average fitness value in group were equal. The improved adaptive genetic algorithm (IAGA) was obtained. In order to eliminate the premature convergence of GA resulted from the chance and randomness of the crossover operation and mutation operation, the preventing premature operation was introduced. This method combined the IAGA and the preventing premature operation. It has good global astringency, and it can ensure the network complete observability with the minimum number of PMU.

A novel data assimilation methodology for predicting lithology based on sequence labeling algorithms

AbstractA hidden Markov model (HMM) and a conditional random fields (CRFs) model for lithological predictions based on multiple geophysical well‐logging data are derived for dealing with directional nonstationarity through bidirectional training and conditioning. The developed models were benchmarked against their conventional counterparts, and hypothetical boreholes with the corresponding synthetic geophysical data including artificial errors were employed. In the three test scenarios devised, the average fitness and unfitness values of the developed CRFs model and HMM are 0.84 and 0.071 and 0.81 and 0.084, respectively, while those of the conventional CRFs model and HMM are 0.78 and 0.091 and 0.77 and 0.099, respectively. Comparisons of their predictabilities show that the models designed for directional nonstationarity clearly perform better than the conventional models for all tested examples. Among them, the developed linear‐chain CRFs model showed the best or close to the best performance with high predictability and a low training data requirement.

Genetic diversity, structure, and breed relationships in Tunisian sheep

The aim of this research was (i) to quantify the levels of genetic variability in Tunisian sheep breeds and (ii) to investigate their population structure and the extent of admixture, to provide information for their conservation. Blood samples from 249 sheep, belonging to 5 breeds and 1 crossbred population were collected across different agroecological zones in Tunisia. Tunisian sheep and 31 Appenninica Italian sheep (APP), used as an out-group, were genotyped at 17 microsatellite loci. All the sheep breeds investigated were genetically diverse, as evidenced by the high allele (>7) and gene (>0.7) diversity values. The average FIS (0.105) and FIT (0.132) values indicated significant levels of inbreeding within these breeds. The average FST estimate (0.030) between all breeds and Reynolds’ genetic distance revealed the close relationship among native Tunisian breeds and their clear distinction from the D’man exotic breed and APP. The clustering analysis performed with STRUCTURE also evidenced that native Tunisian breeds could be considered as subpopulations belonging to one breed. The high level of admixture found also reflects the sheep breeding history of Tunisia. Application of this knowledge may be useful in breeding programs and genetic management of breeds. These findings represent a starting point for the characterization of Tunisian sheep breeds suggesting the need to set up accurate conservation measures aiming to safeguard and monitor their genetic variability.

Improved Artificial Bee Colony Algorithm for Continuous Optimization Problems

The artificial bee colony (ABC) algorithm is a swarm-based metaheuristic optimization technique, developed by inspiring foraging and dance behaviors of honey bee colonies. ABC consists of four phases named as initialization, employed bee, onlooker bee and scout bee. The employed bees try to improve their solution in employed bees phase. If an employed bee cannot improve self-solution in a certain time, it becomes a scout bee. This alteration is done in the scout bee phase. The onlooker bee phase is placed where information sharing is done. Although a candidate solution improved by onlookers is chosen among the employed bee population according to fitness values of the employed bees, neighbor of candidate solution is randomly selected. In this paper, we propose a selection mechanism for neighborhood of the candidate solutions in the onlooker bee phase. The proposed selection mechanism was based on information shared by the employed bees. Average fitness value obtained by the employed bees is calculated and those better than the aver- age fitness value are written to memory board. Therefore, the onlooker bees select a neighbor from the memory board. In this paper, the proposed ABC-based method called as iABC were applied to both five numerical benchmark functions and an estimation of energy demand problem. Obtained results for the problems show that iABC is better than the basic ABC in terms of solution quality.

Multi-Subexpression Programming

Gene Expression Programming is a new and adaptive brand evolution algorithm which is developed on the basis of genetic algorithm. In recent years, Multi-Expression Programming which is proposed in the genetic programming is a linear structure coding scheme,its main feature is a chromosome contains multiple expressions. The idea of MEP is introduced into the GEP in this paper, so a single GEP gene contains multiple solutions to solve the problem.The new algorithm analyzes each gene in the GEP to extract relational subexpressions, then fitness evaluate certain subexpressions to choose the best fitness as individuals fitness, and carry on related genetic manipulation. Finally, the improved algorithm experiment with GEP and MEP, compare their mining the same functions ability,record average fitness value and success rate. The experiment results show that the improved algorithm has better evolutionary efficiency.