Unsupervised Feature Selection Using the Atomic Orbital Search Algorithm for Information Retrieval

The increasing growth of the news and a large number of users on information retrieval (IR) has resulted in making the retrieval of documents complex and more difficult. The IR process consists of pre-processing, extraction and representation, feature selection and indexing, querying, and retrieving results. The weakness of IR is concerned with the process of extraction, where most important words focused on verbs or nouns. Unsupervised Feature selection is an important task in content classification for being among the most popular and effective methods for retrieval reduction. The use of the verbs and nouns as extraction was recently introduced in the IR technique to avoid irrelevant and redundant unsupervised features. This paper aims to enhance IR using noun-verb extraction using Word-Net and Krill Herd Algorithm as Unsupervised Feature Selection (KHUFS) combine with Simulated Annealing as Unsupervised Feature Selection to find the suitable retrieval of ranking. The external of Mean Average Precision (MAP) and Mean Average Recall (MAR) internal of Mean Average Distance (MAD) as measurements were used to verify the proposed retrieval of ranking. The results demonstrate that the proposed nouns verbs method extraction outperformed other extraction methods, in which the proposed extraction was 26.15 % using MAP measure and using MAR was 45.41%. In comparison with other unsupervised feature selection algorithms such as Harmony Search, Simulated Annealing, Particle Swarm, and Genetic Algorithm, the combined combination outperformed other unsupervised feature selection algorithms with an accuracy 26.163 % MAP, and 11.653% MAR. On the other side, the effect of use proposed extraction on the proposed unsupervised feature selection was 39.563% MAP, and 8.96% MAR. The other evaluation using the number of features, the using combined Krill Herd with Simulated Annealing number of features has decreased to more than 50 %, which the total feature in the dataset was 10682 features and after used the proposed was 4723 features.

 The water distribution network (WDN), a vital component of the water supply system, is an essential urban infrastructure distributing potable water to society. Its design, a non-deterministic polynomial-hard problem, has been a widely studied complex research problem for decades, with various optimization models proposed for its optimal design. Recent advancements in enhancing the computational efficiency of stochastic optimization algorithms by introducing chaotic force have elevated the scope of formulating chaos-directed evolutionary algorithms (EAs). The present study proposes one such approach, the chaos-directed genetic algorithm (CDGA) model, to improve the search mechanism of the genetic algorithm (GA) in solving the complex optimization problem of WDN optimal design.In one of our recent works, the influence of chaotic maps with high-dimensionality, the Henon and Lorenz maps, are explored and compared to the low-dimensional Logistic map in improving the performance of GA. With the one-dimensional Logistic map demonstrating better computational improvement of GA, the present study considers it for formulating the CDGA model. The Logistic map is a non-linear first-order difference equation. Its dynamics evolve into various possible states of system range without repetition. For the search mechanism of the optimization technique to explore different regions of search space, this particular characteristic forms the most favorable feature. Consequently, by incorporating the chaotic force of the Logistic map into GA’s evolutionary mechanism by replacing every random search phenomenon, the CDGA model is formulated. A novel method of non-sequential allocation of chaotic dynamics is employed to induce chaotic force. Notably, the method is unique, using the same initial characteristics of the Logistic equation, retaining the chaos ergodicity for the evolutionary search.To demonstrate the computational efficiency of the CDGA model, the enormously studied benchmark problem, the Hanoi network (HN), is considered. HN is a 34-dimensional problem having a complex search space with multiple locally optimal solutions. Defining the WDN optimization problem as the single-objective design framework subjected to linear and non-linear constraints of governing laws, the principal objective is to minimize the investment cost of HN pipes. While minimizing the pipe investment cost, the constraints levied ensure that the HN is hydraulically adequate to deliver the design demands. Thus, the optimization model formulated is the integrated framework with the simulation tool to simulate the WDN's hydraulic conditions. The code for the CDGA model is written in MATLAB R2015a and combined with the simulation software, EPANET 2.0, using the EPANET-MATLAB toolkit.The computational results demonstrate the convergence precision of the CDGA model over its traditional GA, converging to the optimal cost of 6,081,564 units, the previous best solution reported for HN in the literature. Moreover, it outperforms many stochastic optimization models reported in the literature with computational efficiency in solving HN, particularly simulated annealing, shuffled complex, shuffled frog leaping, ant colony, particle swarm, harmony search, krill herd, and cuckoo search algorithms. Hence, from the results, the study suggests formulating chaos-directed optimization algorithms to improve their traditional model's computational efficiency in solving complex optimization problems.Keywords: Water distribution network optimal design; Evolutionary algorithms; Genetic algorithm; Chaotic maps; Logistic equation; Hanoi network  

In general Digital Signal Processing (DSP) and more especially filtering is an important and basic requirement for signal systems, computers, and communication networks. The design of Optimal Digital Filters is a intimidating task and it has challenged the scientist, engineers, and researchers for designing the filters with improvised, proficient, and intelligent techniques using the Emerging Evolutionary Computations. Metaheuristics have emerged as the best promising tool and an striking area of research with numerous improvements and advancements in the solution to optimization issues. However, it has not shown clarity to decide the best performing metaheuristic for designing an optimal digital filter. In this paper, a comprehensive review and analysis of various metaheuristics used by researchers for designing an optimal digital filter are carried out. More specifically, “Finite Impulse Response (FIR)” and “Infinite Impulse Response (IIR)” filter design using optimization-based techniques such as nature-inspired “Swarm Intelligence (SI) Swarm Intelligence (SI), Cuckoo Search (CS), Grasshopper Optimization Algorithms,, Particle Swarm Optimization, (PSO), Ant Colony Optimization (ACO Bat Algorithms (BA), Genetic Algorithms (GA), Artificial Bee Colony (ABC), Bacterial foraging optimization (BFO, Biogeography-based optimization (BBO), Harmony search (HS), Krill herd (KH), Social spider optimization (SSO), Symbiotic organisms search (SOS), Firefly algorithm (FA), Gravitational search algorithm (GSA), Grey wolf algorithm (GWO), Teaching-learning-based optimization (TLBO), Whale optimization algorithm (WOA)” are also described. Also, a survey on the origin of twenty-one optimization algorithms is carried out that is being proposed as optimization algorithms in literature for the proposal of digital filters.

An electronic nose (E-nose) is an intelligent system that we will use in this paper to distinguish three indoor pollutant gases (benzene (C6H6), toluene (C7H8), formaldehyde (CH2O)) and carbon monoxide (CO). The algorithm is a key part of an E-nose system mainly composed of data processing and pattern recognition. In this paper, we employ support vector machine (SVM) to distinguish indoor pollutant gases and two of its parameters need to be optimized, so in order to improve the performance of SVM, in other words, to get a higher gas recognition rate, an effective enhanced krill herd algorithm (EKH) based on a novel decision weighting factor computing method is proposed to optimize the two SVM parameters. Krill herd (KH) is an effective method in practice, however, on occasion, it cannot avoid the influence of some local best solutions so it cannot always find the global optimization value. In addition its search ability relies fully on randomness, so it cannot always converge rapidly. To address these issues we propose an enhanced KH (EKH) to improve the global searching and convergence speed performance of KH. To obtain a more accurate model of the krill behavior, an updated crossover operator is added to the approach. We can guarantee the krill group are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iterations. The recognition results of EKH are compared with those of other optimization algorithms (including KH, chaotic KH (CKH), quantum-behaved particle swarm optimization (QPSO), particle swarm optimization (PSO) and genetic algorithm (GA)), and we can find that EKH is better than the other considered methods. The research results verify that EKH not only significantly improves the performance of our E-nose system, but also provides a good beginning and theoretical basis for further study about other improved krill algorithms’ applications in all E-nose application areas.

Recently, Gandomi and Alavi proposed a robust meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization. To improve the performance of the KH algorithm, harmony search (HS) is applied to mutate between krill during the process of krill updating instead of physical diffusion used in KH. A novel hybrid meta-heuristic optimization approach HS/KH is proposed to solve global numerical optimization problem. HS/KH combines the exploration of harmony search (HS) with the exploitation of KH effectively, and hence, it can generate the promising candidate solutions. The detailed implementation procedure for this improved meta-heuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most cases, the performance of this hybrid meta-heuristic method (HS/KH) is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, HS, KH, PSO, and SGA. The effect of the HS/FA parameters is also analyzed.

Harmony search (HS) algorithm is a heuristic optimization algorithm which is newly developed in recent years. In this paper, according to the shortcomings of the existing harmony search algorithm, an improved adaptive harmony search algorithm (IAHS) is proposed. In the IAHS algorithm, the adaptive parameters HMCR and PAR and BW are used to adjust the global and local search, so as to improve the robustness and speed of convergence of the algorithm. IAHS algorithm is tested by five standard benchmark functions and contrasted with HS、HIS and GHS algorithm. Experimental results demonstrated that the proposed IAHS algorithm has the favorable abilities of accuracy and escaping local minimums. Introduction Harmony search (HS) algorithm is a new heuristic optimization algorithm which put forward by Geem, etc in 2001 [1]. Similar with Particle swarm optimization (PSO) algorithm, HS algorithm is based on the music improvisation process. In the process, musicians repeatedly adjusting the tone of each instrument in the band, and finally get a good harmony. HS algorithm has the advantages of simple model, strong randomicity, good ergodicity and global search ability. HS and its improved algorithm has been successfully applied to many practical optimization problems [2-5], such as environmental economic load dispatch optimization, traffic path optimization, the optimization of water distribution system and fault location in distribution networks and other issues. Since the advent of harmony search algorithm, a series of achievements are made in various fields such as optimization problem: bus lines, water network design problem, the problem of reservoir scheduling, civil engineering problems. At present, this method has been widely applied in the problem of multi dimensional function optimization, pipeline optimization design, slope stability analysis etc. Research shows that the HS algorithm in solving multidimensional function optimization problems show a genetic algorithm and simulated annealing algorithm to optimize the better. However, the standard HS exists some defects such as setting BW blindly harmony memory diversity gradually dissipating with iterations, falling into local optimum easily, low accuracy and so on[6].Therefore, in order to improve the performance of the HS, this paper propose an improved adaptive harmony search algorithm (IAHS) whose parameters are adjusted adaptively. IAHS algorithm is tested by five standard benchmark functions and contrasted with HS、HIS and GHS algorithm. Experimental results demonstrated that the proposed IAHS algorithm has the favorable abilities of accuracy and escaping local minimums. Standard harmony search algorithm Harmony search (HS) algorithm was inspired by the improvising process of composing a piece of music. In the play, each player generates a tone to constitute a harmony vector. If the harmony is better, write it down, so that the next time to produce better harmony. In the algorithm, the tones of music instrument are analogous to the decision variables (i 1, 2, , n) j xi   of the optimization problem, each harmony are analogous to the solution vector ( , , , ) 1 2 j j j j x x x xn   , aesthetic evaluation are analogous to the objective function   j f x , the musicians want to find the beautiful harmonies by defined by aesthetic evaluation, the researchers want to find the global optimal solution defined by objective function .HS algorithm contains a series of optimization International Conference on Computational Science and Engineering (ICCSE 2015) © 2015. The authors Published by Atlantis Press 54 factors, such as the harmony memory (HM), harmony memory size (HMS), harmony memory consideration rate (HMCR), pitch adjusting rate (PAR), bandwidth (BW) stores the feasible solution vector represents the probability is the probability of disturbing to the Aesthetic evaluation is issued by the decision, as the value of the objective function brief description is given of the above statement Table 1 Analog elements best condition be evaluated by

In this paper, two recent heuristic optimization algorithms are presented to optimally manage the operation of the micro-grid (MG) with installed renewable energy sources (RESs); krill herd (KH) optimization and ant lion optimizer (ALO) algorithms. The first algorithm is used for solving single-objective function represents either total operation cost or total pollutant emission injected from the installed generating units while ALO is applied to solve the multi-objective function of both total operating cost and emission. The problem is formulated as nonlinear constrained objective function with equality and inequality constraints. In this work; the devices installed in MGs are photovoltaic panel (PV), wind turbine (WT), micro-turbine (MT), fuel cell (FC), battery and grid. Two scenarios are studied; the first one is optimizing MG with installing all RESs within specified limits in addition to grid, while the second scenario is operating both PV and WT at their rated powers. The obtained results are compared with different reported algorithms like genetic algorithm (GA), Fuzzy self-adaptive PSO (FSAPSO) and others programmed like particle swarm optimization (PSO), grey-wolf optimizer (GWO) and whale optimization algorithm (WOA). For first scenario; the proposed KH gives the best optimal cost of 105.94 €ct while the best emission is 420.57 kg, the best optimal cost and emission of 592.86 €ct 339.71 kg are obtained via KH in the second scenario.

Metaheuristic approaches with extremely important improvements are very promising in the solution of intractable optimization problems. The objective of the present study is to test the capability of applications and compare the performance of the four selected algorithms from “classical” (simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE)) and “new generation” (firefly algorithm (FFA), krill herd (KH), grey wolf optimization (GWO), and symbiotic organism search (SOS)) each by solving selected benchmark problems that are used in the literature for algorithm testing purpose. The selected test problems had very complex objective functions and associated constraints with multiple local optima. Among all selected algorithms, the “new generation” SOS and KH algorithm successfully solved most of all the selected benchmark problems and achieved the best solution for most of them. Among four “classical” algorithms, DE, and PSO effectively attained the optimal solution which was very close to the best one. However, the “new generation” algorithm performed much better than the “classical” one. Therefore, no firm conclusion can be done about the universally best algorithm and their performance may be varied for different benchmark problems. However, in this study for the seven selected test problems, SOS and KH exhibited the most promising result and great potential with respect to execution time also. This study gives some insights to use SOS and KH as the best-performing algorithms to the novice user who can easily get lost in the plethora of large optimization algorithms.

Spam is defined as junk and unwanted e-mail. The implementation of a reliable spam email filter becomes more and more important for e-mail users since they have to face with the growing amount of uninvited e-mails. The faults of spam classifiers are characterized by being more and more insufficient to handle huge volumes of relevant emails and to identify and detect the new spam email as example with high performance. The problem in spam classifiers is a huge number of features. Feature selection is an important task in keyword content classification for being among the most popular and effective methods for feature reduction. Accordingly, irrelevant and redundant features that can impede performance would be eliminated. Meta-heuristic optimization is to choose the optimal solution between possible multi-solutions, which respect the aim of this research that is the performance. The other problem is related to ambiguity of the effect of optimization feature selection on multiple classifiers algorithm which are popular used by previous work namely; K-nearest Neighbor, Naive Bayesian and Support Vector Machine. Therefore, the aim of this research is to improve the accuracy of feature selection by applying hybrid Water Cycle and Simulated Annealing to optimize results and to evaluate the proposed Spam Detection. The methodology used in this study which consists of groundwork, induction, improvement, evaluation and comparison quality. The cross-validation was used for training and validation dataset and seven datasets were employed in testing the spam classification proposed. The results demonstrate that the meta-heuristic namely water cycle feature selection (WCFS) was employed and three ways of hybridization with Simulated Annealing as a feature selection employed. In comparison with other feature selection algorithms such as Harmony Search, Genetic Algorithm, and Particle Swarm, the hybridization interleaved hybridization outperformed other feature selection algorithms with accuracy 96.3%, on the other side the effect of using three classifier algorithms, the SVM was better than other of classifier algorithms with f-measurement 96.3%. The number of features using interleaved water cycle and Simulated Annealing the number of features has decreased to more than 50%.

Hyperspectral images (HSIs) have recently been exploited in several aspects as HSIs contain many contiguous and narrow discriminative spectral bands. The problem of dimensionality is a significant dilemma for HSIs due to there being plenty of irrelevant and redundant spectral bands and highly correlated bands that lead to Hughes phenomenon. To this end, we present an approach to selecting the most informative and relevant spectral bands for HSI dimensionality reduction using the Krill Herd (KH) algorithm. Moreover, KH is a heuristic search method that seeks to reach the optimum global solution within the search space and effectively evade falling into the local optima. Then an edge-preserving filter was employed to extract the spatial features while reducing noise and obtaining a suitable smoothing that improves the classification performance. Finally, the support vector machine classifier was performed at the pixel level for HSI classification. Furthermore, the proposed work was compared with the harmony search, genetic algorithm, bat algorithm, particle swarm optimization, and firefly algorithm. The experimental results demonstrated outstanding performance with an overall accuracy equal to 96.54%, 98.93%, 99.78%, and 98.66% on four hyperspectral datasets: Indian Pines scene, Pavia University scene, Salinas scene, and Botswana scene, respectively.

Harmony search (HS) was introduced in 2001 as a heuristic population-based optimisation algorithm. Since then HS has become a popular alternative to other heuristic algorithms like simulated annealing and particle swarm optimisation. However, some flaws, like the need for parameter tuning, were identified and have been a topic of study for much research over the last 10 years. Many variants of HS were developed to address some of these flaws, and most of them have made substantial improvements. In this paper we compare the performance of three recent HS variants: exploratory harmony search, self-adaptive harmony search, and dynamic local-best harmony search. We compare the accuracy of these algorithms, using a set of well-known optimisation benchmark functions that include both unimodal and multimodal problems. Observations from this comparison led us to design a novel hybrid that combines the best attributes of these modern variants into a single optimiser called generalised adaptive harmony search.

Feature selection is selecting a subset of optimal features. Feature selection is being used in high dimensional data reduction and it is being used in several applications like medical, image processing, text mining, etc. Several methods were introduced for unsupervised feature selection. Among those methods some are based on filter approach and some are based on wrapper approach. In the existing work, unsupervised feature selection methods using Genetic Algorithm, Particle Swarm Optimization with Relative Reduct, Quick Reduct and Ant Colony Optimization have been introduced. These methods yield better performance for unsupervised feature selection. In this paper we proposed a novel method to select subset of features from unlabeled data using binary bat algorithm with sum of squared error as the fitness function. The proposed method is then tested with various classification algorithms like decision tree, multilayer perceptron, support vector machine and clustering quality measures like sum of squared error. The results show that our proposed method gives more accuracy when compared with other optimization algorithm.

Unsupervised soft-label feature selection

This paper proposes a new multiobjective hybrid particle swarm optimization (PSO)–krill herd (KH) Pareto-based optimization algorithm to optimize number and location of the measurement devices for accurate state estimation (SE) in smart distribution networks. Three objectives are considered to be minimized: 1) the total cost; 2) the average relative percentage error (APE) of bus voltage magnitude; and 3) APE of bus voltage angle. As the objective functions are conflicting with respect to each other, a multiobjective Pareto-based nondominated sorting hybrid PSO–KH optimization algorithm is proposed. In this approach, the random variation in loads and the metrological error of the measurement devices are also taken into account. The proposed algorithm minimizes the cost and enhances the accuracy of the distribution state estimator for better monitoring and control of the system. Furthermore, the impacts of distributed generation on SE performance are also investigated. The feasibility of the proposed algorithm is demonstrated on IEEE 69-bus system and practical Indian 85-bus radial distribution network. The results obtained are compared with conventional KH algorithm and PSO, with well-known multiobjective nondominated sorting genetic algorithm and also with an existing technique based on dynamic programming method for validation.

Image segmentation is one of the first steps within the framework for processing scenes. Among the main existing techniques, we highlight the histogram-based binarization, which due to the simplicity of understanding and low computational complexity is one of the most used methods. However, for a multi-threshold process, this method becomes computationally costly. To minimize this problem, optimization algorithms are used to find the best thresholds. Recently, several algorithms inspired by nature have been proposed in a generic way in the area of combinatorial optimization and obtained excellent results, among which we highlight the more traditional ones such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution Algorithm (DE), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Krill Herd (KH). This work shows a comparison between some of these algorithms and more recent algorithms, from 2014, as Grey Wolf Optimizer (GWO), Elephant Herding Optimization (EHO), Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA) and Harris Hawks Optmization (HHO) . This work compared the thresholds obtained by 7 bio-inspired algorithms in a base composed of 100 images with 1 single object provided by the Weizmann Institute of Science (WIS). The comparison was made using consolidated metrics like Dice/Jaccard and PSNR, as well the recent Hxyz. In the experiments were used the extensive system as an objective function (Kapurs´ Method). Still in the proposal of this experiment, the extensive system was compared with a Tsallis nonadditive entropy, with the Super-extensive system being configured with q ? [0.1, 0.2, . . . 0.9] and the Sub-extensive system with q ? [1.1, 1.2, . . . 1.9]. The image Database contains 100 images with only 1 object on scene. The results show that the Krill Herd (KH) algorithm was the winning algorithm in 35% of executions according to the PSNR metric, 28% in the Dice/Jaccard metric and 35% on the Hxyz metric. The extensive system had the best overall performance and was responsible for the best threshold of 54 images according to the metric PSNR, 30 according to the metric Dice/Jaccard and 39 according to the Hxyz metric