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In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is still open for further investigations since no FS method gives cutting-edge results for all cases. In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. The best performance is achieved by BERSOC followed by BERSO and then BRSO. These proposed versions are comparatively assessed against 25 well-regarded metaheuristic methods and five filter-based approaches. The obtained results underline their superiority by producing new best results for some datasets., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses when they are trying to survive. To build a Binary version of HOA, or referred to as BHOA, twofold of adjustments were made: i) Three transfer functions, namely S-shape, V-shape and U-shape, are utilized to transform the continues domain into a binary one. Four configurations of each transfer function are also well studied to yield four alternatives. ii) Three crossover operators: one-point, two-point and uniform are also suggested to ensure the efficiency of the proposed method for FS domain. The performance of the proposed fifteen BHOA versions is examined using 24 real-world FS datasets. A set of six metric measures was used to evaluate the outcome of the optimization methods: accuracy, number of features selected, fitness values, sensitivity, specificity and computational time. The best-formed version of the proposed versions is BHOA with S-shape and one-point crossover. The comparative evaluation was also accomplished against 21 state-of-the-art methods. The proposed method is able to find very competitive results where some of them are the best-recorded. Due to the viability of the proposed method, it can be further considered in other areas of machine learning., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Gene expression data are expected to make a great contribution in the producing of efficient cancer diagnosis and prognosis. Gene expression data are coded by large measured genes, and only of a few number of them carry precious information for different classes of samples. Recently, several researchers proposed gene selection methods based on metaheuristic algorithms for analysing and interpreting gene expression data. However, due to large number of selected genes with limited number of patient's samples and complex interaction between genes, many gene selection methods experienced challenges in order to approach the most relevant and reliable genes. Hence, in this paper, a hybrid filter/wrapper, called rMRMR-MBA is proposed for gene selection problem. In this method, robust Minimum Redundancy Maximum Relevancy (rMRMR) as filter to select the most promising genes and an modified bat algorithm (MBA) as search engine in wrapper approach is proposed to identify a small set of informative genes. The performance of the proposed method has been evaluated using ten gene expression datasets. For performance evaluation, MBA is evaluated by studying the convergence behaviour of MBA with and without TRIZ optimisation operators. For comparative evaluation, the results of the proposed rMRMR-MBA were compared against ten state-of-arts methods using the same datasets. The comparative study demonstrates that the proposed method produced better results in terms of classification accuracy and number of selected genes in two out of ten datasets and competitive results on the remaining datasets. In a nutshell, the proposed method is able to produce very promising results with high classification accuracy which can be considered a promising contribution for gene selection domain., (Copyright © 2019 Elsevier Inc. All rights reserved.)

To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.

The Internet of Vehicles (IoV) is an Intelligent Transportation System, which in its turn is an application of the Internet of Things. The IoV is a network of connected vehicles that sends/receives messages. However, the moving nature of the vehicles in IoV networks raises a dynamic topology issue. Therefore, establishing effective and dependable communication pathways among vehicular nodes, contingent upon traffic density conditions, presents a growing challenge. As a result, the main contribution of this paper is to propose an efficient network optimization system involving a cluster-based network routing optimizer. The proposed method is a metaheuristic algorithm adapted as a clustering algorithm. For this purpose, the JAYA Algorithm (JAYA) was utilized and integrated with two clustering concepts extracted from K-means Clustering and Automatic Clustering, resulting in proposing JAYA Clustering Algorithm for IoV (JAYACIoV). Subsequently, the performance of the proposed algorithm was investigated utilizing 176 network testing scenarios, and extensive comparative evaluations were conducted against five algorithms from the literature. The results confirmed the superiority of the proposed system with a percentage of 64.82%, where the algorithms were ranked according to the percentage achievement of best results. [ABSTRACT FROM AUTHOR]

This paper presents an effective approach for solving economic load dispatch problems contemplating the scheduling a set of thermal generating units to produce a specific power at low consumption costs. These problems can be thought of as nonlinear, non-convex, and highly constrained optimization problems with a large number of local minima. To cope with the above issues in solving such problems, a new meta-heuristic named capuchin search algorithm was adopted. To boost the search performance of this algorithm as well as to mitigate its early convergence and regression to the local optimum, it was hybridized with another algorithm and improved using several positive amendments. First, a memory element was added to this algorithm to ameliorate its position and velocity update mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive parametric functions were used to manage the exploration and exploitation features of this algorithm and balance them appropriately. Finally, the hybridization was made using the gradient-based optimizer to strengthen the intensification ability of this algorithm and balance its searching ability to fulfill sensible search performance. The proficiency of the proposed algorithm was divulged by assessing it on computationally difficult economic load dispatch problems under 6 different tests with a generator of 3, 13, 40, 80, and 140 units, each with different constraints and load conditions. The proposed algorithm provided the best performance among many other competitors. Its superiority and practicality were revealed by obtaining optimal solutions for large-scale test cases such as 40-unit and 140-unit test systems. [ABSTRACT FROM AUTHOR]

Intrusion Detection (ID) is an essential task in the cyberattacks domain built to secure Internet applications and networks from malicious actors. The main shortcoming of the current Intrusion Detection Systems (IDSs) is their attack detection performance because the existing solutions fail to detect the threats efficiently. Therefore, improving the prediction model of the IDS can improve its performance. Several studies suggest improving the Machine Learning (ML)-based IDS prediction models by selecting the most informative features from the security data before utilizing it in the ML-IDS model. Thus, meta-heuristic algorithms such as White Shark Optimizer (WSO) are adapted to deal with such feature selection problems. For the IDS prediction model, in this paper, the WSO algorithm is improved to cope with the binary domain of the feature selection task as follows: Firstly, two transfer functions are used to map the continuous domain into binary. Secondly, the modified K-means algorithm is proposed to assess building the initial population with a high level of diversity. Finally, several crossover operators are utilized to improve the evolution process of the binary WSO. The three improved versions are BIWSO1 which is WSO with transfer functions, BIWSO2, which is BIWSO1 with a modified k-means algorithm, and BIWSO3, which is BIWSO2 with crossover operators. The proposed versions of BIWSO are tested using twelve public real-world IDS and IoT datasets. Comparative evaluations against well-established meta-heuristic algorithms are conducted where the BIWSO3 proves its efficiency in terms of classification accuracy, precision, recall, and F1 measurements. For further validation, statistical evidence using Friedman's, Wilcoxon's, and Mann–Whitney U tests has been conducted where BIWSO3 proves its performance at a remarkably significant level. [ABSTRACT FROM AUTHOR]

An Enhanced Chameleon Swarm Algorithm (ECSA) by integrating roulette wheel selection and Lé vy flight methods is presented to solve non-convex Economic Load Dispatch (ELD) problems. CSA has diverse strategies to move towards the optimal solution. Even so, this algorithm's performance faces some hurdles, such as early convergence and slumping into local optimum. In this paper, several enhancements were made to this algorithm. First, it's position updating process was slightly tweaked and took advantage of the chameleons' randomization as well as adopting several time-varying functions. Second, the Lévy flight operator is integrated with roulette wheel selection method and both are combined with ECSA to augment the exploration behavior and lessen its bias towards exploitation. Finally, an add-on position updating strategy is proposed to develop a further balance between exploration and exploitation conducts. The optimization performance of ECSA is shown by testing it on five various real ELD cases with a generator having 3, 13, 40, 80 and 140 units, each with different constraints. The results of the ELD systems' analysis depict that ECSA is better than the parent CSA and other state-of-the art methods. Further, the efficacy of ECSA was experimented on several benchmark test functions, and its performance was compared to other well-known optimization methods. Experimental results show that ECSA surpasses other methods on complex benchmark functions with modest computational burdens. The superiority and practicality of ECSA is demonstrated by getting new best solutions for large-scale ELD cases such as 40-unit and 140-unit test systems. [ABSTRACT FROM AUTHOR]

Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions. [ABSTRACT FROM AUTHOR]

Email spam has been a big issue in recent years. As the percentage of internet users grows, so does the number of spam emails. Technologies are being used for illegitimate and immoral activities, such as phishing and robbery. As a consequence, it is essential to identify fraudulent spammers by employing machine learning techniques. This paper presents a novel spam classification technique that integrates the Harris Hawks optimizer (HHO) algorithm with the k-Nearest Neighbors algorithm (k-NN). The Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm motivated by Harris' Hawks' cooperative relations and surprises pounce pursue technique in nature. According to empirical results on the dataset, the suggested model can handle high-dimensional data (Spambase). The suggested model's spam detection accuracy is compared to the numerous algorithms, including the Binary Dragonfly Algorithm (BDA), Equilibrium Optimizer (EO), Teaching-Learning-based Optimization, Seagull Optimization Algorithm (SO), and Marine Predators Algorithm (MPA). We found that the proposed technique trumps the other spam detection techniques investigated in this study in terms of classification accuracy. The experimental results showed that the proposed technique accuracy reaches %94.3. [ABSTRACT FROM AUTHOR]

In this paper, software effort prediction (SEP) and software test prediction (STP) (i.e., software reliability problems) are tackled by integrating the salp swarm algorithm (SSA) with a backpropagation neural network (BPNN). Software effort and test prediction problems are common in software engineering and arise when seeking to determine the actual software resources needed to develop a project. BPNN is the most popular prediction algorithm used in the literature. The performance of BPNN depends totally on the initial parameter values such as weight and biases. The main objective of this paper is to integrate SSA with the BPNN to find the optimal weight for every training cycle and thereby improve prediction accuracy. The proposed method, abbreviated as SSA-BPNN, is tested on twelve SEP datasets and two STP datasets. All datasets vary in terms of complexity and size. The results obtained by SSA-BPNN are evaluated according to twelve performance measures: MSE, RMSE, RAE, RRSE, MAE, MRE, MMRE, MdMRE, VAF(%), R2(%), ED, and MD. First, the results obtained by BPNN with SSA (i.e., SSA-BPNN) and without SSA are compared. The evaluation of the results indicates that SSA-BPNN performs better than BPNN for all datasets. In the comparative evaluation, the results of SSA-BPNN are compared against thirteen state-of-the-art methods using the same SEP and STP problem datasets. The evaluation of the results reveals that the proposed method outperforms the comparative methods for almost all datasets, both SEP and STP, in the case of most performance measures. In conclusion, integrating SSA with BPNN is a very powerful approach for solving software reliability problems that can be used widely to yield accurate prediction results. [ABSTRACT FROM AUTHOR]

In this paper, the economic load dispatch (ELD) problem with valve point effect is tackled using a hybridization between salp swarm algorithm (SSA) as a population-based algorithm and β -hill climbing optimizer as a single point-based algorithm. The proposed hybrid SSA is abbreviated as HSSA. This is to achieve the right balance between the intensification and diversification of the ELD search space. ELD is an important problem in the power systems which is concerned with scheduling the generation units in active generators in optimal way to minimize the fuel cost in accordance with equality and inequality constraints. The proposed HSSA is evaluated using six real-world ELD systems: 3-unit generator, two cases of 13-unit generator, 40-unit generator, 80-unit generator, and 140-unit generator system. These ELD systems are well circulated in the previous literature. The comparative results against 66 well-regarded algorithms are conducted. The results show that the proposed HSSA is able to produce viable and competitive solutions for ELD problems. [ABSTRACT FROM AUTHOR]

In this paper, a new metaheuristic algorithm called JAYA algorithm has been adapted for feature selection. Feature selection is a typical problem in machine learning and data mining domain concerned with determining the subset of high discriminative features from the irrelevant, noisy, redundant, and high-dimensional features. JAYA algorithm is initially proposed for continuous optimization. Due to the binary nature of the feature selection problem, the JAYA algorithm is adjusted using sinusoidal (i.e., S-shape) transfer function. Furthermore, the mutation operator controlled by adaptive mutation rate ( R m ) parameter is also utilized to control the diversity during the search. The proposed binary JAYA algorithm with adaptive mutation is called BJAM algorithm. The performance of BJAM algorithm is tested using 22 real-world benchmark datasets, which vary in terms of the number of features and the number of instances. Four measures are used for performance analysis: classification accuracy, number of features, fitness values, and computational times. Initially, a comparison between binary JAYA (BJA) algorithm and the proposed BJAM algorithm is conducted to show the effect of the mutation operator in the convergence behavior. After that, the results produced by the BJAM algorithm are compared against those yielded by ten state-of-the-art methods. Surprisingly, the proposed BJAM algorithm is able to excel other comparative methods in 7 out of 22 datasets in terms of classification accuracy. This can lead to the conclusion that the proposed BJAM algorithm is an efficient algorithm for the problems belonging to the feature selection domain and is pregnant with fruitful results. [ABSTRACT FROM AUTHOR]

Text clustering has been widely utilized with the aim of partitioning specific document collection into different subsets using homogeneity/heterogeneity criteria. It has also become a very complicated area of research, including pattern recognition, information retrieval, and text mining. Metaheuristics are typically used as efficient approaches for the text clustering problem. The multi-verse optimizer algorithm (MVO) involves a stochastic population-based algorithm. It has been recently proposed and successfully utilized to tackle many hard optimization problems. However, a recently applied research trend involves hybridizing two or more algorithms with the aim of obtaining a superior solution regarding the problems of optimization. In this paper, a new hybrid of MVO algorithm with the K-means clustering algorithm is proposed, i.e., the H-MVO algorithm with the aims of enhancing the quality of initial candidate solutions, as well as the best solution, which is produced by MVO at each iteration. This hybrid algorithm aims at improving the global (diversification) ability of the search and finding a better cluster partition. The proposed H-MVO effectiveness was tested on five standard datasets, which are used in the domain of data clustering, as well as six standard text datasets, which are utilized in the domain of text document clustering, in addition to two scientific articles' datasets. The experiments showed that K-means hybridized MVO improves the results in terms of high convergence rate, accuracy, error rate, purity, entropy, recall, precision, and F-measure criteria. In general, H-MVO has outperformed or at least proven to be highly competitive compared to the original MVO algorithm and with well-known optimization algorithms like KHA, HS, PSO, GA, H-PSO, and H-GA and the clustering techniques like K-mean, K-mean++, DBSCAN, agglomerative, and spectral clustering techniques. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient version of artificial bee colony (ABC) algorithm based on the island model concepts. The new version is called the island artificial bee colony (iABC) algorithm. It uses the structured population concept by applying the island model to improve the diversification capabilities of ABC. In the island model, the population is divided into a set of sub-populations called islands, each of which is manipulated separately by an independent variant of the ABC. After a predefined number of iterations, the islands exchange their solutions by migration. This process can help ABC in controlling the diversity of the population during the search process and thus improve the performance. The proposed iABC is evaluated using global optimization functions established by the IEEE-CEC 2015 which include 15 test functions with various dimensions and complexities (i.e., 10, 30, and 50). In order to evaluate the performance of iABC, various parameter settings are utilized to test the effectiveness of their convergence properties. Furthermore, the performance of iABC is compared against 19 comparative methods that used the same IEEE-CEC 2015 test functions. The results show that iABC produced better results when compared with ABC in all IEEE-CEC 2015 test functions, while the results of iABC better than those of the other island-based algorithm on almost all test functions. Furthermore, iABC is able to obtain three new results for three test functions better than all the comparative methods. Using Friedman test and Holm's procedure, iABC is ranked third, seventh, and ninth out of 19 comparative methods for the test functions with 10, 30, 50 dimensionality, respectively. [ABSTRACT FROM AUTHOR]

Economic load dispatch (ELD) is a crucial problem in the power system which is tackled by distributing the required generation power through a set of units to minimize the fuel cost required. This distribution is subject to two main constraints: (1) equality and inequality related to power balance and power output, respectively. In the optimization context, ELD is formulated as a non-convex, nonlinear, constrained optimization problem which cannot be easily solved using calculus-based techniques. Several optimization algorithms have been adapted. Due to the complexity nature of ELD search space, the theoretical concepts of these optimization algorithms have been modified or hybridized. In this paper, the grey wolf optimizer (GWO) which is a swarm intelligence is hybridized with β -hill climbing optimizer (β HC) which is a local search algorithm, to improve convergence properties. GWO is very powerful in a wide search, while β HC is very powerful in deep search. By combining the wide and deep search ability in a single optimization framework, the balance between the exploration and exploitation is correctly managed. The proposed hybrid algorithm is named β -GWO which is evaluated using five different test cases of ELD problems: 3 generating units with 850 MW; 13 generating units with 1800 MW; 13 generating units with 2520 MW; 40 generating units with 10,500 MW; and 80 generating units with 21,000 MW. β -GWO is comparatively measured using 49 comparative methods. The results obtained by β -GWO outperform others in most test cases. In conclusion, the proposed β -GWO is proved to be a powerful method for ELD problem or for any other similar problems in the power system domain. [ABSTRACT FROM AUTHOR]

The multi-reservoir systems optimization problem requires defining a set of rules to recognize the water amount stored and released in accordance with the system constraints. Traditional methods are not suitable for complex multi-reservoir systems with high dimensionality. Recently, metaheuristic-based algorithms such as evolutionary algorithms and local search-based algorithms are successfully used to solve the multi-reservoir systems. β-hill climbing is a recent metaheuristic local search-based algorithm. In this paper, the multi-reservoir systems optimization problem is tackled using β-hill climbing. In order to validate the proposed method, four-reservoir systems used in the literature to evaluate the algorithm are utilized. A comparative evaluation is conducted to evaluate the proposed method against other methods found in the literature. The obtained results show the competitiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the update process of harmony search (HS) algorithm is modified to improve its concept of diversity. The update process in HS is based on a greedy mechanism in which the new harmony solution, created in each generation, replaces the worst individual in the population, if better. This greedy process could be improved with other updates mechanisms in order to control the diversity perfectly. Three versions of HS have been proposed: (1) Natural Proportional HS ; (2) Natural Tournament HS; (3) Natural Rank HS. These three HS versions employed the natural selection principle of the “survival of the fittest”. Instead of replacing the worst individual in population, any individual can be replaced based on certain criteria. Four versions of economic loading dispatch (ELD) problems with valve point have been used to measure the effect of the newly proposed HS versions. The results show that the new HS versions are very promising for ELD domain. This claim is proved based on the comparative evaluation process where the new HS versions are able to excel the state-of-the-art methods in almost ELD problems used. [ABSTRACT FROM AUTHOR]

In this paper, alternative selection mechanisms in the bat-inspired algorithm for global optimization problems are studied. The bat-inspired algorithm is a recent swarm-based intelligent system which mimics the echolocation system of micro-bats. In the bat-inspired algorithm, the bats randomly fly around the best bat locations found during the search so as to improve their hunting of prey. In practice, one bat location from a set of best bat locations is selected. Thereafter, that best bat location is used by local search with a random walk strategy to inform other bats about the prey location. This selection mechanism can be improved using other natural selection mechanisms adopted from other advanced algorithms like Genetic Algorithm. Therefore, six selection mechanisms are studied to choose the best bat location: global-best, tournament, proportional, linear rank, exponential rank, and random. Consequently, six versions of bat-inspired algorithm are proposed and studied which are global-best bat-inspired algorithm (GBA), tournament bat-inspired algorithm (TBA), proportional bat-inspired algorithm (PBA), linear rank bat-inspired algorithm (LBA), exponential rank bat-inspired algorithm (EBA), and random bat-inspired algorithm (RBA). Using two sets of global optimization functions, the bat-inspired versions are evaluated and the sensitivity analyses of each version to its parameters studied. Our results suggest that there are positive effects of the selection mechanisms on the performance of the classical bat-inspired algorithm which is GBA. For comparative evaluation, eighteen methods are selected using 25 IEEE-CEC2005 functions. The results show that the bat-inspired versions with various selection schemes observing the “survival-of-the-fittest” principle are largely competitive to established methods. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is generally associated with the process of using a probabilistic method to select optimal feature combinations during pre-processing steps in data mining. This technique can optimize the datasets' features that need to be considered to heighten the performance of classification on the grounds of the selected optimal feature set. In this paper, a hybridization model is evolved and applied to select the optimal feature subset based on a binary version of the Hybrid Memory Improved Chameleon Swarm Algorithm (CSA) (HMICSA) and the k-Nearest Neighbor (k-NN) classifier. In this FS model, the following are proposed and applied: (1) Four kinds of transfer functions, (2) Amendments to the velocity of the CSA's individuals, (3) Addition of internal memory to the CSA's individuals, and (4) Hybridization of CSA with Ali baba and the Forty Thieves (AFT) algorithm. These actions are aimed to strike an adequate equipoise between global exploration and local exploitation conducts of the search space of the basic CSA. This is to mitigate the problem of early convergence, and to sidestep trapping into a local optima in CSA. The efficacy of the proposed FS algorithm was evaluated on 24 medical diagnosis benchmark datasets collected from different specialized repositories and compared with other k-NN-based FS methods. The all-inclusive outcomes using various evaluation methods disclose the competence of the proposed method in augmenting the classification performance compared to other methods, ensuring its potential in scouting the feature space and designating the most useful features for classification tasks. • A hybrid chameleon swarm algorithm is proposed for feature selection problems. • The proposed algorithm was assessed on 24 well-known benchmark datasets. • The results proved exploration and exploitation abilities of the proposed algorithm. • The convergence and statistical results proved the proposed algorithm's performance. [ABSTRACT FROM AUTHOR]

Krill Herd (KH) algorithm is a class of nature-inspired algorithm, which simulates the herding behavior of krill individuals. It has been successfully utilized to tackle many optimization problems in different domains and found to be very efficient. As a result, the studies has expanded significantly in the last 3 years. This paper presents the extensive (not exhaustive) review of KH algorithm in the area of applications, modifications, and hybridizations across these fields. The description of how KH algorithm was used in the approaches for solving these kinds of problems and further research directions are also discussed. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an analysis of some selection methods used in memory consideration of Harmony search (HS) Algorithm. The selection process in memory consideration entails selecting the value of the decision variable from any solution in the Harmony memory (HM). Quite recently, there has been a tendency to adopt novel selection methods that mimic the natural phenomena of the ‘survival of the fittest’ to replace the random selection method in memory consideration. Consequently, the value of decision variable selected using memory consideration is chosen from the higher promising solutions in HM. The adopted selection methods include: proportional, tournament, linear rank, and exponential rank. It has been demonstrated that experimenting with any of these methods in memory consideration directly affects the performance of HS. However, the success of these methods is based on choosing the optimal parameter value of each. The wrong parameter settings might affect the balance between exploration and exploitation of the search space. Accordingly, this paper studies the effect of the selection method parameters in order to show their effect on HS behavior. The evaluation is conducted using standard mathematical functions used in the literature for HS adoptions. The results suggest that the optimal setting of the selection method parameters is crucial to improve the HS performance. [Copyright &y& Elsevier]

Abstract: In this paper, the Harmony Search Algorithm (HSA) is proposed to tackle the Nurse Rostering Problem (NRP) using a dataset introduced in the First International Nurse Rostering Competition (INRC2010). NRP is a combinatorial optimization problem that is tackled by assigning a set of nurses with different skills and contracts to different types of shifts, over a predefined scheduling period. HSA is an approximation method which mimics the improvisation process that has been successfully applied for a wide range of optimization problems. It improvises the new harmony iteratively using three operators: memory consideration, random consideration, and pitch adjustment. Recently, HSA has been used for NRP, with promising results. This paper has made two major improvements to HSA for NRP: (i) replacing random selection with the Global-best selection of Particle Swarm Optimization in memory consideration operator to improve convergence speed. (ii) Establishing multi-pitch adjustment procedures to improve local exploitation. The result obtained by HSA is comparable with those produced by the five INRC2010 winners’ methods. [Copyright &y& Elsevier]

Abstract: Selection is a vital component used in Evolutionary Algorithms (EA) where the fitness value of the solution has influence on the evolution process. Normally, any efficient selection method makes use of the Darwinian principle of natural selection (i.e., survival of the fittest). Harmony search (HS) is a recent EA inspired by musical improvisation process to seek a pleasing harmony. Originally, two selection methods are used in HS: (i) memory consideration selection method where the values of the decision variables are randomly selected from the population (or solutions stored in harmony memory (HM)) to generate a new harmony, and (ii) selecting a new solution in HM whereby a greedy selection is used to update the HM. The memory consideration selection, the focal point of this paper, is not based on natural selection principle which draws heavily on random selection. In this paper, novel selection schemes which replace the random selection scheme in memory consideration are investigated, comprising global-best, fitness-proportional, tournament, linear rank and exponential rank. The proposed selection schemes are individually altered and incorporated in the process of memory consideration and each adoption is realized as a new HS variation. The performance of the proposed HS variations are evaluated and a comparative study is conducted. The experimental results using benchmark functions show that the selection schemes incorporated in memory consideration directly affect the performance of HS algorithm. Finally, a parameter sensitivity analysis of the proposed HS variations is analyzed. [Copyright &y& Elsevier]

Face recognition under unconstrained environments has become increasingly important due to the broad prospect in real-world applications. In order to counter uncertainties imposed by faces captured in such unconstrained imaging situations, a robust, discriminative and computationally efficient feature selection scheme is of paramount significance. In this regard, bio-inspired feature selection methods have been exploited due to their sophisticated ability, flexibility and adaptability. However, their performances tend to deteriorate severely in large-scale domains such as face recognition due to the premature convergence problem. In this paper, high-dimensional LBP features are extracted from face images and fused with Gabor wavelet features using Canonical Correlation Analysis (CCA). To further enhance the discrimination power of the facial representation and to alleviate the curse of dimensionality, a novel membrane-inspired feature selection approach is proposed, where a Binary Bat Algorithm (BBA) under the framework of Membrane Computing (MC) is employed. Inherent parallelism and non-determinism are two distinguishing characteristics of MC that can help in maintaining the diversity of population and balancing the exploration–exploitation trade-off. In the proposed membrane-inspired BBA (MIBBA), the structure as well as the evolution, dissolution and communication rules of MC are integrated into the BBA to enhance the trajectories of bats. Furthermore, the Great Deluge Algorithm (GDA), is integrated into the skin membrane to further improve its exploitation ability. Experimental results show that the proposed approach yields competitive recognition rates and outperforms well-known state-of-the-art methods on three benchmark databases (AR, LFW and GBU). Further experimental evaluations justify the ability of the proposed approach to handle the small sample size problem. [ABSTRACT FROM AUTHOR]

Dragonfly Algorithm (DA) is a recent swarm-based optimization method that imitates the hunting and migration mechanisms of idealized dragonflies. Recently, a binary DA (BDA) has been proposed. During the algorithm iterative process, the BDA updates its five main coefficients using random values. This updating mechanism can be improved to utilize the survival-of-the-fittest principle by adopting different functions such as linear, quadratic, and sinusoidal. In this paper, a novel BDA is proposed. The algorithm uses different strategies to update the values of its five main coefficients to tackle Feature Selection (FS) problems. Three versions of BDA have been proposed and compared against the original DA. The proposed algorithms are Linear-BDA, Quadratic-BDA, and Sinusoidal-BDA. The algorithms are evaluated using 18 well-known datasets. Thereafter, they are compared in terms of classification accuracy, the number of selected features, and fitness value. The results show that Sinusoidal-BDA outperforms other proposed methods in almost all datasets. Furthermore, Sinusoidal-BDA exceeds three swarm-based methods in all the datasets in terms of classification accuracy and it excels in most datasets when compared in terms of the fitness function value. In a nutshell, the proposed Sinusoidal-BDA outperforms the comparable feature selection algorithms and the proposed updating mechanism has a high impact on the algorithm performance when tackling FS problems. • This paper proposes an improved Binary Dragonfly Algorithm (BDA) for Feature Selection. • Three versions of BDA are proposed: LBDA, QBDA, and SBDA. • Eighteen datasets taken from UCI machine learning repository are used for evaluation process. • SBDA excels LBDA and QBDA in terms of accuracy, number of features, and fitness function. • SBDA excels the nine comparative methods in 12 out of 18 dataset in terms of accuracy. [ABSTRACT FROM AUTHOR]

• Hybridizing Flower Pollination with hill climbing (FPAc) for EEG-based person identification. • FPAc is used to select the optimal EEG channels which can provides high accuracy. • FPAc tested using standard EEG dataset, namely, EEG motor movement/imagery dataset. • The results of FPAc are compared with standard FPA and with other state-of-arts. • Effect of FPAc on the performance of EEG channels selection is studied and shows significant improvements. Recently, electroencephalogram (EEG) signal presents a great potential for a new biometric system to deal with a cognitive task. Several studies defined the EEG with uniqueness features, universality, and natural robustness that can be used as a new track to prevent spoofing attacks. The EEG signals are the graphical recording of the brain electrical activities which can be measured by placing electrodes (channels) in various positions of the scalp. With a large number of channels, some channels have very important information for biometric system while others not. The channel selection problem has been recently formulated as an optimisation problem and solved by optimisation techniques. This paper proposes hybrid optimisation techniques based on binary flower pollination algorithm (FPA) and β -hill climbing (called FPA β -hc) for selecting the most relative EEG channels (i.e., features) that come up with efficient accuracy rate of personal identification. Each EEG signals with three different groups of EEG channels have been utilized (i.e., time domain, frequency domain, and time-frequency domain). The FPA β -hc is measured using a standard EEG signal dataset, namely, EEG motor movement/imagery dataset with a real world data taken from 109 persons each with 14 different cognitive tasks using 64 channels. To evaluate the performance of the FPA β -hc, five measurement criteria are considered:accuracy (Acc), (ii) sensitivity (Sen), (iii) F-score (F_s), (v) specificity (Spe), and (iv) number of channels selected (No. Ch). The proposed method is able to identify the personals with high Acc, Sen., F_s, Spe, and less number of channels selected. Interestingly, the experimental results suggest that FPA β -hc is able to reduce the number of channels with accuracy rate up to 96% using time-frequency domain features. For comparative evaluation, the proposed method is able to achieve results better than those produced by binary-FPA-OPF method using the same EEG motor movement/imagery datasets. In a nutshell, the proposed method can be very beneficial for effective use of EEG signals in biometric applications. [ABSTRACT FROM AUTHOR]

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm. [ABSTRACT FROM AUTHOR]