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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]

This paper proposes binary versions of artificial rabbits optimization (ARO) for feature selection (FS) with medical diagnosis data. ARO is a recent swarm-based optimization algorithm that mimics rabbits' natural survival tactics and eating habits. It was modeled in an optimization context to tackle optimization problems of continuous search spaces. In this paper, ARO is improved to deal with the binary domain of FS. The improvements include three additions: First, different alternatives of transfer functions were used to convert ARO from continuous to binary; second, the global-best concept was added to the binary ARO to improve the exploitation capability of the proposed algorithm; and finally, Lévy flight and opposition-based learning strategies were injected into the proposed algorithm to enhance its diversity and thus improve the balance between global exploration and local exploitation during all stages of the search process. Six binary variants of ARO were designed across an extensive set of experiments to study the impact of using the proposed amendments on the performance of the proposed ARO algorithm. These variants are: binary ARO with S-shaped transfer function (BAROS), binary ARO with V-shaped transfer function (BAROV), BAROS with the global-best concept (BGAROS), BGAROV with the global-best concept (BGAROV), BGAROS with Lévy flight and opposition-based learning strategies (BGAROSLO), and BGAROV with Lévy flight and opposition-based learning strategies (BGAROVLO). The proposed binary ARO versions were evaluated using 23 medical FS datasets. In addition, the proposed algorithm was applied to detect coronavirus disease using a real COVID-19 dataset. Five performance measures were used: classification accuracy, sensitivity, specificity, fitness value, and the number of selected features. In a nutshell, the proposed binary ARO versions were able to achieve success rates for these performance metrics as follows: 66.7%, 50%, 33.3%, 66.7%, and 83.3%, respectively. In conclusion, the success of the proposed ARO versions was realized due to the suitable design of the parameters of the proposed ARO version, such as transfer functions, global-best concept, Lévy flight, and opposition-based learning strategies. A comprehensive comparative evaluation was studied against ten well-established methods using the same datasets with a high preference for the proposed ARO versions, especially BGAROSLO which can achieve the best accuracy for the majority of the FS datasets. This is proven using Friedman's statistical test ad-hocked by Holm's test. [ABSTRACT FROM AUTHOR]

Parameter identification for complex systems of nonlinear nature is challenging due to the complicated process structure and large number of parameters with different identifiability. The scope of this work is to develop a model-based parameter identification method for a nonlinear industrial winding system. The proposed parameter identification method consists of two key steps: First, enhanced whale optimization algorithm (EWOA) was proposed to alleviate the issues of low search performance and premature convergence of WOA, for which the following enhancements were made to WOA: (1) improvements to the bubble-net strategy of its mathematical model, (2) amendments to the humpback whales' movements in the direction of the best whales, and (3) imitation of the schooling behavior of humpback whales when chasing prey. Second, EWOA was acted as a training method for artificial neural networks (ANNs)-type multilayer perceptron (MLP), a method referred to as EWOA-MLP. In this, EWOA was applied to train ANNs in order to mitigate the main difficulties of ANNs due to their nonlinear nature and unknown optimal set of control parameters (i.e., weights and biases). The performance of the proposed EWOA-MLP was assessed in modeling the subsystems of the winding process under study and in solving fifteen classification datasets. The effectiveness of EWOA-MLP in both modeling and classification studies was judged by several pertinent assessment metrics. Results of comparison of the proposed EWOA-MLP with other promising methods firmly confirm the promising performance of EWOA-MLP for both local optima avoidance and convergence rate, proving its value and superiority. Moreover, EWOA-MLP was able to outperform other algorithms in modeling the winding process and solving many classification problems. [ABSTRACT FROM AUTHOR]

The success of the supervised learning process for feedforward neural networks, especially multilayer perceptron neural network (MLP), depends on the suitable configuration of its controlling parameters (i.e., weights and biases). Normally, the gradient descent method is used to find the optimal values of weights and biases. The gradient descent method suffers from the local optimal trap and slow convergence. Therefore, stochastic approximation methods such as metaheuristics are invited. Coronavirus herd immunity optimizer (CHIO) is a recent metaheuristic human-based algorithm stemmed from the herd immunity mechanism as a way to treat the spread of the coronavirus pandemic. In this paper, an external archive strategy is proposed and applied to direct the population closer to more promising search regions. The external archive is implemented during the algorithm evolution, and it saves the best solutions to be used later. This enhanced version of CHIO is called ACHIO. The algorithm is utilized in the training process of MLP to find its optimal controlling parameters thus empowering their classification accuracy. The proposed approach is evaluated using 15 classification datasets with classes ranging between 2 to 10. The performance of ACHIO is compared against six well-known swarm intelligence algorithms and the original CHIO in terms of classification accuracy. Interestingly, ACHIO is able to produce accurate results that excel other comparative methods in ten out of the fifteen classification datasets and very competitive results for others. [ABSTRACT FROM AUTHOR]

In this paper, the learning process of multilayer perceptron (MLP) neural network is boosted using hybrid metaheuristic optimization algorithms. Normally, the learning process in MLP requires suitable settings of its weight and bias parameters. In the original version of MLP, the gradient descent algorithm is used as a learner in MLP which suffers from two chronic problems: local minima and slow convergence. In this paper, six versions of memetic algorithms (MAs) are proposed to replace gradient descent learning mechanism of MLP where adaptive β -hill climbing (A β HC) as a local search algorithm is hybridized with six population-based metaheuristics which are hybrid flower pollination algorithm, hybrid salp swarm algorithm, hybrid crow search algorithm, hybrid grey wolf optimization (HGWO), hybrid particle swarm optimization, and hybrid JAYA algorithm. This is to show the effect of the proposed MA versions on the performance of MLP. To evaluate the proposed MA versions for MLP, 15 classification benchmark problems with different size and complexity are used. The A β HC algorithm is invoked in the improvement loop of any MA version with a probability of B r parameter, which is investigated to monitor its effect on the behavior of the proposed MA versions. The B r setting which obtains the most promising results is then used to set the hybrid MA. The results show that the proposed MA versions excel the original algorithms. Moreover, HGWO outperforms all other MA versions in almost all the datasets. In a nutshell, MAs are a good choice for training MLP to produce results with high accuracy. [ABSTRACT FROM AUTHOR]

High-dimensional Feature Selection Problems (HFSPs) have grown in popularity but remain challenging. When faced with such complex situations, the majority of currently employed Feature Selection (FS) methods for these problems drastically underperform in terms of effectiveness. To address HFSPs, a new Binary variant of the Ali Baba and the Forty Thieves (BAFT) algorithm known as binary adaptive elite opposition-based AFT (BAEOAFT), incorporating historical information and dimensional mutation is presented. The entire population is dynamically separated into two subpopulations in order to maintain population variety, and information and knowledge about individuals are extracted to offer adaptive and dynamic strategies in both subpopulations. Based on the individuals’ history knowledge, Adaptive Tracking Distance (ATD) and Adaptive Perceptive Possibility (APP) schemes are presented for the exploration and exploitation subpopulations. A dynamic dimension mutation technique is used in the exploration subpopulation to enhance BAEOAFT’s capacity in solving HFSPs. Meanwhile, the exploratory subpopulation uses Dlite Dynamic opposite Learning (EDL) to promote individual variety. Even if the exploitation group prematurely converges, the exploration subpopulation’s variety can still be preserved. The proposed BAEOAFT-based FS technique was assessed by utilizing the k-nearest neighbor classifier on 20 HFSPs obtained from the UCI repository. The developed BAEOAFT achieved classification accuracy rates greater than those of its competitors and the conventional BAFT in more than 90% of the applied datasets. Additionally, BAEOAFT outperformed its rivals in terms of reduction rates while selecting the fewest number of features. [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]

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]

Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms simulate the foraging behavior of honey bees in their hive. ABC starts with a colony of artificial bees with sole aim of discovering the place of food sources with high nectar amount using the solution search equation in the employed bee and onlooker bee operators. However, the solution search equation is good in exploration and poor in exploitation. In this paper, the solution search equation of the onlooker bee is modified by using a value of the fittest food sources selected by a set of selection schemes inspired from the evolutionary algorithms. This is to guide the search process of onlooker bee toward the fittest food sources from the population in order to empower the exploitation capability and convergence. Four selection schemes are incorporated with the ABC algorithm to choose the fittest food sources in four versions as follows: global-best, tournament, linear rank, and exponential rank. For evaluation purposes, 10 classical benchmark optimization functions are used to study the sensitivity analysis of each ABC algorithm to its parameters. The performance of the proposed ABC versions is compared with the original ABC version in order to study the effectiveness of the modifications. In addition, a comparative evaluation of ABC algorithms is carried out against the state-of-the-art methods that worked on CEC2005 benchmark functions, CEC2015 benchmark functions, and two real-world cases of economic load dispatch problem. The experimental results show that the selection schemes incorporated within the search equation of the onlooker bee directly affects the performance of ABC algorithm. [ABSTRACT FROM AUTHOR]

Flower pollination algorithm (FPA) is a recent swarm-based evolutionary algorithm that was inspired by the biological evolution of pollination of the flowers. It deals with a panmictic population of pollens (or solutions) at each generation, using global and local pollination operators, to improve the whole population at once. Like other evolutionary algorithms, FPA has a chronic shortcoming that lies in its inability to maturely converge. This is conventionally known as a premature convergence where the diversity of the population is loosed and thus the search is stagnated. Island model is one of the successful structured population techniques that were utilized in the theoretical characteristics of several evolutionary-based algorithms. In this model, the population is divided into a set of islands. The knowledge is distributed among those islands using a migration process that is controlled by migration rate, topology, frequency, and policy. In this paper, the island model is utilized in the evolution process of FPA to control diversity. The proposed approach is called IsFPA. The ability of IsFPA in maintaining the diversity during the search process, and in producing impressive results, can be interpreted by utilizing the island model in the FPA optimization framework. To assess the efficiency of IsFPA, 23 benchmark functions with various sizes and complexities were used. The best parameter configurations of IsFPA were investigated and analyzed. Comparing the results of IsFPA with those of state-of-the-art methods which are FPA, genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), multi-verse optimizer (MVO), island bat algorithm (iBA), and island harmony search (iHS), the comparison results show that the IsFPA is able to control the diversity and improves the outcomes where IsFPA is ranked first followed by FPA, iBA, iHS, GSA, MVO, GA, PSO, respectively, based on the Friedman test with Holm and Hochberg as post hoc statistical test. [ABSTRACT FROM AUTHOR]

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]

The selection process is the most attractive operator in the optimization algorithms. It normally mimics the natural selection of survival of the fittest principle. When the selection is too greedy, the selection pressure will be high and therefore the search becomes biased toward exploitation. In contrast, when the selection has a tendency to be random, the selection pressure will be low and thus the exploration is observed more. The selection process in Grey Wolf Optimizer (GWO) tends to be too greedy since the search is driven by the three best solutions. In this paper, different selection methods extracted from other evolutionary algorithms (EAs) are investigated for GWO. Along with the original selection method of GWO called greedy-based selection method, five others selection methods which are proportional, tournament, universal sampling, linear rank, and random selection methods are investigated. Accordingly, six versions of GWO are proposed which are Greedy-based GWO (GGWO), Proportional-based GWO (PGWO), Tournament-based GWO (TGWO), Universal sampling-based GWO (UGWO), Linear rank-based GWO (LGWO), Random-based GWO (RGWO). The six versions are evaluated using 23 test functions circulated in the literature with different characteristics and complexity. The sensitivity analysis of the control parameters of some new versions are discussed. Interestingly, TGWO achieved the best results for almost all benchmark functions. This proves that the selection methods have a high impact on the performance of GWO. [ABSTRACT FROM AUTHOR]

In this paper, the problem of economic load dispatch (ELD) is tackled using a recently introduced local search-based method called β-hill climbing optimizer. In a power system, the ELD problem is tackled by arranging a set of generation units’ outputs in a specific order to minimize the cost of the operating fuel and to match the power system load demand. This goal is achieved by satisfying all the power balance equality constraints and power output inequality constraints. β-hill climbing algorithm is a new local search algorithm which uses an intelligent stochastic operator called β-operator to escape the trap of local optima. The proposed method is evaluated using five real-world ELD benchmarks which vary in terms of complexity and size. The sensitivity analysis to study the effect of the proposed method parameters is conducted based on eight different convergence cases. The evaluation results of the proposed method are compared with 45 state-of-the-art methods using the same tested ELD benchmarks. Interestingly, the proposed method is able to produce a very closed-to-optimum result for almost all the tested ELD systems and the best result for one of them. [ABSTRACT FROM AUTHOR]

Structured population in evolutionary algorithms is a vital strategy to control diversity during the search. One of the most popular structured population strategies is the island model in which the population is divided into several sub-populations (islands). The EA normally search for each island independently. After a number of predefined iterations, a migration process is activated to exchange specific migrants between islands. Recently, bat-inspired algorithm has been proposed as a population-based algorithm to mimic the echolocation system involved in micro-bat. The main drawback of bat-inspired algorithm is its inability to preserve the diversity during the search and thus the prematurity can take place. In this paper, the strategy of island model is adapted for bat-inspired algorithm to empower its capability in controlling its diversity concepts. The proposed island bat-inspired algorithm is evaluated using 25 IEEE-CEC2005 benchmark functions with different size and complexity. The sensitivity analysis for the main parameters of island bat-inspired algorithm is well-studied to show their effect on the convergence properties. For comparative evaluation, island bat-inspired algorithm is compared with 17 competitive methods and shows very successful outcomes. Furthermore, the proposed algorithm is applied for three real-world cases of economic load dispatch problem where the results obtained prove considerable efficiency in comparison with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Classification of medical data is largely dependent on the effective identification of key features of the data that can be used to aid in the diagnosis of related diseases. This goal can be achieved through feature selection methods that endeavor to get rid of redundant and irrelevant features to ameliorate classification accuracy. This is the aim of this work where a new meta-heuristic, referred to as snake optimizer, was adopted for the purpose of boosting the performance of existing feature selection methods. This optimizer may smoothly fall into local optimal solutions, which may present weak search performance and slow convergence speeds in handling feature selection problems. On this basis, this paper presents three improved adaptive versions of this optimizer, each of which has increased search performance over the basic optimizer. This optimizer was improved using three mathematical models named exponential, power, and delayed S-shaped, to create three methods, referred to as exponential, power, and delayed S-shaped snake optimizers, respectively. These proposed versions were also matured to have more balance between exploration and exploitation aspects. Then, binary variants of these optimizers were evolved to solve feature selection problems using the k-nearest neighbor classifier. To verify the efficacy of these binary optimizers, 24 datasets were used, and then compared with other feature selection optimizers. The experimental results obviously manifested the efficiency of the proposed optimizers in realizing the optimal feature set by achieving utmost accuracy and minimal number of features in the majority of the studied datasets. The proposed binary power snake optimizer outperformed all other competitors in 13, 10, 8, 8, and 12 datasets in respect of classification accuracy, number of chosen attributes, specificity, sensitivity, and fitness scores, respectively. Out of the 24 datasets taken into consideration, the results on 12, 6, and 8 datasets, respectively, showed that this proposed optimizer presented performance scores of more than 90% in respect of sensitivity, accuracy, and specificity metrics. [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]

Objective: While telemedicine is in expanding use in acute stroke care, little is known about its use in general neurology, especially in acute care. We sought to investigate the feasibility and possible effects of a telemedicine device within the neurological back-up service of an acute care hospital.Methods: In a 450 bed academic teaching hospital an experienced neurologist (EN) is on call to support the junior doctor at the hospital. Support was possible whether by standard telephone advice (TA) or by audiovisual consultations (AVC). In AVC the expert used a mobile telemedicine device and so he could establish audiovisual contact from his home to the emergency room and examine newly admitted patients. Technical and patient details including timing and diagnosis were recorded. Video and audio quality as well as impact of AVC on diagnosis was rated by the EN.Results: Out of about 1200 cases in off peak times, during the study period, 164 AVC including remote video examination were done (13.6%). Also 48 cases were documented by pure TA. Video quality was rated to a medium of 1.7, audio quality to 2.1. In 36 cases the audiovisual consultation was influenced by technical issues leading to cessation of AVC in 8 cases. Duration of teleconsultation was 17.3 min in AVC compared to 8.7 min for TA. The consultation diagnosis in AVC was confirmed in 74.4% of all cases compared to 57.7% in TA. AVC was rated as a valuable contribution to the diagnostic workup in 74.3% of all cases seen. In about 40% of all cases AVC was not possible due to technical or organizational reasons.Conclusions: Audiovisual consultation seems to be a feasible and useful support in routine neurology back-up service of an acute care hospital. Better mobility of devices and flexibility of service is needed to improve availability and quality of this valuable tool. [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]

This paper proposes a hybrid harmony search algorithm (HHSA) for solving the highly constrained nurse rostering problem (NRP). The NRP is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses; each has specific skills and work contract, to a predefined rostering period according to a set of constraints. The harmony search is a metaheuristic approach, where the metaheuristics are the most successful methods for tackling this problem. In HHSA, the harmony search algorithm is hybridized with the hill climbing optimizer to empower its exploitation capability. Furthermore, the memory consideration operator of the HHSA is modified by replacing the random selection scheme with the global-best concept of particle swarm optimization to accelerate its convergence rate. The standard dataset published in the first international nurse rostering competition 2010 (INRC2010) was utilized to evaluate the proposed HHSA. Several convergence scenarios have been employed to study the effects of the two HHSA modifications. Finally, a comparative evaluation against twelve other methods that worked on the INRC2010 dataset is carried out. The experimental results show that the proposed method achieved five new best results, and 33 best published results out of 69 instances as achieved by other comparative methods. [ABSTRACT FROM AUTHOR]

In evolutionary computation, systematically structuring the population is used to manage the evolution process. Thus controlling the amount of diversity during the algorithm search. Island-based, hierarchical-based, and cellular automata are the most popular structured population models utilized for evolutionary algorithms to improve their diversity and convergence. Specifically, the cellular automata model arranges the population of individuals in a 2D grid spatial structure where the concepts of cells and their neighbors are considered to drive the evolution step. A recent population-based algorithm is proposed called the crow search algorithm (CSA). It stimulates the behavior of crows in keeping their food in hidden places to be retrieved when needed. Like other population-based algorithms, CSA endures from slow convergence because of inadequate diversity. In this paper, the cellular automata model is incorporated with the optimization framework of CSA to control its diversity during the search, thus boosts its efficiency. The proposed method is abbreviated as CCSA. In CCSA, the population is structured as a 2D grid where the active population is iteratively determined. At each iteration, each individual is updated based on current neighboring individuals determined by the topological neighborhood shapes and follows the best neighboring individual in its active population. The CCSA is evaluated using 23 standard benchmark functions well-circulated in the literature. Initially, six cellular topological neighborhood shapes (i.e., L5, L9, C9, C13, C21, and C25) are studied with various problem sizes to investigate their impact on the CCSA convergence. Comparative evaluations against twelve state-of-the-art methods, including four CSA versions, are conducted. The comparative results prove the effectiveness of the proposed CCSA. The diversity of the proposed CCSA has also been stressed using convergence analysis and hamming distance. For further validations, the proposed algorithm is evaluated using three real-world problems published in IEEE-CEC2011 and welded beam design problems. Again, the proposed CCSA yields more fruitful results than other well-established methods using real-world problems. In conclusion, this paper provides an efficient alternative of CSA to perfectly maintain the diversity of the search, which can be further tested using other optimization problems. • Cellular automata is injected with crow search algorithm (CCSA) to empower diversity. • CCSA with six neighborhood shapes (i.e., L5, L9, C9, C13, C21, and C25) are proposed. • CSA is evaluated using 23 test functions, and four Engineering problems. • Results show the superiority of the CCSA against the other comparative algorithms. [ABSTRACT FROM AUTHOR]

Harmony search (HS) algorithm is a recent meta-heuristic algorithm that mimics the musical improvisation concepts. This algorithm has been widely used for solving optimization problems. Moreover, many modifications in this algorithm have been carried out in order to improve the performance of the search. Island model is a structured population mechanism used in evolutionary algorithms to preserve the diversity of the population and thus improve the performance. In this paper, the island model concepts are embedded into the main framework of HS algorithm to improve its convergence properties where the new method is refer to as island HS ( i HS). In the proposed method, the individuals in population are distributed into separate sub-population named (islands). Then the breeding loop is separately involved in each island. After specific generations, a number of individuals run an exchange through a process called migration. This process is performed to keep the diversity of population and to allow islands to interact with each other. The experimental result using a set of benchmark function shows that the island model context is crucial to the performance of i HS. Finally the sensitivity analysis and the comparative study for i HS prove the efficiency of the island model. [ABSTRACT FROM AUTHOR]

This paper proposes new versions of Harris Hawks Optimizer (HHO) incorporated the survival-of-the-fittest principle of evolutionary algorithms. HHO is the recent swarm-based optimization algorithm imitating the surprise pounce behaviour of Harris' hawks chasing style. HHO can show different patterns of the exploration and exploitation. It has a simple and time-varying structure, which further assist a smooth transition between the core phases. It has two main phases to iterate toward the optimal solution: exploration and exploitation. In the exploration phase, the current solution is either randomly modified based on any solution selected randomly or rebuilt from scratch. In evolutionary algorithms, selecting any solution from swarm basically relies on the natural selection principle of the survival-of-the-fittest to accelerate convergence. To make use of such principle, three selection strategies (i.e., tournament, proportional and linear rank-based methods) are employed in the exploration phase of HHO and introduces three new versions, which are Tournament HHO (THHO), Proportional HHO (PHHO), and Linear-Rank HHO (LHHO). In order to evaluate the performance of the proposed HHO versions, 23 well-regarded benchmark functions with various sizes and complexities are utilized as well as three real-world engineering problems. The sensitivity of proposed HHO versions to their parameter settings are studied and analyzed. Thereafter, a scalability study is conducted to show the effect of the population dimensions on the proposed HHO versions. Comparative evaluation shows that THHO version has superiority over other proposed HHO versions. Furthermore, the proposed HHO versions show enhanced trade off between the exploratory and exploitative trends and a better local optima avoidance. They are able to produce viable results competitively comparable with other eleven state-of-the-art methods using the same benchmark functions. Interestingly, the proposed variants of HHO are able to yield new results for some benchmark functions. Furthermore, three real-world engineering optimization problem of IEEE CEC2011 are also used in the evaluation process. Again, the proposed variants of HHO are able to achieve the best results. The information, guides and supplementary accessible files for this research will be publicly available at https://aliasgharheidari.com. [ABSTRACT FROM AUTHOR]

• Method required minimum number of sensors to predict cracks in turbine blades. • Method used preceding damage data to predict current crack locations. • Robustness and optimal sensor location were examined and verified. • Method predicted crack locations with accuracy of more than 90%. • Proposed method is capable of generating real-time predictions. Undetected micro-cracks in wind turbine blades lead to failure and costly repairs. Effective remedies depend on proactive measures taken to detect such cracks at early stages. Defects and cracks cause certain shifts in resonant peaks amplitude and frequency (spectral shifts) of the blade depending on the location and dimensions of the crack or damage. Precise location of the crack is difficult to ascertain due to its small dimensions and random causes. This study developed a special Kriging model for predicting crack location using spectral shifts in the blade's resonant peaks. Records of blade's spectral shifts collected for known crack locations were used to construct the Kriging model. The model predicts crack location by correlating spectral shifts of cracked blade with spectral shifts recorded from blades with known crack locations. Experimental validation demonstrated the ability of the method to detect micro crack locations with an accuracy of 95%. Experimental results are presented and discussed. [ABSTRACT FROM AUTHOR]

The original version of this article unfortunately contained a mistake in the acknowledgement section. [ABSTRACT FROM AUTHOR]

In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. In order to protect other members of society from the disease, social distancing is suggested by health experts. Herd immunity is a state the population reaches when most of the population is immune which results in the prevention of disease transmission. These concepts are modeled in terms of optimization concepts. CHIO mimics the herd immunity strategy as well as the social distancing concepts. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. This is to determine how the newly generated solution updates its genes with social distancing strategies. CHIO is evaluated using 23 well-known benchmark functions. Initially, the sensitivity of CHIO to its parameters is studied. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. For more validations, three real-world engineering optimization problems extracted from IEEE-CEC 2011 are used. Again, CHIO is proved to be efficient. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive version of β - hill climbing is proposed. In the original β - hill climbing, two control parameters are utilized to strike the right balance between a local-nearby exploitation and a global wide-range exploration during the search: N and β , respectively. Conventionally, these two parameters require an intensive study to find their suitable values. In order to yield an easy-to-use optimization method, this paper proposes an efficient adaptive strategy for these two parameters in a deterministic way. The proposed adaptive method is evaluated against 23 global optimization functions. The selectivity analysis to determine the optimal progressing values of N and β during the search is carried out. Furthermore, the behavior of the adaptive version is analyzed based on various problems with different complexity levels. For comparative evaluation, the adaptive version is initially compared with the original one as well as with other local search-based methods and other well-regarded methods using the same benchmark functions. Interestingly, the results produced are very competitive with the other methods. In a nutshell, the proposed adaptive β - hill climbing is able to achieve the best results on 10 out of 23 test functions. For more validation, the test functions established in IEEE-CEC2015 are used with various scaling values. The comparative results show the viability of the proposed adaptive method. [ABSTRACT FROM AUTHOR]

In this paper, a modified version of the Multi-objective Grey Wolf Optimizer (MGWO), known as linked-based GWO (LMGWO), is proposed for the Appliances Energy Scheduling Problem (AESP). The proposed LMGWO is utilized by combining the MGWO searching mechanism with a novel strategy, called neighbourhood selection strategy, to improve local exploitation capabilities. AESP is a problem that can be tackled by searching for the best appliances schedule according to a set of constraints and a dynamic pricing scheme(s) utilized for optimizing energy consumed at a particular period. Three objectives are considered to handle AESP: improving user comfort while reducing electricity bills and maintaining power systems' performance. Therefore, AESP is modelled as a multi-objective optimization problem to handle all objectives simultaneously. In the evaluation results, the LMGWO is tested using a new dataset containing 30 power consumption scenarios with up to 36 appliances. For comparative purposes, the same linked-based neighbourhood selection strategy is utilized with other three optimization algorithms, including particle swarm optimization, salp swarm optimization, and wind-driven algorithm. The performance of the modified versions is compared with each other and that of the original versions to show their improvements. Furthermore, the proposed LMGWO is compared with eight state-of-the-art methods using their recommended datasets to show the viability of the proposed LMGWO. Interestingly, the proposed LMGWO is able to outperform the compared methods in almost all produced results. [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]

This study proposes a novel framework to improve intrusion detection system (IDS) performance based on the data collected from the Internet of things (IoT) environments. The developed framework relies on deep learning and metaheuristic (MH) optimization algorithms to perform feature extraction and selection. A simple yet effective convolutional neural network (CNN) is implemented as the core feature extractor of the framework to learn better and more relevant representations of the input data in a lower-dimensional space. A new feature selection mechanism is proposed based on a recently developed MH method, called Reptile Search Algorithm (RSA), which is inspired by the hunting behaviors of the crocodiles. The RSA boosts the IDS system performance by selecting only the most important features (an optimal subset of features) from the extracted features using the CNN model. Several datasets, including KDDCup-99, NSL-KDD, CICIDS-2017, and BoT-IoT, were used to assess the IDS system performance. The proposed framework achieved competitive performance in classification metrics compared to other well-known optimization methods applied for feature selection problems. [ABSTRACT FROM AUTHOR]

The Coronavirus herd immunity optimizer (CHIO) is a new human-based optimization algorithm that imitates the herd immunity strategy to eliminate of the COVID-19 disease. In this paper, the coronavirus herd immunity optimizer (CHIO) is modified to tackle a discrete power scheduling problem in a smart home (PSPSH). PSPSH is a combinatorial optimization problem with NP-hard features. It is a highly constrained discrete scheduling problem concerned with assigning the operation time for smart home appliances based on a dynamic pricing scheme(s) and several other constraints. The primary objective when solving PSPSH is to maintain the stability of the power system by reducing the ratio between average and highest power demand (peak-to-average ratio (PAR)) and reducing electricity bill (EB) with considering the comfort level of users (UC). This paper modifies and adapts the CHIO algorithm to deal with such discrete optimization problems, particularly PSPSH. The adaptation and modification include embedding PSPSH problem-specific operators to CHIO operations to meet the discrete search space requirements. PSPSH is modeled as a multi-objective problem considering all objectives, including PAR, EB, and UC. The proposed method is examined using a dataset that contains 36 home appliances and seven consumption scenarios. The main CHIO parameters are tuned to find their best values. These best values are used to evaluate the proposed method by comparing its results with comparative five metaheuristic algorithms. The proposed method shows encouraging results and almost obtains the best results in all consumption scenarios. [ABSTRACT FROM AUTHOR]

COVID‐19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID‐19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID‐19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID‐19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID‐19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID‐19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID‐19 and inspire their works for future development. [ABSTRACT FROM AUTHOR]

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86 % using only 24 sensors with AR 20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas. [ABSTRACT FROM AUTHOR]

Structured population in evolutionary algorithms (EAs) is an important research track where an individual only interacts with its neighboring individuals in the breeding step. The main rationale behind this is to provide a high level of diversity to overcome the genetic drift. Cellular automata concepts have been embedded to the process of EA in order to provide a decentralized method in order to preserve the population structure. Harmony search (HS) is a recent EA that considers the whole individuals in the breeding step. In this paper, the cellular automata concepts are embedded into the HS algorithm to come up with a new version called cellular harmony search (cHS). In cHS, the population is arranged as a two-dimensional toroidal grid, where each individual in the grid is a cell and only interacts with its neighbors. The memory consideration and population update are modified according to cellular EA theory. The experimental results using benchmark functions show that embedding the cellular automata concepts with HS processes directly affects the performance. Finally, a parameter sensitivity analysis of the cHS variation is analyzed and a comparative evaluation shows the success of cHS. [ABSTRACT FROM AUTHOR]

This paper presents an efficient method for aircraft landing problem (ALP) based on a mechanism that hybridizes the iterated local search (ILS) and simulated annealing (SA) algorithms. ALP is handled by scheduling each incoming aircraft to land on a runway in accordance with a predefined landing time frame. The main objective to address is to find a feasible aircraft scheduling solution within the range of target time. The proposed hybridization method complements the advantages of both ILS and SA in a single optimization framework, referred to as iterated simulated annealing (ISA). The optimization framework of ISA has two main loops: an inner loop and an outer loop. In the inner loop, SA is utilized through a cooling schedule and a relaxing acceptance strategy to allow ISA to escape the local optima. In the outer loop, the restart mechanism and perturbation operation of the standard ILS are used to empower ISA to broadly navigate different search space regions. Extensive evaluation experiments were conducted on thirteen small- and large-sized ALP instances to assess the effectiveness and solution quality of ISA. The obtained results confirm that the proposed ISA method considerably performs better than other state-of-the-art methods in which ISA is capable of reaching new best results in 4 out of 24 large-sized problem instances as well as obtaining the best results in all small-sized instances. Evaluation on large-sized instances confirms a high degree of performance. As a new line of research, ISA is an effective method for ALP which can be further investigated for other combinatorial optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents a novel meta-heuristic algorithm so-called White Shark Optimizer (WSO) to solve optimization problems over a continuous search space. The core ideas and underpinnings of WSO are inspired by the behaviors of great white sharks, including their exceptional senses of hearing and smell while navigating and foraging. These aspects of behavior are mathematically modeled to accommodate a sufficiently adequate balance between exploration and exploitation of WSO and to assist search agents to explore and exploit each potential area of the search space in order to achieve optimization. The search agents of WSO randomly update their position in connection with best-so-far solutions, to eventually arrive at the optimal outcome. The performance of WSO was comprehensively benchmarked on a set of 29 test functions from the CEC-2017 test suite for several dimensions. WSO was further applied to solve the benchmark problems of the CEC-2011 evolutionary algorithm competition to prove its reliability and applicability to real-world problems. A thorough analysis of computational and convergence results was presented to shed light on the efficacy and stability levels of WSO. The performance score of WSO in terms of several statistical methods was compared with 9 well-established meta-heuristics based on the solutions generated. Friedman's and Holm's tests of the results showed that WSO revealed reasonable solutions, in terms of global optimality, avoidance of local minima and solution quality, compared to other existing meta-heuristics. [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: 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]

DNA microarray technology is the fabrication of a single chip to contain a thousand genetic codes. Each microarray experiment can analyze many thousands of genes in parallel. The outcomes of the DNA microarray is a table/matrix, called gene expression data. Pattern recognition algorithms are widely applied to gene expression data to differentiate between health and cancerous patient samples. However, gene expression data is characterized as a high dimensional data that typically encompassed of redundant, noisy, and irrelevant genes. Datasets with such characteristics pose a challenge to machine learning algorithms. This is because they impede the training and testing process and entail high resource computations that deteriorate the classification performance. In order to avoid these pitfalls, gene selection is needed. This paper proposes a new hybrid filter-wrapper approach using robust Minimum Redundancy Maximum Relevancy (rMRMR) as a filter approach to choose the top-ranked genes. Modified Gray Wolf Optimizer (MGWO) is used as a wrapper approach to seek further small sets of genes. In MGWO, new optimization operators inspired by the TRIZ-inventive solution are coupled with the original GWO to increase the diversity of the population. To evaluate the performance of the proposed method, nine well-known microarray datasets are tested. The support vector machine (SVM) is employed for the classification task to estimate the goodness of the selected subset of genes. The effectiveness of TRIZ optimization operators in MGWO is evaluated by investigating the convergence behavior of GWO with and without TRIZ optimization operators. Moreover, the results of MGWO are compared with seven state-of-art gene selection methods using the same datasets based on classification accuracy and the number of selected genes. The results show that the proposed method achieves the best results in four out of nine datasets and it obtains remarkable results on the remaining datasets. The experimental results demonstrated the effectiveness of the proposed method in searching the gene search space and it was able to find the best gene combinations. [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]