A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients

Harris’ Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset

A novel multistart hyper-heuristic algorithm on the grid for the quadratic assignment problem

Hyper-heuristics introduce novel approaches for solving challenging combinatorial optimization problems by operating over a set of low level (meta)-heuristics. This is achieved by an evolutionary selection mechanism that controls and combines the strengths of the low level (meta) -heuristics. In this study, we propose a high-performance MultiStart Hyper-heuristic algorithm (MSH-QAP) on the grid for the solution of the Quadratic Assignment Problem (QAP). MSH-QAP algorithm makes use of state-of:the-art (meta)-heuristics, Simulated Annealing (SA), Robust Tabu Search (RTS), Ant Colony Optimization (FAnt), and Breakout Local Search (BLS) that have been reported among the best performing algorithms for the solution of difficult QAP instances in standard benchmark libraries. In the first phase of the algorithm, the most appropriate(meta)-heuristic with its near-optimal parameter settings is selected by using a genetic algorithm optimization layer that uses a self-adaptive parameter setting method for the given. problem instance. In the second phase, if an optimal solution cannot be found, selected best performing (meta) heuristic (with its finely adjusted parameter settings) is executed on the grid using parallel processing and performing several multistarts in order to increase the quality of the discovered solution. MSH-QAP algorithm is tested on 134 problem instances of the QAPLIB benchmark and is shown to be able to solve 122 of the instances exactly. The overall deviation for the problem instances is obtained as 0.013% on the average

Dynamic Programming with Ant Colony Optimization Metaheuristic for Optimization of Distributed Database Queries

In this paper, we introduce and evaluate a new query optimization algorithm based on Dynamic Programming (DP) and Ant Colony Optimization (ACO) metaheuristic for distributed database queries. DP algorithm is widely used for relational query optimization, however its memory, and time requirements are very large for the query optimization problem in a distributed database environment which is an NP-hard combinatorial problem. Our aim is to combine the power of DP with heuristic approaches so that we can have a polynomial time approximation algorithm based on a constructive method. DP and ACO algorithms together provide execution plans that are very close to the best performing solutions, and achieve this in polynomial time. This makes our algorithm viable for large multi-way join queries

Optimization of one-dimensional Bin Packing Problem with island parallel grouping genetic algorithms

The well-known one-dimensional Bin Packing Problem (BPP) of whose variants arise in many real life situations is a challenging NP-Hard combinatorial optimization problem. Metaheuristics are widely used optimization tools to find (near-) optimal solutions for solving large problem instances of BPP in reasonable running times. With this study, we propose a set of robust and scalable hybrid parallel algorithms that take advantage of parallel computation techniques, evolutionary grouping genetic metaheuristics, and bin-oriented heuristics to obtain solutions for large scale one-dimensional BPP instances. A total number of 1318 benchmark problems are examined with the proposed algorithms and it is shown that optimal solutions for 88.5% of these instances can be obtained with practical optimization times while solving the rest of the problems with no more than one extra bin. When the results are compared with the existing state-of-the-art heuristics, the developed parallel hybrid grouping genetic algorithms can be considered as one of the best one-dimensional BPP algorithms in terms of computation time and solution quality

A robust Island Parallel Genetic Algorithm for the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) is a difficult and important problem studied in the domain of combinatorial optimisation. It is possible to solve QAP instances with 10--20 facilities using exhaustive parallel algorithms within a few days on a cluster machine. However, large QAP instances with more than 100 facilities are not solvable using exhaustive techniques. We have explored a variety of Genetic Algorithm crossover operators for this problem and verified its performance experimentally using well-known instances from the QAPLIB library. By increasing the number of processors, generations and population sizes we have been able to find solutions that are the same as (or very close to) the best reported solutions for large QAP instances in QAPLIB. In order to parallelise the Genetic Algorithm we generate and evolve separate solution pools on each cluster processor, using an island model. This model exchanges 10% of each processor's solutions at the initial stages of optimisation. We show experimentally that both execution times and solution qualities are improved for large QAP instances by using our Island Parallel Genetic Algorithm

Context-sensitive and keyword density-based supervised machine learning techniques for malicious webpage detection

Conventional malicious webpage detection methods use blacklists in order to decide whether a webpage is malicious or not. The blacklists are generally maintained by third-party organizations. However, keeping a list of all malicious Web sites and updating this list regularly is not an easy task for the frequently changing and rapidly growing number of webpages on the web. In this study, we propose a novel context-sensitive and keyword density-based method for the classification of webpages by using three supervised machine learning techniques, support vector machine, maximum entropy, and extreme learning machine. Features (words) of webpages are obtained from HTML contents and information is extracted by using feature extraction methods: existence of words, keyword frequencies, and keyword density techniques. The performance of proposed machine learning models is evaluated by using a benchmark data set which consists of one hundred thousand webpages. Experimental results show that the proposed method can detect malicious webpages with an accuracy of 98.24%, which is a significant improvement compared to state-of-the-art approaches

Robust hyper-heuristic algorithms for the offline oriented/non-oriented 2D bin packing problems

The offline 2D bin packing problem (2DBPP) is an NP-hard combinatorial optimization problem in which objects with various width and length sizes are packed into minimized number of 2D bins. Various versions of this well-known industrial engineering problem can be faced frequently. Several heuristics have been proposed for the solution of 2DBPP but it has not been possible to find the exact solutions for large problem instances. Next fit, first fit, best fit, unified tabu search, genetic and memetic algorithms are some of the state-of-the-art methods successfully applied to this important problem. In this study, we propose a set of novel hyper-heuristic algorithms that select/combine the state-of-the-art heuristics and local search techniques for minimizing the number of 2D bins. The proposed algorithms introduce new crossover and mutation operators for the selection of the heuristics. Through the results of exhaustive experiments on a set of offline 2DBPP benchmark problem instances, we conclude that the proposed algorithms are robust with their ability to obtain high percentage of the optimal solutions

A Comprehensive Survey on Recent Metaheuristics for Feature Selection

Feature selection has become an indispensable machine learning process for data preprocessing due to the ever-increasing sizes in actual data. There have been many solution methods proposed for feature selection since the 1970s. For the last two decades, we have witnessed the superiority of metaheuristic feature selection algorithms, and tens of new ones are being proposed every year. This survey focuses on the most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluation, and parameter setting techniques. Current challenges of the metaheuristic feature selection algorithms and possible future research topics are examined and brought to the attention of the researchers as well. Keywords: Feature selection, Survey, Metaheuristic algorithms, Machine learning, Classification

Robust parallel hybrid artificial bee colony algorithms for the multi-dimensional numerical optimization

This study proposes a set of new robust parallel hybrid metaheuristic algorithms based on artificial bee colony (ABC) and teaching learning-based optimization (TLBO) for the multi-dimensional numerical problems. The best practices of ABC and TLBO are implemented to provide robust algorithms on a distributed memory computation environment using MPI libraries. Island parallel versions of the proposed hybrid algorithm are observed to obtain much better results than those of sequential versions. Parallel pseudorandom number generators are used to provide diverse solution candidates to prevent stagnation into local optima. The performances of the proposed hybrid algorithms are compared with eight different metaheuristics algorithms of particle swarm optimization, differential evolution variants, ABC variants and evolutionary algorithm. The empirical results show that the new hybrid parallel algorithms are scalable and the best performing algorithms when compared to the state-of-the-art metaheuristics.Peer reviewe

Fuzzy Processing in Surveillance Wireless Sensor Networks

This paper introduces a new distributed fuzzy network clustering approach that specifically targets surveillance applications of wireless sensor networks. Surveillance domain heavily consists of multimedia applications which require heavy energy consumption. Moreover, in sensor networks, positioned nodes can be relocated either by users or external events which might yield a non-uniform deployment in the long run. In this regard, fuzzy processing becomes crucial if the domain and existing resources include uncertainties. In this study, a distributed fuzzy clustering approach is introduced and then experimentally evaluated. The obtained results on the effect of fuzzy processing in heterogeneous sensor networks are presented