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Purpose: Coronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction. Design/methodology/approach: In this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results. Findings: The main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task. Originality/value: The novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector. [ABSTRACT FROM AUTHOR]

• A novel graph-based feature grouping framework with different types of feature relationships. • An undirected graph representing features as vertices with edges. • Both straightforward and metaheuristic search methods for graph optimisation. Most current feature selection techniques are focused on the incremental inclusion or exclusion of single individual features with respect to the candidate feature subset(s). The use of such approaches, where only the individual inclusion/exclusion of features is considered, means that information such as the collaborative contribution or correlation between features may be lost. The result is that the final selected feature subset may contain high levels of inter-feature redundancy, assuming that the key information embedded in the original feature set can still be retained. To address this problem, a general framework based on graph processing and three-way mutual information metrics is proposed in this paper that works by clustering similar features into groups, from which representative features are then drawn. Two different feature selection techniques based on this framework are presented: one by straightforward selection of representative features from the resulting feature groups and the other via a music-inspired metaheuristic search. Comparative experimental evaluation against traditional feature selection techniques over a diverse range of 20 benchmark datasets demonstrates the efficacy of the proposed approach. With these implementations, significant performance gains can be made in terms of classification accuracy in general and dimensionality reduction in particular while retaining feature semantics and considerably lessening the redundancy in the returned feature subsets. [ABSTRACT FROM AUTHOR]

Highlights • The low effectiveness of the uniform randomization selection in the harmony search algorithm is discovered. • A novel measurement for evaluating the effectiveness of the search strategy of any evolutionary algorithm is developed. • A new search strategy for pitch adjustment and harmony randomization is proposed. • A parameter-setting-free technique without any extra statistic and external archive is developed. • The interesting and distinctive framework of the original HS algorithm is maintained. Abstract Harmony search is a music-inspired optimization algorithm. Our findings reveal that the common uniform randomization selection in the harmony search algorithm is less efficient than the Gaussian mutation to explore the global optimal solution, regardless of solving unimodal or multimodal problems under the same parameters settings. To evaluate the effectiveness of a given search strategy, a general measurement that can apply to other algorithms is also proposed. To enhance the search efficiency and effectiveness, a self-adaptive global-best harmony search algorithm is developed. The proposed algorithm takes full advantage of the valuable information hidden in harmony memory to devise a high-performance search strategy and integrates a self-adaptive mechanism to develop a parameter-setting-free technique. Moreover, it is as simple and straightforward to implement as the canonical harmony search algorithm. It does not require any extra statistic and external archive. It well maintains the interesting and distinctive framework of the original version in the evolutionary computation domain. The experimental results show that the proposed algorithm significantly outperforms the recent adaptive variant of the harmony search algorithm and achieves the strongly competitive performances compared with other state-of-the-art adaptive evolutionary algorithms. The proposed algorithm is also successfully applied into the real-world space trajectory optimization problem. [ABSTRACT FROM AUTHOR]

Harmony search is a powerful metaheuristic algorithm with excellent exploitation capabilities but suffers a very serious limitation of premature convergence if one or more initially generated solutions/harmonies are in the vicinity of local optimal. In order to remove this limitation this paper proposes a novel algorithm based on hybridization of Harmony search and Simulated Annealing called HS-SA to inherit their advantages in a complementary way. Taking the inspiration from Simulated Annealing the proposed HS-SA algorithm accepts even the inferior harmonies with a probability determined by parameter called Temperature. The Temperature parameter is initially kept high to favor exploration of search space and is linearly decreased to gradually shift focus to exploitation of promising search areas. The performance of HS-SA is tested on IEEE CEC 2014 benchmark functions and real life problem from computer vision called Camera Calibration problem. The numerical results demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This study presents a hybrid harmony search particle swarm optimization with global dimension selection (HHSPSO-GDS) for improving the performance of particle swarm optimization (PSO). In HHSPSO-GDS, a new global velocity updating strategy is introduced to enhance the neighborhood region search of the current best solution and to get a better trade-off between convergence rate and robustness. Additionally, a dynamic non-linear decreased inertia weight is utilized to balance the global exploration and local exploitation. Moreover, the best-worst improvisation mechanism of harmony search (HS) is implanted in the HHSPSO-GDS algorithm and a global dimension selection is employed in the improvisation process, which can effectively accelerate convergence. Global best information sharing strategy is developed to link the two layer exploration frames (PSO and HS). Finally, a comprehensive experimental study is conducted on a large number of benchmark functions. The experimental results reveal that HHSPSO-GDS performs better in terms of the quality of solution, convergence rate, robustness and scalability compared to various state-of-the-art PSOs and other meta-heuristic search algorithms. [ABSTRACT FROM AUTHOR]

The problem of identifying semantically aligned entities in different ontologies known as ontology mapping is an outstanding research area and lies at the heart of many semantic applications. The overarching goal of ontology mapping is to discover a valid and comprehensive mapping with the aim of maximizing the number of reasonable alignments of ontological entities. Recently many efforts to automate the ontology mapping have been carried out, with some problems such as scalability and efficiency still evident. In this paper, ontology mapping in heterogeneous knowledge bases is formalized as an optimization problem, and an efficient method called Harmony Search based Ontology Mapping (HSOMap) is proposed, that effectively finds a near-optimal mapping for two input ontologies. In this approach, we make use of many kinds of rating functions, which are also called base matchers to evaluate the similarity of entities. Each base matcher captures the similarity between entities from a different perspective and is able to exploit the available side information about the entities effectively. Also, a novel weighted harmonic-mean method is proposed to aggregate different metrics into a single similarity metric among all pairs of entities from two ontologies. After obtaining the combined similarity metric between ontological entities, a discrete harmony search algorithm is proposed to extract the best alignment. To demonstrate the merits and advantages of the HSOMap algorithm, we conduct a set of experiments on benchmark data sets and compare its performance to other state-of-the-other methods. Our experimental results demonstrate that applying harmony search in the context of ontology mapping is a feasible approach and improves the mapping effectiveness significantly. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have been broadly used to deal with a range of water resources optimization problems over the past decades. One issue that exists in the use of these algorithms is the requirement of large computational resources, especially when handling real-world problems. To overcome this challenge, this paper develops a hybrid optimization method, the so-called CSHS, in which a cuckoo search (CS) algorithm is combined with a harmony search (HS) scheme. Within this hybrid framework, the CS is employed to find the promising regions of the search space within the initial explorative stages of the search, followed by a thorough exploitation phase using the combined CS and HS algorithms. The utility of the proposed CSHS is demonstrated using four water distribution system design problems with increased scales and complexity. The obtained results reveal that the CSHS method outperforms the standard CS, as well as the majority of other meta-heuristics that have previously been applied to the case studies investigated, in terms of efficiently seeking optimal solutions. Furthermore, the CSHS has two control parameters that need to be fine-tuned compared to many other algorithms, which is appealing for its practical application as an extensive parametercalibration process is typically computationally very demanding. [ABSTRACT FROM AUTHOR]

It has been proven that the hybridization of the harmony search algorithm with a local search algorithm (LS) is essential to compensate for the inadequacy of its exploitation. However, the success of this hybridization relies on the achievement of a proper balance between HSA exploration and LS exploitation. The question is can we obtain this balance by adaptively selecting (i) HSA parameter values, (ii) LS and its parameters and (iii) the LS neighborhood structures? To address these issues, this work proposes a meta-harmony search algorithm (meta-HSA) that uses two HSA algorithms, an HSA-optimizer and HSA-solver. The HSA-optimizer will adaptively adjust the components and the configurations of the HSA-solver based on the search status. The HSA-solver, which is a hybridization of HSA and LS, takes the configuration generated by the HSA-optimizer as input and then solves the given problem instance. That is, the HSA-optimizer operates on the components and the configurations of the HSA-solver, while the HSA-solver operates directly on the given problem instance (the solution search space). The proposed meta-HSA was applied to Solomon’s vehicle routing problem with time windows benchmark to verify its effectiveness compared with standard HSA and the state-of-the art methods. The results of the comparison confirmed that the meta-HSA produces competitive results with respect to the other methods. Therefore, we can conclude that the meta-optimization technique does assist the hybrid HSA in obtaining the appropriate selection of its components and configurations during the search process. This demonstrates that the meta-HSA can provide a proper balance between exploration and exploitation by adaptively selecting (i) HSA parameter values, (ii) LS and its parameters and (iii) the LS neighborhood structures. Moreover, the meta-HSA optimizer decreases the effort exerted by the user in tuning these components and configurations. [ABSTRACT FROM AUTHOR]

Harmony search (HS) and its variants have been found successful applications, however with poor solution accuracy and convergence performance for high-dimensional (≥200) multimodal optimization problems. The reason is mainly huge search space and multiple local minima. To tackle the problem, we present a new HS algorithm called DIHS, which is based on Dynamic-Dimensionality-Reduction-Adjustment (DDRA) and dynamic fret width ( fw ) strategy. The former is for avoiding generating invalid solutions and the latter is to balance global exploration and local exploitation. Theoretical analysis on the DDRA strategy for success rate of update operation is given and influence of related parameters on solution accuracy is investigated. Our experiments include comparison on solution accuracy and CPU time with seven typical HS algorithms and four widely used evolutionary algorithms (SaDE, CoDE, CMAES and CLPSO) and statistical comparison by the Wilcoxon Signed-Rank Test with the seven HS algorithms and four evolutionary algorithms. The problems in experiments include twelve multimodal and four complex uni-modal functions with high-dimensionality. Experimental results indicate that the proposed approach can provide significant improvement on solution accuracy with less CPU time in solving high-dimensional multimodal optimization problems, and the more dimensionality that the optimization problem is, the more benefits it provides. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new Harmony Search algorithm has been defined. [•] Definition of a new improvisation phase involving a selection procedure. [•] New memory consideration process based on a recombination operator. [•] Large experimental phase using a suite of benchmark problems. [•] Better results than the ones produced by other well-known HS algorithms. [Copyright &y& Elsevier]

Highlights: [•] A novel learning-based harmony search (HS) algorithm is proposed in this paper. [•] Learning automata is employed for fine-tuning of HS parameters. [•] No parameter-setting is required in the proposed learning automata based harmony search algorithm. [•] The numerical results indicate that the proposed algorithm outperforms the other HS variants. [ABSTRACT FROM AUTHOR]

Abstract: Harmony search algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves solutions in the problem search space by mimicking the musical improvisation process in seeking agreeable harmony measured by aesthetic standards. The nurse rostering problem (NRP) is a well-known NP-hard scheduling problem that aims at allocating the required workload to the available staff nurses at healthcare organizations to meet the operational requirements and a range of preferences. This work investigates research issues of the parameter settings in HSA and application of HSA to effectively solve complex NRPs. Due to the well-known fact that most NRPs algorithms are highly problem (or even instance) dependent, the performance of our proposed HSA is evaluated on two sets of very different nurse rostering problems. The first set represents a real world dataset obtained from a large hospital in Malaysia. Experimental results show that our proposed HSA produces better quality rosters for all considered instances than a genetic algorithm (implemented herein). The second is a set of well-known benchmark NRPs which are widely used by researchers in the literature. The proposed HSA obtains good results (and new lower bound for a few instances) when compared to the current state of the art of meta-heuristic algorithms in recent literature. [Copyright &y& Elsevier]

Abstract: Harmony Search (HS), inspired by the music improvisation process, is a new meta-heuristic optimization method and has been successfully used to tackle the optimization problems in discrete or continuous space. Although the standard HS algorithm is able to solve binary-coded optimization problems, the pitch adjustment operator of HS is degenerated in the binary space, which spoils the performance of the algorithm. Based on the analysis of the drawback of the standard HS, an improved adaptive binary Harmony Search (ABHS) algorithm is proposed in this paper to solve the binary-coded problems more effectively. Various adaptive mechanisms are examined and investigated, and a scalable adaptive strategy is developed for ABHS to enhance its search ability and robustness. The experimental results on the benchmark functions and 0–1 knapsack problems demonstrate that the proposed ABHS is efficient and effective, which outperforms the binary Harmony Search, the novel global Harmony Search algorithm and the discrete binary Particle Swarm Optimization in terms of the search accuracy and convergence speed. [Copyright &y& Elsevier]

Abstract: Earliness/tardiness scheduling, in connection with Just-In-Time manufacturing philosophy, is crucial in reducing the production cost while maintaining a high service level. In this article, the well-known restrictive single-machine earliness/tardiness scheduling problem (RSMETP) is addressed. A novel hybrid metaheuristic, named as PHVNS, is developed for this NP-hard problem. There are three main innovative aspects in the proposed PHVNS. First, a permutation-based harmony search (PHS) is developed in compliance with the RSMETP features and optimality properties. Secondly, in order to provide an effective balance between the global diversification and local intensification, an enhanced basic variable neighborhood search (EBVNS) is incorporated into the iterative PHS. Thirdly, several problem-dependent mechanisms are introduced to facilitate the search process of PHVNS, such as the distinctive schedule representation, specific tweaks and speed-ups to the new harmony vector, and local search strategies within a neighborhood. An extensive calibration for the main parameters in PHVNS is carried out with the aid of a carefully designed set of experiments. To evaluate and validate the fine-tuned PHVNS, computational experiments are conducted upon the test suite of 280 benchmark instances. As demonstrated in the results, the tuned PHVNS is capable of reaching the high-quality solutions in reasonable time for most of the instances. Compared with some state-of-the-art metaheuristics for RSMETP, PHVNS shows high competitiveness. [Copyright &y& Elsevier]

Abstract: Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time. Then, harmony clustering is integrated with the K-means algorithm in three ways to achieve better clustering by combining the explorative power of HS with the refining power of the K-means. Contrary to the localized searching property of K-means algorithm, the proposed algorithms perform a globalized search in the entire solution space. Additionally, the proposed algorithms improve K-means by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, therefore, making it more stable. The behavior of the proposed algorithm is theoretically analyzed by modeling its population variance as a Markov chain. We also conduct an empirical study to determine the impacts of various parameters on the quality of clusters and convergence behavior of the algorithms. In the experiments, we apply the proposed algorithms along with K-means and a Genetic Algorithm (GA) based clustering algorithm on five different document datasets. Experimental results reveal that the proposed algorithms can find better clusters and the quality of clusters is comparable based on F-measure, Entropy, Purity, and Average Distance of Documents to the Cluster Centroid (ADDC). [Copyright &y& Elsevier]

Abstract: This paper describes two new harmony search (HS) meta-heuristic algorithms for engineering optimization problems with continuous design variables. The key difference between these algorithms and traditional (HS) method is in the way of adjusting bandwidth (bw). bw is very important factor for the high efficiency of the harmony search algorithms and can be potentially useful in adjusting convergence rate of algorithms to optimal solution. First algorithm, proposed harmony search (PHS), introduces a new definition of bandwidth (bw). Second algorithm, improving proposed harmony search (IPHS) employs to enhance accuracy and convergence rate of PHS algorithm. In IPHS, non-uniform mutation operation is introduced which is combination of Yang bandwidth and PHS bandwidth. Various engineering optimization problems, including mathematical function minimization problems and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of these algorithms. In all cases, the solutions obtained using IPHS are in agreement or better than those obtained from other methods. [Copyright &y& Elsevier]