The difficulty and complexity of the real-world numerical optimization problems has grown manifold, which demands efficient optimization methods. To date, various metaheuristic approaches have been introduced, but only a few have earned recognition in research community. In this paper, a new metaheuristic algorithm called Archimedes optimization algorithm (AOA) is introduced to solve the optimization problems. AOA is devised with inspirations from an interesting law of physics Archimedes’ Principle. It imitates the principle of buoyant force exerted upward on an object, partially or fully immersed in fluid, is proportional to weight of the displaced fluid. To evaluate performance, the proposed AOA algorithm is tested on CEC’17 test suite and four engineering design problems. The solutions obtained with AOA have outperformed well-known state-of-the-art and recently introduced metaheuristic algorithms such genetic algorithms (GA), particle swarm optimization (PSO), differential evolution variants L-SHADE and LSHADE-EpSin, whale optimization algorithm (WOA), sine-cosine algorithm (SCA), Harris’ hawk optimization (HHO), and equilibrium optimizer (EO). The experimental results suggest that AOA is a high-performance optimization tool with respect to convergence speed and exploration-exploitation balance, as it is effectively applicable for solving complex problems. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/79822-archimedes-optimization-algorithm

Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems

In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global optimization and engineering design problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between the exploitation and exploration, and premature convergence of the GWO algorithm. The I-GWO algorithm benefits from a new movement strategy named dimension learning-based hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in nature. DLH uses a different approach to construct a neighborhood for each wolf in which the neighboring information can be shared between wolves. This dimension learning used in the DLH search strategy enhances the balance between local and global search and maintains diversity. The performance of the proposed I-GWO algorithm is evaluated on the CEC 2018 benchmark suite and four engineering problems. In all experiments, I-GWO is compared with six other state-of-the-art metaheuristics. The results are also analyzed by Friedman and MAE statistical tests. The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.

An improved grey wolf optimizer for solving engineering problems

Binary differential evolution with self-learning for multi-objective feature selection

Review of swarm intelligence-based feature selection methods

Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

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Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

An evolutionary gravitational search-based feature selection

A Feature Selection (FS) is a preprocessing step that becomes a mandatory when dealing with data a large set of features. FS process is known to be a NP-hard optimization problem. Therefore, metaheuristics algorithms proved their ability to tackle this problem as in other optimization problems. The Thermal Exchange Optimization (TEO) is a recent population-based metaheuristic algorithm that is based on Newton’s law of cooling. In this paper, a binary version of TEO algorithm (called BTEO) as a search strategy was used in a wrapper feature selection method for the first time in literature. Both K-Nearest Neighborhood (KNN) and Decision Tree (DT) classifiers were used in the evaluation process. Eighteen well-known UCI datasets were utilized to assess the performance of the proposed approach. To prove the efficiency of proposed approach, three popular wrapper FS methods that use nature inspired algorithms (i.e., Genetic Algorithm (GA), Particle Swarm Optimizer (PSO), and Grey Wolf Optimizer (GWO)) as search strategies, were used for comparison purposes, and the results demonstrate the effectiveness of the proposed approach in solving different feature selection tasks.

Binary Thermal Exchange Optimization for Feature Selection

Enrichment of high-density lipoproteins with phosphatidylethanolamine (36:5) impairs their protective biological activities and is associated with atherosclerosis in women

Mohammad Taradeh

Papers

An evolutionary gravitational search-based feature selection

Binary Thermal Exchange Optimization for Feature Selection

Enrichment of high-density lipoproteins with phosphatidylethanolamine (36:5) impairs their protective biological activities and is associated with atherosclerosis in women