Because of successful implementations and high intensity, metaheuristic research has been extensively reported in literature, which covers algorithms, applications, comparisons, and analysis. Though, little has been evidenced on insightful analysis of metaheuristic performance issues, and it is still a “black box” that why certain metaheuristics perform better on specific optimization problems and not as good on others. The performance related analyses performed on algorithms are mostly quantitative via performance validation metrics like mean error, standard deviation, and co-relations have been used. Moreover, the performance tests are often performed on specific benchmark functions—few studies are those which involve real data from scientific or engineering optimization problems. In order to draw a comprehensive picture of metaheuristic research, this paper performs a survey of metaheuristic research in literature which consists of 1222 publications from year 1983 to 2016 (33 years). Based on the collected evidence, this paper addresses four dimensions of metaheuristic research: introduction of new algorithms, modifications and hybrids, comparisons and analysis, and research gaps and future directions. The objective is to highlight potential open questions and critical issues raised in literature. The work provides guidance for future research to be conducted more meaningfully that can serve for the good of this area of research.

Metaheuristic research: a comprehensive survey

K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data

Meta-heuristics are problem-independent optimization techniques which provide an optimal solution by exploring and exploiting the entire search space iteratively. These techniques have been successfully engaged to solve distinct real-life and multidisciplinary problems. A good amount of literature has been already published on the design and role of various meta-heuristic algorithms and on their variants. The aim of this study is to present a comprehensive analysis of nature-inspired meta-heuristic utilized in the domain of feature selection. A systematic review methodology has been used for synthesis and analysis of one hundered and seventy six articles. It is one of the important multidisciplinary research areas that assist in finding an optimal set of features so that a better rate of classification can be achieved. The concept of feature selection process along with relevance and redundancy metric is briefly elucidated. A categorical list of nature-inspired meta-heuristic techniques has been presented. The major applications of these techniques are explored to highlight the least and most explored areas. The area of disease diagnosis has been extensively assessed. In addition, the special attention has been given on highlighting the role and performance of binary and chaotic variants of different nature-inspired meta-heuristic techniques. The summary of nature-inspired meta-heuristic methods and their variants along with datasets, performance (mean, best, worst, error rate and standard deviation) is also depicted. In addition, the detailed publication trend of meta-heuristic feature selection approaches has also been presented. The research gaps have been identified for the researcher who inclines to design or analyze the performance of divergent meta-heuristic techniques in solving feature selection problem.

A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem

This chapter introduces teaching-learning-based optimization (TLBO) algorithm and its elitist and non-dominated sorting multiobjective versions. Two examples of unconstrained and constrained benchmark functions and an example of a multiobjective constrained problem are presented to demonstrate the procedural steps of the algorithm.

Teaching-Learning-Based Optimization Algorithm

A survey of teaching–learning-based optimization

An adaptive encoding learning for artificial bee colony algorithms

To improve the ability of the sine cosine algorithm (SCA) in the exploitation process, an improved symmetric SCA with adaptive probability selection (SSCA-APS), is proposed. The search process of this algorithm is divided into early and late stages. In the early stage, the operators of the traditional SCA algorithm continue to be used. In the late stage, three improvements were applied. Firstly, the symmetric sine and cosine operators are proposed. The adaptive probability selection strategy is adopted to integrate original sine and cosine operators and symmetric sine and cosine operators for dynamically adjusting the step size of the search range. Furthermore, to prevent the population from falling into local optimization, Gaussian perturbation is used to mutate the globally optimal individuals of the current generation. In addition, the information of two randomly selected individuals and the globally optimal individual is integrated by quadratic interpolation to maintain population diversity and produce a new individual. 23 test functions were used to verify the performance of the proposed algorithm. The simulation results indicate that the performance of the SSCA-APS algorithm has competitiveness when it is compared with classical SCA and some state-of-the-art SCA variants.

/pdf/a-symmetric-sine-cosine-algorithm-with-adaptive-probability-1mo6bt49jt.pdf

A Symmetric Sine Cosine Algorithm With Adaptive Probability Selection

Daily tourist flow forecasting using SPCA and CNN‐LSTM neural network

Aiming at the problem of educational news classification, this paper proposes a method of educational news classification based on emotional dictionary. This paper takes educational news as the application background. First, this paper summarizes the common sentiment dictionary and the education-related sentiment words of manually arranged. Then, this paper uses the Laplace smoothing SO-PMI algorithm to expand the emotional dictionary of educational news. Finally, the whole news is divided into several clauses, from which emotional key words in education news is required by combining emotional dictionary, and calculate the weights of each clause according to corresponding rules and get the weights sum of each clause, and we get the emotional tendencies of the whole education news in order to identify the positive and negative classification of educational news. Experimental results show that the method of educational news classification based on emotional news has higher precision and recall rate.

A method of educational news classification based on emotional dictionary

Fingerprint location algorithm is often used in process of indoor location. But the complex environment will cause fluctuations in signal strength and this fluctuation will lead to positioning errors. In order to correct the error we use Gaussian filtering to preprocess the location data and adopt based on voting region decision policy and based on maximum signal strength AP selection policy. SSCM-WKNN algorithm that integrates these policies and the WKNN algorithm of K-value optimized are implemented in a simulation environment, and experiments show that it is effective to reduce the positioning error.

An improved integrated fingerprint location algorithm based on WKNN

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