An improved adaptive binary Harmony Search algorithm

An Overview of the Application of Harmony Search for Chemical Engineering Optimization

Harmony search algorithm and its variants have been used in several applications in medicine, telecommunications, computer science, and engineering. This article reviews the global and multi-objective optimization for chemical engineering using harmony search. The main features of the HS method and several of its popular variants and hybrid versions including their relevant algorithm characteristics are described and discussed. A variety of global and multi-objective optimization problems from chemical engineering and their resolution using HS-based methods are also included. These problems involve thermodynamic calculations (phase stability analysis, phase equilibrium calculations, parameter estimation, and azeotrope calculation), heat exchanger design, distillation simulation, life cycle analysis, and water distribution systems, among others. Remarks on future developments of HS and its related algorithms for global and multi-objective optimization in chemical engineering are also provided in this review. HS is a reliable and promising stochastic optimizer to resolve challenging global and multi-objective optimization problems for process systems engineering.

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive review of the BA, as well as evaluates its main characteristics by comparing it with other optimization algorithms. The review paper highlights the performance of BA in different applications and the modifications that have been conducted by researchers (i.e., variants of BA). At the end, the conclusions focus on the current work on BA, highlighting its weaknesses, and suggest possible future research directions. The review paper will be helpful for the researchers and practitioners of BA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

Adaptive binary multi-objective harmony search algorithm for channel selection and cross-subject generalization in motor imagery-based BCI

OBJECTIVE

Multi-channel EEG data containing redundant information and noise may result in low classification accuracy and high computational complexity, which limits the practicality of motor imagery-based BCI systems. Therefore, channel selection can improve BCI performance and contribute to user convenience. Additionally, cross-subject generalization is a key topic in the channel selection of motor imagery-based BCI.

APPROACH

In this study, an adaptive binary multi-objective harmony search (ABMOHS) algorithm is proposed to select the optimal set of channels. Furthermore, a new adaptive crosssubject generalization model (ACGM) is proposed. Three public motor imagery datasets were used to validate the effectiveness of the proposed method.

MAIN RESULTS

The Wilcoxon signed-rank test was performed on the test accuracies, and the results indicated that the ABMOHS method significantly outperformed all channels (p<0.001), the C3-Cz-C4 channels (p<0.001), and 20 channels (p<0.001) in the sensorimotor cortex. The ABMOHS algorithm based on Fisher's linear discriminant analysis (FLDA) and support vector machine (SVM) classifiers greatly reduces the number of selected channels, especially for larger channel sizes (Dataset 2), and obtains a comparative classification performance. Although there was no significant difference in test classification performance between ABMOHS and non-dominated sorting genetic algorithm II (NSGA-II) when FLDA and SVM were used, ABMOHS required less computational time than NSGA-II. Furthermore, the number of channels obtained by ABMOHS algorithm were significantly smaller than those obtained by CSP-Rank and correlation-based channel selection algorithm (CCS). Additionally, the generalization of ACGM to untrained subjects shows that the mean test classification accuracy of ACGM created by a small sample of trained subjects is significantly better than that of Special-16 and Special-32.

SIGNIFICANCE

The proposed method can reduce the calibration time in the training phase and improve the practicability of MI-BCI.

A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds

OBJECTIVE

The classification of olfactory-induced electroencephalogram (olfactory EEG) signals has potential applications in disease diagnosis, emotion regulation, multimedia, and so on. To achieve high-precision classification, numerous EEG channels are usually used, but this also brings problems such as information redundancy, overfitting and high computational load. Consequently, channel selection is necessary to find and use the most effective channels.

APPROACH

In this study, we proposed a multi-strategy fusion binary harmony search (MFBHS) algorithm and combined it with the Riemannian geometry (RG) classification framework to select the optimal channel sets for olfactory EEG signal classification. MFBHS was designed by simultaneously integrating three strategies into the binary harmony search (BHS) algorithm, including an opposition-based learning strategy (OBL) for generating high-quality initial population, an adaptive parameter strategy (APS) for improving search capability, and a bitwise operation strategy (BOS) for maintaining population diversity. It performed channel selection directly on the covariance matrix of EEG signals, and used the number of selected channels and the classification accuracy computed by a Riemannian classifier to evaluate the newly generated subset of channels.

MAIN RESULTS

With five different classification protocols designed based on two public olfactory EEG datasets, the performance of MFBHS was evaluated and compared with some state-of-the-art algorithms. Experimental results reveal that our method can minimize the number of channels while achieving high classification accuracy compatible with using all the channels. In addition, cross-subject generalization tests of MFBHS channel selection show that subject-independent channels obtained through training can be directly used on untrained subjects without greatly compromising classification accuracy.

SIGNIFICANCE

The proposed MFBHS algorithm is a practical technique for effective use of EEG channels in olfactory recognition.

A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization

Over previous decades, many nature-inspired optimization algorithms (NIOAs) have been proposed and applied due to their importance and significance. Some survey studies have also been made to investigate NIOAs and their variants and applications. However, these comparative studies mainly focus on one single NIOA, and there lacks a comprehensive comparative and contrastive study of the existing NIOAs. To fill this gap, we spent a great effort to conduct this comprehensive survey. In this survey, more than 120 meta-heuristic algorithms have been collected and, among them, the most popular and common 11 NIOAs are selected. Their accuracy, stability, efficiency and parameter sensitivity are evaluated based on the 30 black-box optimization benchmarking (BBOB) functions. Furthermore, we apply the Friedman test and Nemenyi test to analyze the performance of the compared NIOAs. In this survey, we provide a unified formal description of the 11 NIOAs in order to compare their similarities and differences in depth and a systematic summarization of the challenging problems and research directions for the whole NIOAs field. This comparative study attempts to provide a broader perspective and meaningful enlightenment to understand NIOAs.

Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

This article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

MAX-SAT Problem using Hybrid Harmony Search Algorithm

Maximum Satisfiability problem is an optimization variant of the Satisfiability problem (SAT) denoted as MAX-SAT. The aim of this problem is to find Boolean variable assignment that maximizes the number of satisfied clauses in the Boolean formula. In case the number of variables per clause is equal or greater than three, then this problem is considered NP-complete. Hence, many researchers have developed techniques to deal with MAX-SAT. In this paper, we investigate the impact of different hybrid versions of binary harmony search (HS) algorithm on solving MAX 3-SAT problem. Therefore, we propose two novel hybrid binary HS algorithms. The first hybridizes Flip heuristic with HS, and the second uses Tabu search combined with Flip heuristic. Furthermore, a distinguished feature of our proposed approaches is using an objective function that is updated dynamically based on the stepwise adaptation of weights (SAW) mechanism to evaluate the MAX-SAT solution using the proposed hybrid versions. The performance of the proposed approaches is evaluated over standard MAX-SAT benchmarks, and the results are compared with six evolutionary algorithms and three stochastic local search algorithms. The obtained results are competitive and show that the proposed novel approaches are effective.

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems

Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application.

Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization algorithm

To solve some complicated function optimization problems, an artificial infectious disease optimization algorithm based on the SEIQR epidemic model is constructed, it is called as the SEIQR algorithm, or SEIQRA in short. The algorithm supposes that some human individuals exist in an ecosystem; each individual is characterized by a number of features; an infectious disease (SARS) exists in the ecosystem and spreads among individuals, the disease attacks only a part of features of an individual. Each infected individual may pass through such states as susceptibility (S), exposure (E), infection (I), quarantine (Q) and recovery (R). State S, E, I, Q and R can automatically and dynamically divide all people in the ecosystem into five classes, it provides the diversity for SEIQRA; that people can be attacked by the infectious disease and then transfer it to other people can cause information exchange among people, information exchange can make a person to transit from one state to another; state transitions can be transformed into operators of SEIQRA; the algorithm has 13 legal state transitions, which corresponds to 13 operators; the transmission rules of the infectious disease among people is just the logic to control state transitions of individuals among S, E, I, Q and R, it is just the synergy of SEIQRA, the synergy can be transformed into the logic structure of the algorithm. The 13 operators in the algorithm provide a native opportunity to integrate many operations with different purposes; these operations include average, differential, expansion, chevy, reflection and crossover. The 13 operators are executed equi-probably; a stable heart rhythm of the algorithm is realized. Because the infectious disease can only attack a small part of organs of a person when it spreads among people, the part variables iteration strategy (PVI) can be ingeniously applied, thus enabling the algorithm to possess of high performance of computation, high suitability for solving some kinds of complicated optimization problems, especially high dimensional optimization problems. Results show that SEIQRA has characteristics of strong search capability and global convergence, and has a high convergence speed for some complicated functions optimization problems.