Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

Grey wolf optimizer: a review of recent variants and applications

This paper proposes a novel metaheuristic algorithm called Chimp Optimization Algorithm (ChOA) inspired by the individual intelligence and sexual motivation of chimps in their group hunting, which is different from the other social predators. ChOA is designed to further alleviate the two problems of slow convergence speed and trapping in local optima in solving high-dimensional problems. In this paper, a mathematical model of diverse intelligence and sexual motivation of chimps is proposed. In this regard, four types of chimps entitled attacker, barrier, chaser, and driver are employed for simulating the diverse intelligence. Moreover, four main steps of hunting, i.e. driving, chasing, blocking, and attacking, are implemented. The proposed ChOA algorithm is evaluated in 3 main phases. First, a set of 30 mathematical benchmark functions is utilized to investigate various characteristics of ChOA. Secondly, ChOA was tested by 13 high-dimensional test problems. Finally, 10 real-world optimization problems were used to evaluate the performance of ChOA. The results are compared to several newly proposed meta-heuristic algorithms in terms of convergence speed, the probability of getting stuck in local minimums, and exploration, exploitation. Also, statistical tests were employed to investigate the significance of the results. The results indicate that the ChOA outperforms the other benchmark optimization algorithms.

Chimp optimization algorithm

In an organization, a group of people working for a common goal may not achieve their goal unless they organize themselves in a hierarchy called Corporate Rank Hierarchy (CRH). This principle motivates us to map the concept of CRH to propose a new algorithm for optimization that logically arranges the search agents in a hierarchy based on their fitness. The proposed algorithm is named as heap-based optimizer (HBO) because it utilizes the heap data structure to map the concept of CRH. The mathematical model of HBO is built on three pillars: the interaction between the subordinates and their immediate boss, the interaction between the colleagues, and self-contribution of the employees. The proposed algorithm is benchmarked with 97 diverse test functions including 29 CEC-BC-2017 functions with very challenging landscapes against 7 highly-cited optimization algorithms including the winner of CEC-BC-2017 (EBO-CMAR). In the first two experiments, the exploitative and explorative behavior of HBO is evaluated by using 24 unimodal and 44 multimodal functions, respectively. It is shown through experiments and Friedman mean rank test that HBO outperforms and secures 1 st rank. In the third experiment, we use 29 CEC-BC-2017 benchmark functions. According to Friedman mean rank test HBO attains 2 nd position after EBO-CMAR; however, the difference in ranks of HBO and EBO-CMAR is shown to be statistically insignificant by using Bonferroni method based multiple comparison test. Moreover, it is shown through the Friedman test that the overall rank of HBO is 1 st for all 97 benchmarks. In the fourth and the last experiment, the applicability on real-world problems is demonstrated by solving 3 constrained mechanical engineering optimization problems. The performance is shown to be superior or equivalent to the other algorithms, which have been used in the literature. The source code of HBO is publicly available at https://github.com/qamar-askari/HBO.

Heap-based optimizer inspired by corporate rank hierarchy for global optimization

Neural network (NN) finds role in variety of applications due to combined effect of feature extraction and classification availability in deep learning algorithms. In this paper, we have chosen SVM, logistic regression machine learning algorithms and NN for EEG signal classification. Two-layer LSTM and four-layer improved NN deep learning algorithms are proposed to improve the performance in EEG classification. Novelty lies in one-dimensional gradient descent activation functions with radial basis operations used in the initial layers of improved NN which help in achieving better performance. Statistical features namely mean, standard deviation, kurtosis and skewness are extracted for input EEG collected from Bonn database and then applied for various classification techniques. Accuracy, precision, recall and F1 score are the performance metrics used for analyzing the algorithms. Improved NN and LSTM give better performance compared to all other architectures. The simulations are carried out with variety of activation functions, optimizers and loss models to analyze the performance using Python in keras.

EEG signal classification using LSTM and improved neural network algorithms

The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation.

https://www.mdpi.com/1424-8220/19/17/3667/pdf

Sensors Driven AI-Based Agriculture Recommendation Model for Assessing Land Suitability

Classification of underwater acoustical dataset using neural network trained by Chimp Optimization Algorithm

Underwater targets classification using local wavelet acoustic pattern and Multi-Layer Perceptron neural network optimized by modified Whale Optimization Algorithm

Real-time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm

Due to the compatibility of the designed classifiers with MLP Neural Networks (MLP NNs), in this article, MLP NNs have been used to identify and classify active and passive sonar targets. On the one hand, the great importance of precise and immediate classification of sonar targets, and on the other hand, being trapped in local minimums and the low convergence speed in classic MLP NNs have led the newly proposed Dragonfly Algorithm (DA) to be offered for training MLP NNs. In order to assess the performance of the designed classifier, this algorithm have been compared with BBO, GWO, ALO, ACO, GSA and MVO algorithms in terms of precision of classification, convergence speed and the ability to avoid local optimum. To have a comprehensive comparison, the three sets of active and passive data were used. Simulation results indicate that DA-based classification have better results in all three datasets compared to benchmark algorithms.

Mohammad Khishe

Papers

Chimp optimization algorithm

Classification of underwater acoustical dataset using neural network trained by Chimp Optimization Algorithm

Underwater targets classification using local wavelet acoustic pattern and Multi-Layer Perceptron neural network optimized by modified Whale Optimization Algorithm

Real-time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm

Classification of Sonar Targets Using an MLP Neural Network Trained by Dragonfly Algorithm