A New K means Grey Wolf Algorithm for Engineering Problems

Purpose: The development of metaheuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. One of the common metaheuristic optimization algorithms is called Grey Wolf Optimization (GWO). The algorithm works based on imitation of the wolves' searching and the process of attacking grey wolves. The main purpose of this paper to overcome the GWO problem which is trapping into local optima. Design or Methodology or Approach: In this paper, the K-means clustering algorithm is used to enhance the performance of the original Grey Wolf Optimization by dividing the population into different parts. The proposed algorithm is called K-means clustering Grey Wolf Optimization (KMGWO). Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019 benchmark test functions. Results prove that KMGWO is better compared to GWO. KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank in terms of performance. Statistical results proved that KMGWO achieved a higher significant value compared to the compared algorithms. Also, the KMGWO is used to solve a pressure vessel design problem and it has outperformed results. Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of performance. Also, the KMGWO is used to solve a classical engineering problem and it is superiorComment: 15 pages. World Journal of Engineering, 202

An Improved Chaotic Grey Wolf Optimization Algorithm (CGWO)

Grey Wolf Optimization (GWO) is a new type of swarm-based technique for dealing with realistic engineering design constraints and unconstrained problems in the field of metaheuristic research. Swarm-based techniques are a type of population-based algorithm inspired by nature that can produce low-cost, quick, and dependable solutions to a wider variety of complications. It is the best choice when it can achieve faster convergence by avoiding local optima trapping. This work incorporates chaos theory with the standard GWO to improve the algorithm's performance due to the ergodicity of chaos. The proposed methodology is referred to as Chaos-GWO (CGWO). The CGWO improves the search space's exploration and exploitation abilities while avoiding local optima trapping. Using different benchmark functions, five distinct chaotic map functions are examined, and the best chaotic map is considered to have great mobility and ergodicity characteristics. The results demonstrated that the best performance comes from using the suitable chaotic map function, and that CGWO can clearly outperform standard GWO

Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework

Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA

An Improved Binary Grey-Wolf Optimizer with Simulated Annealing for Feature Selection

This paper proposes improvements to the binary grey-wolf optimizer (BGWO) to solve the feature selection (FS) problem associated with high data dimensionality, irrelevant, noisy, and redundant data that will then allow machine learning algorithms to attain better classification/clustering accuracy in less training time. We propose three variants of BGWO in addition to the standard variant, applying different transfer functions to tackle the FS problem. Because BGWO generates continuous values and FS needs discrete values, a number of V-shaped, S-shaped, and U-shaped transfer functions were investigated for incorporation with BGWO to convert their continuous values to binary. After investigation, we note that the performance of BGWO is affected by the selection of the transfer function. Then, in the first variant, we look to reduce the local minima problem by integrating an exploration capability to update the position of the grey wolf randomly within the search space with a certain probability; this variant was abbreviated as IBGWO. Consequently, a novel mutation strategy is proposed to select a number of the worst grey wolves in the population which are updated toward the best solution and randomly within the search space based on a certain probability to determine if the update is either toward the best or randomly. The number of the worst grey wolf selected by this strategy is linearly increased with the iteration. Finally, this strategy is combined with IBGWO to produce the second variant of BGWO that was abbreviated as LIBGWO. In the last variant, simulated annealing (SA) was integrated with LIBGWO to search around the best-so-far solution at the end of each iteration in order to identify better solutions. The performance of the proposed variants was validated on 32 datasets taken from the UCI repository and compared with six wrapper feature selection methods. The experiments show the superiority of the proposed improved variants in producing better classification accuracy than the other selected wrapper feature selection algorithms

Nature Inspired Evolutionary Swarm Optimizers for Biomedical Image and Signal Processing -- A Systematic Review

The challenge of finding a global optimum in a solution search space with limited resources and higher accuracy has given rise to several optimization algorithms. Generally, the gradient-based optimizers converge to the global solution very accurately, but they often require a large number of iterations to find the solution. Researchers took inspiration from different natural phenomena and behaviours of many living organisms to develop algorithms that can solve optimization problems much quicker with high accuracy. These algorithms are called nature-inspired meta-heuristic optimization algorithms. These can be used for denoising signals, updating weights in a deep neural network, and many other cases. In the state-of-the-art, there are no systematic reviews available that have discussed the applications of nature-inspired algorithms on biomedical signal processing. The paper solves that gap by discussing the applications of such algorithms in biomedical signal processing and also provides an updated survey of the application of these algorithms in biomedical image processing. The paper reviews 28 latest peer-reviewed relevant articles and 26 nature-inspired algorithms and segregates them into thoroughly explored, lesser explored and unexplored categories intending to help readers understand the reliability and exploration stage of each of these algorithms

A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems

This paper is concerned with data clustering to separate clusters based on the connectivity principle for categorizing similar and dissimilar data into different groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. They are designed to escape from a local optimum problem by allowing flexible movements or random behaviors. In this study, we attempt to conceptualize a powerful approach using the three main components: Chimp Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm (GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions of ChOA with two different independent groups' strategies and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Secondly, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. The results are evaluated against seven popular meta-heuristic optimization algorithms and eight recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the achievement in minimizing the Sum of Intra-Cluster Distances (SICD), obtaining the lowest Error Rate (ER), accelerating the convergence speed, and finding the optimal cluster centers.Comment: 48 pages, 14 Tables, 12 Figure

Niching grey wolf optimizer for multimodal optimization problems

Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorithm that requires less parameter tuning. However, the GWO suffers from premature convergence and fails to maintain the balance between exploration and exploitation for solving multi-modal problems. This study proposes a niching GWO (NGWO) that incorporates personal best features of PSO and a local search technique to address these issues. The proposed algorithm has been tested for 23 benchmark functions and three engineering cases. The NGWO outperformed all other considered algorithms in most of the test functions compared to state-of-the-art metaheuristics such as PSO, GSA, GWO, Jaya and two improved variants of GWO, and niching CSA. Statistical analysis and Friedman tests have been conducted to compare the performance of these algorithms thoroughly