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

In pattern mining, users generally set a minimum threshold to find useful patterns from databases. As a result, patterns with higher values than the user-given threshold are discovered. However, it is hard for the users to determine an appropriate minimum threshold. The reason for this is that they cannot predict the exact number of patterns mined by the threshold and control the mining result precisely, which can lead to performance degradation. To address this issue, top-k mining has been proposed for discovering patterns from ones with the highest value to ones with the kth highest value with setting the desired number of patterns, k. Top-k utility mining has emerged to consider characteristics of real-world databases such as relative importance of items and item quantities with the advantages of top-k mining. Although a relevant algorithm has been suggested in recent years, it generates a huge number of candidate patterns, which results in an enormous amount of execution time. In this paper, we propose an efficient algorithm for mining top-k high utility patterns with highly decreased candidates. For this purpose, we develop three strategies that can reduce the search space by raising a minimum threshold effectively in the construction of a global tree, where they utilize exact and pre-evaluated utilities of itemsets. Moreover, we suggest a strategy to identify actual top-k high utility patterns from candidates with the exact and pre-calculated utilities. Comprehensive experimental results on both real and synthetic datasets show that our algorithm with the strategies outperforms state-of-the-art methods.

Top- k high utility pattern mining with effective threshold raising strategies

Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization

In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: CSA, BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design.

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CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems

Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin’s theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997. This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years. The methodology used to search for relevant DE papers and an overview of the original DE are firstly explained. Recent advances in the modifications proposed to enhance the effectiveness and efficiency of the original DE are reviewed by analysing the strengths and weaknesses of each published work, followed by the potential applications of these DE variants in solving different real-world engineering problems. In contrast to most existing DE review papers, additional analyses are performed in this survey by investigating the impacts of various parameter settings on given DE variants to identify their optimal values required for solving certain problem classes. The qualities of modifications incorporated into selected DE variants are also evaluated by measuring the performance gains achieved in terms of search accuracy and/or efficiency against the original DE. The additional surveys conducted in this study are anticipated to provide more insightful perspectives for both beginners and experts of DE research, enabling their better understanding about current research trends and new motivations to outline appropriate strategic planning for future development works.

Differential evolution: A recent review based on state-of-the-art works

In this article, an effective metaheuristic algorithm named multi-trial vector-based differential evolution (MTDE) is proposed. The MTDE is distinguished by introducing an adaptive movement step designed based on a new multi-trial vector approach named MTV, which combines different search strategies in the form of trial vector producers (TVPs). In the developed MTV approach, the TVPs are applied on their dedicated subpopulation, which are distributed by a winner-based distribution policy, and share their experiences efficiently by using a life-time archive. The MTV can be deployed by different types of TVPs, particularly, we use the MTV approach in the MTDE algorithm by three TVPs: representative based trial vector producer, local random based trial vector producer, and global best history based trial vector producer. Therefore, this study introduces the MTV approach to boost the performance of the MTDE and demonstrates its advantages in dealing with problems of different levels of complexity. The performance of the proposed MTDE algorithm is evaluated on CEC 2018 benchmark suite which include unimodal, multimodal, hybrid, and composition functions and four complex engineering design problems. The experimental and statistical results are compared with state-of-the-art metaheuristic algorithms: GWO, WOA, SSA, HHO, CoDE, EPSDE, QUATRE, and MKE. The results demonstrate that the MTDE algorithm shows improved performance and benefits from high accuracy of optimal solutions obtained.

MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems

QANA: Quantum-based avian navigation optimizer algorithm

Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.

B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

R-GWO: Representative-based grey wolf optimizer for solving engineering problems

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Mohammad H. Nadimi-Shahraki

Papers

MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems

QANA: Quantum-based avian navigation optimizer algorithm

B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

R-GWO: Representative-based grey wolf optimizer for solving engineering problems

Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm