Explore the vast range of titles available.

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.

Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers and concrete designers are generally faced with the slightest change in the conditions or objectives of the project, which makes it challenging to choose the optimal design among several ones. Besides, the experimental examination of all of them requires time and high costs. Hence, an efficient approach is to utilize artificial intelligence (AI) techniques to predict and optimize real‐world problems in concrete technology. Despite the large body of publications in this field, there are few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review that lists, summarizes, and categorizes the most widely used machine learning methods, meta‐heuristic algorithms, and hybrid approaches to concrete issues. To this end, 457 publications are considered during the recent decade with a scientometric approach to highlight the annual trend/active journals/top researchers/co‐occurrence of key title words/countries' participation/research hotspots. In addition, AI techniques are classified into distinct clusters using VOSviewer clustering visualization to identify the application scope and their relationship through the link strength. The findings can be a beacon to help researchers utilize AI techniques in future research on advanced concrete technology.

Automated test data generation for path coverage (ATDG-PC) plays an important role in software testing. In this study, ATDG-PC is applied to the case of cloud computing such as Hadoop programmes which are more difficult to search for high-rate path coverage than the normal programmes. The search scale of ATDG-PC is usually enormous, while the relationship between the variables and the paths is unknown. First, a rapid meta-heuristic algorithm particle swarm optimisation (PSO) was chosen to solve the problem of large-scale search. Second, the strategy of convergence speed controller was used to improve the performance of PSO by mining heuristic information from the found paths. The controller adjusts the convergence speed balance periodically by two conditions and rules. The first strategy slows the convergence speed when the algorithm is premature convergence and is trapped in a local optimum. The second strategy accelerates the convergence speed if the algorithm does not converge after many iterations. The effectiveness of the proposed algorithm is evaluated by classic Hadoop programmes of cloud computing. The experimental results indicate that the proposed algorithm can reduce a great number of test cases for path coverage, compared with other metaheuristic algorithms for automated test data generation.

This paper discusses a new variant of Henry Gas Solubility Optimization (HGSO) Algorithm, called Hybrid HGSO (HHGSO). Unlike its predecessor, HHGSO allows multiple clusters serving different individual meta-heuristic algorithms (i.e., with its own defined parameters and local best) to coexist within the same population. Exploiting the dynamic cluster-to-algorithm mapping via penalized and reward model with adaptive switching factor, HHGSO offers a novel approach for meta-heuristic hybridization consisting of Jaya Algorithm, Sooty Tern Optimization Algorithm, Butterfly Optimization Algorithm, and Owl Search Algorithm, respectively. The acquired results from the selected two case studies (i.e., involving team formation problem and combinatorial test suite generation) indicate that the hybridization has notably improved the performance of HGSO and gives superior performance against other competing meta-heuristic and hyper-heuristic algorithms.

Companies are looking for effective strategies to improve warehouse performance quality due to customers dissatisfaction of service. The order picking process is one of the main warehouse management strategies. As the inventory of stored items and the number of orders increased, the picking process and response time became more important. Effective coordination between order batching and order picking process is essential to improve the efficiency of the warehouse management system. In this paper, a novel Mixed-Integer Nonlinear Programming (MINLP) model for on-line order batching is proposed for improving the warehouse performance, which in turn results in the reduction of the response and idle times. The proposed method takes aim at the investigation of order classification for the first time in the picker-to-part system as a manual picking system and an online order batching system, with the intent of minimizing the turnover time and idle time. Besides, an order batching model in a blocked warehouse using a zoning system is proposed which is called Online Order Batching in Blocked Warehouse with One Picker for each Block (OOBBWOPB). The mentioned model is solved by two algorithms: Artificial Bee-Colony (ABC) algorithm and Ant-Colony (ACO) algorithm. Two numerical case studies are defined and analyzed using MATLAB software. According to the results compared with the results of Zhang et al. (2017) the proposed model shows better performance and the average customer order response time is significantly reduced (2017) and the ACO yields better results than ABC.

ABSTRACT This research provides a comprehensive evaluation of seven emergent meta‐heuristic algorithms, including flying fox optimization (FFO), Giza pyramids construction (GPC), Harris Hawks optimizer (HHO), red deer algorithm (RDA), whale optimization algorithm (WOA), mayfly optimization algorithm (MOA), and stochastic paint optimizer (SPO) applied to the vehicle routing problem (VRP). The algorithms were implemented in MATLAB and assessed based on solution quality, execution time, and convergence rate across small, medium, and large‐scale problems. The evaluation revealed significant performance variations among these algorithms. WOA consistently achieved top ranks in small and medium‐scale problems, demonstrating its robustness and efficiency. In contrast, GPC excelled in large‐scale problems, outperforming other algorithms in handling complex and extensive datasets. SPO, however, consistently ranked lowest across all scales, indicating its limited effectiveness for VRP under the tested conditions. The study employed the Shannon Entropy method for weighting the evaluation criteria and a multi‐criteria decision‐making method for the final ranking of the algorithms, providing a structured and comprehensive assessment approach. The findings suggest that WOA is the most effective algorithm, offering reliable and high‐quality solutions with efficient execution times and convergence rates, while SPO requires significant enhancements. These insights are valuable for practitioners and managers in logistics and supply chain management, guiding the selection of appropriate algorithms based on problem scale. The research also opens avenues for future work, including the refinement of lower‐performing algorithms, comprehensive testing with broader datasets, advanced parameter optimization, and exploration of algorithm applicability in other domains, such as scheduling and resource allocation. This study not only benchmarks the performance of emerging meta‐heuristic algorithms on VRP but also lays a foundation for future advancements in optimization techniques. This research offers a comprehensive approach to the sustainable design of supply chains, providing insights into cost‐effective, low‐emission fuel production pathways for the aviation sector.

Yellow Saddle Goatfish Algorithm (YSGA) is an optimization model inspired by the hunting behavior of yellow saddle goatfish which emulates their collaborative behaviors with chaser fish and blocker fish. To improve the global convergence, chaotic maps have been combined with YSGA in this paper. Chaotic is a nonlinear deterministic system that displays complex, noisy‐like, and unpredictable behavior. Due to its non‐repetitive nature, an overall search can be carried out at a higher speed. The proposed algorithm is based on the excellence of the chaotic searching using a multi‐chaotic approach and the YSGA optimization, which has been applied to 68 benchmark functions. The results of the proposed Multi‐Chaotic Yellow Saddle Goatfish algorithm are compared with YSGA and also with nine other states of art meta‐heuristic algorithms. The results show that the proposed algorithm improves the performance of the YSGA algorithm. In this paper, chaotic maps (namely 10) have been combined using a multi‐chaotic approach with Yellow Saddle Goatfish. The results are compared with other metaheuristic techniques for benchmark functions. The quantitative comparison shows that the proposed approach provides a significant improvement in output.

In social network analysis, community detection is one of the significant tasks to study the structure and characteristics of the networks. In recent years, several intelligent and meta-heuristic algorithms have been presented for community detection in complex social networks, among them label propagation algorithm (LPA) is one of the fastest algorithms for discovering community structures. However, due to the randomness of the LPA, its performance is not suitable for the general purpose of network analysis. In this study, the authors propose an improved version of the label propagation (called AntLP) algorithm using similarity indices and ant colony optimisation (ACO). The AntLP consists of two steps: in the first step, the algorithm assigns weights for edges of the input network using several similarity indices, and in the second step, the AntLP using ACO tries to propagate labels and optimise modularity measure by grouping similar vertices in each community based on the local similarities among the vertices of the network. In order to study the performance of the AntLP, several experiments are conducted on some well-known social network datasets. Experimental simulations demonstrated that the AntLP is better than some community detection algorithms for social networks in terms of modularity, normalised mutual information and running time.

This study proposes a bald eagle search (BES) algorithm, which is a novel, nature-inspired meta-heuristic optimisation algorithm that mimics the hunting strategy or intelligent social behaviour of bald eagles as they search for fish. Hunting by BES is divided into three stages. In the first stage (selecting space), an eagle selects the space with the most number of prey. In the second stage (searching in space), the eagle moves inside the selected space to search for prey. In the third stage (swooping), the eagle swings from the best position identified in the second stage and determines the best point to hunt. Swooping starts from the best point and all other movements are directed towards this point. BES is tested by adopting a three-part evaluation methodology that (1) describes the benchmarking of the optimisation problem to evaluate the algorithm performance, (2) compares the algorithm performance with that of other intelligent computation techniques and parameter settings and (3) evaluates the algorithm based on mean, standard deviation, best point and Wilcoxon signed-rank test statistic of the function values. Optimisation results and discussion confirm that the BES algorithm competes well with advanced meta-heuristic algorithms and conventional methods.

The advent of the intelligent information era has witnessed a proliferation of complex optimization problems across various disciplines. Although existing meta-heuristic algorithms have demonstrated efficacy in many scenarios, they still struggle with certain challenges such as premature convergence, insufficient exploration, and lack of robustness in high-dimensional, nonconvex search spaces. These limitations underscore the need for novel optimization techniques that can better balance exploration and exploitation while maintaining computational efficiency. In response to this need, we propose the Artificial Lemming Algorithm (ALA), a bio-inspired metaheuristic that mathematically models four distinct behaviors of lemmings in nature: long-distance migration, digging holes, foraging, and evading predators. Specifically, the long-distance migration and burrow digging behaviors are dedicated to highly exploring the search domain, whereas the foraging and evading predators behaviors provide exploitation during the optimization process. In addition, ALA incorporates an energy-decreasing mechanism that enables dynamic adjustments to the balance between exploration and exploitation, thereby enhancing its ability to evade local optima and converge to global solutions more robustly. To thoroughly verify the effectiveness of the proposed method, ALA is compared with 17 other state-of-the-art meta-heuristic algorithms on the IEEE CEC2017 benchmark test suite and the IEEE CEC2022 benchmark test suite. The experimental results indicate that ALA has reliable comprehensive optimization performance and can achieve superior solution accuracy, convergence speed, and stability in most test cases. For the 29 10-, 30-, 50-, and 100-dimensional CEC2017 functions, ALA obtains the lowest Friedman average ranking values among all competitor methods, which are 1.7241, 2.1034, 2.7241, and 2.9310, respectively, and for the 12 CEC2022 functions, ALA again wins the optimal Friedman average ranking of 2.1667. Finally, to further evaluate its applicability, ALA is implemented to address a series of optimization cases, including constrained engineering design, photovoltaic (PV) model parameter identification, and fractional-order proportional-differential-integral (FOPID) controller gain tuning. Our findings highlight the competitive edge and potential of ALA for real-world engineering applications. The source code of ALA is publicly available at https://github.com/StevenShaw98/Artificial-Lemming-Algorithm.