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Artificial lemming algorithm: a novel bionic meta-heuristic technique for solving real-world engineering optimization problems

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第3期58卷 1-113页

作者： Xiao, Yaning Cui, Hao Abu Khurma, Ruba Castillo, Pedro A. Southern Univ Sci & Technol Ctr Control Sci & Technol Shenzhen 518055 Peoples R China Dalian Univ Technol Inst Adv Control Technol Dalian 116024 Peoples R China Al Balqa Appl Univ Al Huson Univ Coll Informat Technol Dept Irbid 19117 Jordan Univ Granada Dept Comp Engn Automat & Robot Granada 18071 Spain

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 ran

关键词： Artificial lemming algorithm (ALA) meta-heuristic algorithm Swarm intelligence Global optimization Engineering problems

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Reservoir evaporation prediction with integrated development of deep neural network models and meta-heuristic algorithms (Case study: Dez Dam)

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EARTH SCIENCE INFORMATICS 2025年第2期18卷 1-23页

作者： Farzad, Reza Sharafati, Ahmad Ahmadi, Farshad Hosseini, Seyed Abbas Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran Iran Shahid Chamran Univ Ahvaz Dept Hydrol & Water Resources Engn Ahvaz Iran

To manage water resources, reservoirs, and basins, the accurate prediction of evaporation is one of the most significant and necessary processes for the sustainability and preservation of water reserves. On the other hand, climate change can affect the evaporation parameter, so the prediction of evaporation from the reservoirs of dams is vital for managing water resources. development of deep models and their integration to optimization algorithms for modeling and climate variable prediction has drawn a lot of attention in recent years .The present study integrated Multi-Verse Optimizer (MVO), Black Hole (BH), and Marine Predator (MPA) meta-heuristic algorithms with a Long-Term Memory (LSTM) deep neural network model to develop models to forecast the reservoir's evaporation rate at Dez Dam, Khuzestan Province, Iran. The data series has been prepared in two formats: satellite data and modified data from 1981 to 2018 for 37 years. This study also used the meteorological parameters of surface pressure, maximum, minimum, and average temperatures, the speed of wind, relative humidity, and evaporation. Different scenarios have been investigated to examine the effectiveness of various input parameters on the prediction accuracy of the LSTM, LSTM-MPA, LSTM-MVO, and LSTM-BH models. Predicted results of the models showed that the integrated LSTM-MPA-Satellite model with evaluation criteria of RMSE, MAE, KGE, and WI of 64.3540, 48.7079, 0.8164, and 0.8266, respectively, outperforms other meta-heuristic models. The LSTM-BH-Correct is also introduced as the second-best model with almost similar results of 64.4992, 48.8199, 0.8146, and 0.8262. temperature, Relative humidity, and wind speed are the main variables influencing evaporation. Additionally, the candlestick chart shows that the variables have a more regular state when using meta-heuristic techniques as opposed to using the LSTM model alone, indicating a better fit to the data and improved model optimization. Based on the

关键词： Evaporation prediction Dam reservoir LSTM deep neural network meta-heuristic algorithm

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meta-heuristic algorithms for channel scheduling problem in wireless sensor networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2012年第4期25卷 427-446页

作者： Jang, Kil-Woong Korea Maritime Univ Dept Data Informat Pusan 606791 South Korea

The channel scheduling problem is to decide how to commit channels for transmitting data between nodes in wireless networks. This problem is one of the most important problems in wireless sensor networks. In this problem, we aim to obtain a near-optimal solution with the minimal energy consumption within a reasonable time. As the number of nodes increases in the network, however, the amount of calculation for finding the solution would be too high. It can be difficult to obtain an optimal solution in a reasonable execution time because this problem is NP-hard. Therefore, most of the recent studies for such problems seem to focus on heuristic algorithms. In this paper, we propose efficient channel scheduling algorithms to obtain a near-optimal solution on the basis of three meta-heuristic algorithms;the genetic algorithm, the Tabu search, and the simulated annealing. In order to make a search more efficient, we propose some neighborhood generating methods for the proposed algorithms. We evaluate the performance of the proposed algorithms through some experiments in terms of energy consumption and algorithm execution time. The experimental results show that the proposed algorithms are efficient for solving the channel scheduling problem in wireless sensor networks. Copyright (c) 2011 John Wiley & Sons, Ltd.

关键词： meta-heuristic algorithm genetic algorithm Tabu search simulated annealing channel scheduling problem wireless sensor networks

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Novel meta-heuristic bald eagle search optimisation algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2020年第3期53卷 2237-2264页

作者： Alsattar, H. A. Zaidan, A. A. Zaidan, B. B. Univ Pendidikan Sultan Idris Fac Arts Comp & Creat Ind Dept Comp Tanjung Malim Malaysia Al Rafidain Univ Coll Baghdad Iraq

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.

关键词： Bald eagle behaviour meta-heuristic algorithm Optimisation Unconstrained benchmark problem

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Cricket chirping algorithm: an efficient meta-heuristic for numerical function optimisation

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2018年第2期16卷 162-172页

作者： Deuri, Jonti Sathya, S. Siva Pondicherry Univ Dept Comp Sci Pondicherry 605014 India

Nature-inspired meta-heuristic algorithms have proved to be very powerful in solving complex optimisation problems in recent times. The literature reports several inspirations from nature, exploited to solve computational problems. This paper is yet another step in the journey towards the utilisation of natural phenomena for seeking solutions to complex optimisation problems. In this paper, a new meta-heuristic algorithm based on the chirping behaviour of crickets is formulated to solve optimisation problems. It is validated against various benchmark test functions and then compared with popular state-of-the-art optimisation algorithms like genetic algorithm, particle swarm optimisation, bat algorithm, artificial bee colony algorithm and cuckoo search algorithm for performance efficiency. Simulation results show that the proposed algorithm has outperformed its counterparts in terms of speed and accuracy. The implication of the results and suggestions for further research are also discussed.

关键词： optimisation meta-heuristic algorithm numerical function cuckoo search artificial bee colony ABC particle swarm optimisation PSO genetic algorithm calling chirp aggressive chirp cricket chirping algorithm CCA

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Binary Chimp Optimization algorithm (BChOA): a New Binary meta-heuristic for Solving Optimization Problems

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COGNITIVE COMPUTATION 2021年第5期13卷 1297-1316页

作者： Wang, Jianhao Khishe, Mohammad Kaveh, Mehrdad Mohammadi, Hassan Shenma Cord Fabr Dev Co Ltd Pingdingshan 467000 Henan Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr *** Iran KN Toosi Univ Technol Dept Geodesy & Geomat Tehran *** Iran

Chimp optimization algorithm (ChOA) is a newly proposed meta-heuristic algorithm inspired by chimps' individual intelligence and sexual motivation in their group hunting. The preferable performance of ChOA has been approved among other well-known meta-heuristic algorithms. However, its continuous nature makes it unsuitable for solving binary problems. Therefore, this paper proposes a novel binary version of ChOA and attempts to prove that the transfer function is the most important part of binary algorithms. Therefore, four S-shaped and V-shaped transfer functions, as well as a novel binary approach, have been utilized to investigate the efficiency of binary ChOAs (BChOA) in terms of convergence speed and local minima avoidance. In this regard, forty-three unimodal, multimodal, and composite optimization functions and ten IEEE CEC06-2019 benchmark functions were utilized to evaluate the efficiency of BChOAs. Furthermore, to validate the performance of BChOAs, four newly proposed binary optimization algorithms were compared with eighteen novel state-of-the-art algorithms. The results indicate that both the novel binary approach and V-shaped transfer functions improve the efficiency of BChOAs in a statistically significant way.

关键词： Chimp optimization algorithm (ChOA) Binary problems meta-heuristic algorithm Benchmark problems Optimization

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A meta-heuristic density-based subspace clustering algorithm for high-dimensional data

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SOFT COMPUTING 2021年第15期25卷 10237-10256页

作者： Agarwal, Parul Mehta, Shikha Abraham, Ajith Jaypee Inst Informat Technol Dept Comp Sci & Informat Technol Sect 62 Noida India Sci Network Innovat & Res Excellence Auburn Machine Intelligence Res Labs MIR Labs Washington DC USA

Subspace clustering is one of the efficient techniques for determining the clusters in different subsets of dimensions. Ideally, these techniques should find all possible non-redundant clusters in which the data point participates. Unfortunately, existing hard subspace clustering algorithms fail to satisfy this property. Additionally, with the increase in dimensions of data, classical subspace algorithms become inefficient. This work presents a new density-based subspace clustering algorithm (S_FAD) to overcome the drawbacks of classical algorithms. S_FAD is based on a bottom-up approach and finds subspace clusters of varied density using different parameters of the DBSCAN algorithm. The algorithm optimizes parameters of the DBCAN algorithm through a hybrid meta-heuristic algorithm and uses hashing concepts to discover all non-redundant subspace clusters. The efficacy of S_FAD is evaluated against various existing subspace clustering algorithms on artificial and real datasets in terms of F_Score and rand_index. Performance is assessed based on three parameters: average ranking, SRR ranking, and scalability on varied dimensions. Statistical analysis is performed through the Wilcoxon signed-rank test. Results reveal that S_FAD performs considerably better on the majority of the datasets and scales well up to 6400 dimensions on the actual dataset.

关键词： Subspace clustering algorithm High dimensional clustering meta-heuristic algorithm Wilcoxon signed-rank test Success rate ratio ranking Average ranking

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Dragon Boat Optimization: A meta-heuristic for Intelligent Systems

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EXPERT SYSTEMS 2025年第2期42卷

作者： Li, Xiang Lan, Long Lahza, Husam Yang, Shaowu Wang, Shuihua Yang, Wenjing Liu, Hengzhu Zhang, Yudong Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Dept Intelligent Data Sci Changsha Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah Saudi Arabia Xian Jiaotong Liverpool Univ Dept Biol Sci Suzhou Jiangsu Peoples R China Univ Liverpool Dept Math Sci Liverpool England Natl Univ Def Technol Key Lab Adv Microprocessor Chips & Syst Changsha Peoples R China Univ Leicester Sch Comp & Math Sci Leicester England

Dragon boat racing, a popular aquatic folklore team sport, is traditionally held during the Dragon Boat Festival. Inspired by this event, we propose a novel human-based meta-heuristic algorithm called dragon boat optimization (DBO) in this paper. It models the unique behaviours of each crew member on the dragon boat during the race by introducing social psychology mechanisms (social loafing, social incentive). Throughout this process, the focus is on the interaction and collaboration among the crew members, as well as their decision-making in various situations. During each iteration, DBO implements different state updating strategies. By accurately modelling the crew's behaviour and employing adaptive state update strategies, DBO consistently achieves high optimization performance, as validated by comprehensive testing on 29 benchmark functions and 2 structural design problems. Experimental results indicate that DBO outperforms 7 and 16 state-of-the-art meta-heuristic algorithms across these test functions and problems, respectively.

关键词： dragon boat racing human-based algorithm meta-heuristic algorithm optimization algorithm structural optimization

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meta-heuristic algorithms as tools for hydrological science

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GEOSCIENCE LETTERS 2014年第1期1卷 1-7页

作者： Yoo, Do Guen Kim, Joong Hoon Korea Univ Res Ctr Disaster Prevent Sci & Technol Seoul 136713 South Korea Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea

In this paper, meta-heuristic optimization techniques are introduced and their applications to water resources engineering, particularly in hydrological science are introduced. In recent years, meta-heuristic optimization techniques have been introduced that can overcome the problems inherent in iterative simulations. These methods are able to find good solutions and require limited computation time and memory use without requiring complex derivatives. Simulation-based meta-heuristic methods such as Genetic algorithms (GAs) and Harmony Search (HS) have powerful searching abilities, which can occasionally overcome the several drawbacks of traditional mathematical methods. For example, HS algorithms can be conceptualized from a musical performance process and used to achieve better harmony;such optimization algorithms seek a near global optimum determined by the value of an objective function, providing a more robust determination of musical performance than can be achieved through typical aesthetic estimation. In this paper, meta-heuristic algorithms and their applications (focus on GAs and HS) in hydrological science are discussed by subject, including a review of existing literature in the field. Then, recent trends in optimization are presented and a relatively new technique such as Smallest Small World Cellular Harmony Search (SSWCHS) is briefly introduced, with a summary of promising results obtained in previous studies. As a result, previous studies have demonstrated that meta-heuristic algorithms are effective tools for the development of hydrological models and the management of water resources.

关键词： meta-heuristic algorithm Harmony search algorithm Hydrological sciences

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meta-heuristic Ant Colony algorithm for Multi-Tasking Assignment on Collaborative AUVs

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2015年第3期8卷 135-143页

作者： Li, Jian Jun Zhang, Ru Bo Yang, Yu Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China Dalian Nationalities Univ Coll Electromech & Informat Engn Liaoning Dalian 116600 Peoples R China Harbin Univ Commerce Sch Comp & Informat Engn Harbin 150028 Peoples R China

Multiple Unmanned Underwater Vehicles Is a typical combinatorial optimization problem, to achieve multiple AUV, coordinated, collaborative tasks to complete complex jobs subsea. Through analyzing the ant colony optimization algorithm, the paper proposed An meta-heuristic ant colony optimization algorithm the Implementation to solve the multi AUVs to achieve the task allocation problem, and had simulation test based on the consolidated analyze the advantages of multiple unmanned underwater vehicle. results show that the ant colony optimization algorithms in solving multi-task allocation problem of multiple unmanned underwater vehicle showed a good performance.

关键词： Ant Colony Optimization Multi-AUVs Task Assignment meta-heuristic algorithm

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