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Metaheuristic-Driven Optimization Algorithms: Methods and Applications

Submission Deadline: 31 December 2024 View: 2184 Submit to Special Issue

Guest Editors

Prof. Mohammad Shokouhifar, Shahid Beheshti University, Iran
Prof. Frank Werner, Otto-Von-Guericke-University, Germany

Summary

The exploration of optimization problems spans numerous scientific and engineering domains, sparking a growing need for refined optimization techniques. While exact search methods consistently yield the optimal solution, their feasibility diminishes when tackling real-world NP-complete/hard problems due to time constraints. This emphasizes the significance of metaheuristic algorithms that offer near-optimal solutions within a reasonable timeframe while maintaining a beneficial balance between complexity and efficiency. One primary advantage of metaheuristics is their adaptability as general-purpose, problem-independent algorithms. Unlike exact algorithms, metaheuristics depend less on mathematical models and thrive on the "trial-and-error" principle in searching for optimal solutions, excelling in evading local optima across various problems.


This Special Issue invites academia and industry experts to showcase the latest advancements in metaheuristic algorithms and their practical applications in solving real-world optimization problems across various engineering fields including computer, electrical, mechanical, chemical, biomedical, civil, and industrial engineering. Potential topics include, but are not restricted to:

  • Development of new single-solution metaheuristic algorithms

  • Advancements in population-based metaheuristic algorithms

  • Evolution of multi-objective metaheuristic algorithms

  • Ensemble knowledge-based heuristic-metaheuristic algorithms

  • Fusion of fuzzy systems and metaheuristics for optimization

  • Hybridization of machine learning and metaheuristics for optimization

  • Metaheuristic-driven heuristics tailored for Just-in-Time (JIT) optimization

  • Applications in signal, image, and video processing   

  • Applications in healthcare big data analytics

  • Applications in scheduling and resource allocation problems  

  • Applications in optimal design of renewable energy systems

  • Applications in smart manufacturing and supply chain logistics

  • Applications in business intelligence and financial management

  • Applications in Internet-of-Things (IoT) and smart cities

  • Applications in computer and communication networks


Keywords

Optimization, Engineering Problems, Metaheuristics, Evolutionary Algorithms, Swarm Intelligence, Multi-Objective Optimization, Ensemble Metaheuristic Algorithms

Published Papers


  • Open Access

    ARTICLE

    Collaborative Trajectory Planning for Stereoscopic Agricultural Multi-UAVs Driven by the Aquila Optimizer

    Xinyu Liu, Longfei Wang, Yuxin Ma, Peng Shao
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058294
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract Stereoscopic agriculture, as an advanced method of agricultural production, poses new challenges for multi-task trajectory planning of unmanned aerial vehicles (UAVs). To address the need for UAVs to perform multi-task trajectory planning in stereoscopic agriculture, a multi-task trajectory planning model and algorithm (IEP-AO) that synthesizes flight safety and flight efficiency is proposed. Based on the requirements of stereoscopic agricultural geomorphological features and operational characteristics, the multi-task trajectory planning model is ensured by constructing targeted constraints at five aspects, including the path, slope, altitude, corner, energy and obstacle threat, to improve the effectiveness of the trajectory… More >

  • Open Access

    ARTICLE

    Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence

    Ting Cai, Chun Ye, Zhiwei Ye, Ziyuan Chen, Mengqing Mei, Haichao Zhang, Wanfang Bai, Peng Zhang
    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1157-1175, 2024, DOI:10.32604/cmc.2024.055080
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract The world produces vast quantities of high-dimensional multi-semantic data. However, extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging. Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features. The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection, because of its simplicity, efficiency, and similarity to reinforcement learning. Nevertheless, existing methods do not consider crucial correlation information, such as dynamic redundancy and label correlation. To tackle these concerns, the paper proposes a More >

  • Open Access

    ARTICLE

    Dynamical Artificial Bee Colony for Energy-Efficient Unrelated Parallel Machine Scheduling with Additional Resources and Maintenance

    Yizhuo Zhu, Shaosi He, Deming Lei
    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 843-866, 2024, DOI:10.32604/cmc.2024.054473
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract Unrelated parallel machine scheduling problem (UPMSP) is a typical scheduling one and UPMSP with various real-life constraints such as additional resources has been widely studied; however, UPMSP with additional resources, maintenance, and energy-related objectives is seldom investigated. The Artificial Bee Colony (ABC) algorithm has been successfully applied to various production scheduling problems and demonstrates potential search advantages in solving UPMSP with additional resources, among other factors. In this study, an energy-efficient UPMSP with additional resources and maintenance is considered. A dynamical artificial bee colony (DABC) algorithm is presented to minimize makespan and total energy consumption… More >

  • Open Access

    ARTICLE

    Metaheuristic-Driven Two-Stage Ensemble Deep Learning for Lung/Colon Cancer Classification

    Pouyan Razmjouei, Elaheh Moharamkhani, Mohamad Hasanvand, Maryam Daneshfar, Mohammad Shokouhifar
    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3855-3880, 2024, DOI:10.32604/cmc.2024.054460
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract This study investigates the application of deep learning, ensemble learning, metaheuristic optimization, and image processing techniques for detecting lung and colon cancers, aiming to enhance treatment efficacy and improve survival rates. We introduce a metaheuristic-driven two-stage ensemble deep learning model for efficient lung/colon cancer classification. The diagnosis of lung and colon cancers is attempted using several unique indicators by different versions of deep Convolutional Neural Networks (CNNs) in feature extraction and model constructions, and utilizing the power of various Machine Learning (ML) algorithms for final classification. Specifically, we consider different scenarios consisting of two-class colon… More >

  • Open Access

    ARTICLE

    Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

    Tareq Hamadneh, Khalid Kaabneh, Ibraheem AbuFalahah, Gulnara Bektemyssova, Galymzhan Shaikemelev, Dauren Umutkulov, Sayan Omarov, Zeinab Monrazeri, Frank Werner, Mohammad Dehghani
    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2721-2741, 2024, DOI:10.32604/cmc.2024.054317
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization (MFO), inspired by the unique behaviors observed in magnificent frigatebirds in their natural habitats. The foundation of MFO is based on the kleptoparasitic behavior of these birds, where they steal prey from other seabirds. In this process, a magnificent frigatebird targets a food-carrying seabird, aggressively pecking at it until the seabird drops its prey. The frigatebird then swiftly dives to capture the abandoned prey before it falls into the water. The theoretical framework of MFO is thoroughly detailed and mathematically represented, mimicking the frigatebird’s… More >

  • Open Access

    ARTICLE

    Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

    Tareq Hamadneh, Khalid Kaabneh, Omar Alssayed, Gulnara Bektemyssova, Galymzhan Shaikemelev, Dauren Umutkulov, Zoubida Benmamoun, Zeinab Monrazeri, Mohammad Dehghani
    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2005-2030, 2024, DOI:10.32604/cmc.2024.052401
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Management (SCM), which is characterized by elevated risks due to inadequate accountability and transparency. To address these challenges and improve operations in green manufacturing, optimization algorithms play a crucial role in supporting decision-making processes. In this study, we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms, notably the Stork Optimization Algorithm (SOA). The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature. The theoretical framework of SOA is… More >

  • Open Access

    ARTICLE

    Wild Gibbon Optimization Algorithm

    Jia Guo, Jin Wang, Ke Yan, Qiankun Zuo, Ruiheng Li, Zhou He, Dong Wang, Yuji Sato
    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1203-1233, 2024, DOI:10.32604/cmc.2024.051336
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract Complex optimization problems hold broad significance across numerous fields and applications. However, as the dimensionality of such problems increases, issues like the curse of dimensionality and local optima trapping also arise. To address these challenges, this paper proposes a novel Wild Gibbon Optimization Algorithm (WGOA) based on an analysis of wild gibbon population behavior. WGOA comprises two strategies: community search and community competition. The community search strategy facilitates information exchange between two gibbon families, generating multiple candidate solutions to enhance algorithm diversity. Meanwhile, the community competition strategy reselects leaders for the population after each iteration, More >

  • Open Access

    ARTICLE

    Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

    Ibraheem Abu Falahah, Osama Al-Baik, Saleh Alomari, Gulnara Bektemyssova, Saikat Gochhait, Irina Leonova, Om Parkash Malik, Frank Werner, Mohammad Dehghani
    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3631-3678, 2024, DOI:10.32604/cmc.2024.053189
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard’s sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard’s retreat to the treetops after feeding. To assess FLO’s efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two… More >

  • Open Access

    ARTICLE

    An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

    Chen Zhang, Liming Liu, Yufei Yang, Yu Sun, Jiaxu Ning, Yu Zhang, Changsheng Zhang, Ying Guo
    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5201-5223, 2024, DOI:10.32604/cmc.2024.050863
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract The flying foxes optimization (FFO) algorithm, as a newly introduced metaheuristic algorithm, is inspired by the survival tactics of flying foxes in heat wave environments. FFO preferentially selects the best-performing individuals. This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area. To address this issue, the paper introduces an opposition-based learning-based search mechanism for FFO algorithm (IFFO). Firstly, this paper introduces niching techniques to improve the survival list method, which not only focuses on the adaptability of individuals but also considers the population’s crowding degree More >

  • Open Access

    ARTICLE

    A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

    Chao-Lung Yang, Melkamu Mengistnew Teshome, Yu-Zhen Yeh, Tamrat Yifter Meles
    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3519-3547, 2024, DOI:10.32604/cmc.2024.048462
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract In this study, we introduce a novel multi-objective optimization model tailored for modern manufacturing, aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance. Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel, addressing a crucial gap in the integration of maintenance personnel dispatching and station selection. Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness. The core of our methodology is the NSGA III+ Dispatch, an advanced adaptation… More >

  • Open Access

    ARTICLE

    A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

    Deming Lei, Heen Li
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1855-1874, 2024, DOI:10.32604/cmc.2024.049480
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract This study focuses on the scheduling problem of unrelated parallel batch processing machines (BPM) with release times, a scenario derived from the moulding process in a foundry. In this process, a batch is initially formed, placed in a sandbox, and then the sandbox is positioned on a BPM for moulding. The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume. To minimize the makespan, a new cooperated imperialist competitive algorithm (CICA) is introduced. In CICA, the number of empires is not a parameter, and four empires are maintained More >

  • Open Access

    ARTICLE

    A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

    Farhan Ullah, Xuexia Zhang, Mansoor Khan, Muhammad Abid, Abdullah Mohamed
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3373-3395, 2024, DOI:10.32604/cmc.2024.048656
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows. Traditional approaches frequently struggle with complex data and non-linear connections. This article presents a novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts. The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-Era Retrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms using in-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model, while a temporal convolutional network handles time-series complexities and data… More >

  • Open Access

    ARTICLE

    An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

    Deming Lei, Surui Duan, Mingbo Li, Jing Wang
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 47-63, 2024, DOI:10.32604/cmc.2024.049481
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract Bottleneck stage and reentrance often exist in real-life manufacturing processes; however, the previous research rarely addresses these two processing conditions in a scheduling problem. In this study, a reentrant hybrid flow shop scheduling problem (RHFSP) with a bottleneck stage is considered, and an elite-class teaching-learning-based optimization (ETLBO) algorithm is proposed to minimize maximum completion time. To produce high-quality solutions, teachers are divided into formal ones and substitute ones, and multiple classes are formed. The teacher phase is composed of teacher competition and teacher teaching. The learner phase is replaced with a reinforcement search of the More >

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