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Jihong Wan et al.
Summary: This paper proposes a novel interactive and complementary feature selection approach based on a fuzzy multineighborhood rough set model. The approach effectively improves the classification performance of feature subsets while reducing the dimension of feature space.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
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Nutan Saha et al.
Summary: The Whale Optimization Algorithm (WOA) imitates the social behavior of humpback whales and the CamWOA modifies this algorithm by incorporating cosine function and correction factors to achieve better performance and efficiency in benchmark function testing and a control engineering problem.
COMPUTATIONAL INTELLIGENCE
(2022)
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Computer Science, Artificial Intelligence
Sanjoy Chakraborty et al.
Summary: This study proposes an improved Whale Optimization Algorithm (ImWOA) with increased solution diversity to enhance problem-solving ability. By altering the random solution selection process and incorporating the whale's cooperative hunting strategy, the algorithm's exploration and exploitation capabilities are improved. Experimental results demonstrate that ImWOA outperforms other algorithms in terms of problem-solving ability.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Environmental Sciences
Fei Li et al.
Summary: The optimization control scheme based on dynamic multi-objective immune system has shown effectiveness in resolving conflicting performance indicators in wastewater treatment plants (WWTPs). By dividing the control process into dynamic and tracking control layers, adapting energy consumption and effluent quality models, and utilizing an adaptive dynamic immune optimization algorithm, the method successfully optimized complex and conflicting performance indicators. The competitive advantage of this method in control effectiveness was demonstrated through evaluation on a benchmark simulation platform.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
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Engineering, Mechanical
Girish Kumar et al.
Summary: In this work, a multi-plane system is proposed as an approach to solve the collision avoidance problem for a swarm of unmanned aerial vehicles. The approach is aimed at minimizing the impact on the searching algorithm used for search and rescue missions. The study compares well-established algorithms such as particle swarm optimization with novel algorithms like layered search and rescue, spiral search, and fish-inspired task allocation. The simulations and statistical analysis show that the proposed collision avoidance algorithm significantly reduces collisions without affecting the convergence of the optimization algorithm.
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
(2022)
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Computer Science, Interdisciplinary Applications
Bhaskar Nautiyal et al.
Summary: Different versions of the SSA employing various mutation schemes were proposed in this study to enhance the optimization process for complex problems. The Gaussian mutation proved to be particularly effective in boosting the algorithm's exploration and exploitation abilities.
ENGINEERING WITH COMPUTERS
(2022)
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Computer Science, Artificial Intelligence
Jia Cai et al.
Summary: Biologically inspired computing is a method that uses elegantly modeled techniques motivated by the behaviors of creatures in nature to solve real-world problems. This paper investigates an improved Harris hawks optimizer (HHO) by introducing the grey wolf optimizer (GWO) and improving the balance between exploration and exploitation. The proposed approach combines different cognitive hunting behaviors of Harris' hawks and grey wolf packs and selects the best solutions through iterations. Experimental results demonstrate the effectiveness and efficiency of the proposed method.
COGNITIVE COMPUTATION
(2022)
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Computer Science, Artificial Intelligence
Amirreza Naderipour et al.
Summary: The paper presents a new method for reconfiguring unbalanced distribution networks using a fuzzy multi-criteria approach and improved corona-virus herd immunity optimizer algorithm. The results demonstrate that the approach can optimize the network configuration to minimize power loss, voltage unbalance, voltage sag, and energy not supplied by the customers under different loading conditions. The performance of the proposed algorithm surpasses other well-known algorithms in terms of convergence tolerance and accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
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Computer Science, Artificial Intelligence
Yi Chen et al.
Summary: In this study, a multi-strategy-driven shuffled frog leaping algorithm with horizontal and vertical crossover search (HVSFLA) is proposed for medical image segmentation. The algorithm achieves a better balance between diversification and intensification through horizontal and vertical crossover search. Experimental results demonstrate that HVSFLA outperforms other competing algorithms, showing great potential for medical image segmentation.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Thaer Thaher et al.
Summary: This paper proposes an efficient feature selection approach based on a Boolean variant of Particle Swarm Optimization (BPSO) boosted with Evolutionary Population Dynamics (EPD). The experimental results demonstrate the superiority of the proposed EPD-based feature selection approaches, especially the BPSO-TEPD variant when compared with conventional BPSO and other five EPD-based variants.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
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Computer Science, Artificial Intelligence
Mohamed Abdel-Basset et al.
Summary: In this paper, a new approach for multi-threshold color image segmentation using the Otsu method as an objective function is proposed, named HWOA, based on a hybrid of the whale optimization algorithm (WOA) and the local minima avoidance method (LMAM). Experimental results show that HWOA outperforms other algorithms in terms of PSNR, FSIM, and objective values, while being competitive in terms of SSIM.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Fatma A. Hashim et al.
Summary: In recent years, various metaheuristic algorithms have been introduced in engineering and scientific fields to solve real-life optimization problems. This study proposes a novel nature-inspired metaheuristic algorithm called Snake Optimizer (SO), which imitates the mating behavior of snakes to tackle different optimization tasks. Experimental results demonstrate the effectiveness and efficiency of SO compared to other algorithms in terms of exploration-exploitation balance and convergence speed.
KNOWLEDGE-BASED SYSTEMS
(2022)
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Computer Science, Artificial Intelligence
Mohammed Qaraad et al.
Summary: This paper proposes a novel hybrid meta-heuristic algorithm called SSA-FGWO based on the Salp swarm algorithm (SSA) and the Grey Wolf Algorithm (GWO). The experimental results show that SSA-FGWO significantly improves the convergence speed, precision, and global optimization capability compared to the basic SSA, GWO, and other algorithms.
NEURAL COMPUTING & APPLICATIONS
(2022)
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Mathematical & Computational Biology
Mustafa Tunay et al.
Summary: This paper introduces a new metaheuristic algorithm, the hypercube optimization search (HOS), for training multilayer perceptrons (MLP) in medical data classification. Experimental results demonstrate that MLP trained by HOS outperforms other comparative models in terms of mean square error, classification accuracy, and convergence rate.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2022)
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Computer Science, Interdisciplinary Applications
Ailiang Qi et al.
Summary: A new variant of the whale optimization algorithm, named LXMWOA, is proposed in this paper to enhance the performance of WOA by introducing Levy initialization strategy, directional crossover mechanism, and directional mutation mechanism. Experimental results show that LXMWOA outperforms its peers in both exploration and exploitation capabilities, suggesting great potential for solving engineering problems.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
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Computer Science, Information Systems
Ali E. Takieldeen et al.
Summary: Dipper throated optimization (DTO) algorithm, inspired by the hunting technique of dipper throated bird, is a novel and efficient metaheuristic algorithm. The experimental results demonstrate that DTO outperforms other algorithms in solving feature selection problems and has the potential to handle complex real-world situations.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
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Computer Science, Artificial Intelligence
Dong Zhao et al.
Summary: The research introduced improvements such as horizontal crossover search and vertical crossover search to enhance the solution quality and convergence speed of the ACOR algorithm, forming the improved CCACO algorithm. Experimental results demonstrate that CCACO shows superior convergence speed and solution quality in image segmentation.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
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Computer Science, Artificial Intelligence
Dong Zhao et al.
Summary: By enhancing the selection mechanism of the ACOR method and introducing random spare strategy and chaotic intensification strategy, the convergence speed and accuracy can be significantly improved, effectively avoiding local optima. Through a series of experiments, these improved methods demonstrate superior performance in problem-solving, and compared to other techniques, RCACO has a more reliable ability to step out of local optima.
KNOWLEDGE-BASED SYSTEMS
(2021)
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Computer Science, Artificial Intelligence
Ashish Kumar Tripathi et al.
Summary: In the era of Web 2.0, the growing data volume aids E-commerce websites in making better decisions. A map-reduce-based clustering recommendation system is proposed, utilizing a novel variant of the whale optimization algorithm to improve clustering efficiency.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Engineering, Multidisciplinary
Ahmed A. Ewees et al.
Summary: This paper proposes a modified salp swarm algorithm (SSAFA) to solve the unrelated parallel machine scheduling problem with sequence-dependent setup times. By using the operators of the firefly algorithm as a local search, the quality of the solution is improved. Evaluation outcomes confirm the competitive performance of SSAFA in various problem instances using different performance measures.
APPLIED MATHEMATICAL MODELLING
(2021)
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Computer Science, Artificial Intelligence
Sanjoy Chakraborty et al.
Summary: Differential evolution and its variants have been proven effective in evolutionary optimization techniques. The study introduces a new algorithm, SHADE-WOA, which combines SHADE with a modified Whale optimization algorithm, showing enhanced performance in solving real-world problems with reduced chances of local optima and stagnation.
APPLIED SOFT COMPUTING
(2021)
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Biology
Sanjoy Chakraborty et al.
Summary: The COVID-19 pandemic has had a significant impact on various aspects of human life, highlighting the importance of rapid diagnosis and treatment. This research focuses on developing a computational tool to improve diagnostic accuracy by enhancing the whale optimization method and evaluating its efficiency through population reduction.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Biology
Songwei Zhao et al.
Summary: This paper presents a novel approach to improve the Salp Swarm Algorithm (SSA), named EHSSA, which is applied to Multi-threshold image segmentation (MIS). By enhancing the global search capability of the algorithm, it successfully avoids local optimal drawbacks and proves its effectiveness and performance through experiments.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
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Computer Science, Artificial Intelligence
Jiaze Tu et al.
Summary: The study introduces an enhanced WOA method, EWOA, which combines a new communication mechanism and partial utilization of the BBO algorithm to improve the exploration ability, exploitation ability, and convergence speed of the algorithm.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
ChenYang Li et al.
Summary: The Harris Hawks Optimization (HHO) algorithm simulates the hunting process of Harris hawks and has a strong optimization effect, but is prone to premature convergence. To address this issue, two novel strategies were integrated into HHO, resulting in enhanced exploration and exploitation capabilities. The novel meta-heuristic algorithm called RLHHO outperformed traditional and advanced meta-heuristic algorithms in various test functions and demonstrated scalability in solving complex real-world problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
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Computer Science, Artificial Intelligence
Adel Got et al.
Summary: This paper introduces a novel hybrid filter-wrapper feature selection approach using whale optimization algorithm, which optimizes multiple objective functions simultaneously. Experimental results demonstrate the efficiency of the proposed algorithm on twelve benchmark datasets, showing its ability to obtain subsets with smaller number of features and excellent classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
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Computer Science, Information Systems
Sushmita Sharma et al.
Summary: A novel hybrid BOA algorithm, MPBOA, is proposed in this paper, combining the exploration and exploitation characteristics of BOA with mutualism and parasitism phases of the SOS algorithm. The algorithm shows satisfactory performance in terms of search behavior and convergence time on twenty-five benchmark functions.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
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Computer Science, Artificial Intelligence
Wu Deng et al.
Summary: The paper introduces an improved differential evolution algorithm NBOLDE with neighborhood mutation operators and opposition-based learning, which accelerates convergence speed and enhances optimization capabilities by proposing a new neighborhood strategy and implementing a new neighborhood mutation strategy.
Article
Computer Science, Information Systems
Ali Jameel Al-Mousawi
Summary: This comprehensive survey explores the role of swarm intelligence in wireless communication networks, focusing on network routing, quality of service, congestion, and security. The study examines the impact of swarm intelligence on three standard-based networks – IEEE 802.11, IEEE 802.16, and IEEE 802.20 – and presents graphical qualitative comparisons to demonstrate performance differences.
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Computer Science, Artificial Intelligence
Mohit Agarwal et al.
Summary: In distributed computing environments, task scheduling is critical to minimize overall completion time, and various methods have been applied. The proposed PSO algorithm based on opposition-based learning shows better performance in experiments compared to other algorithms.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Sanjoy Chakraborty et al.
Summary: The Whale Optimization Algorithm was developed based on the prey-catching characteristics of humpback whales and has been widely used in various disciplines due to its simplicity and efficiency. However, it has been found to have limitations in exploration ability, accuracy, and convergence in high-dimensional optimization problems. This study introduces a new variant with modifications to address these issues and enhance the algorithm's performance by balancing global and local search phases, modifying co-efficient vectors, and introducing random movement. The proposed algorithm demonstrates better performance on higher-dimensional problems compared to the basic Whale Optimization Algorithm and its variants.
KNOWLEDGE-BASED SYSTEMS
(2021)
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Mathematics, Interdisciplinary Applications
Xiaojia Ye et al.
Summary: The article introduces a novel modified Whale Optimization Algorithm (MWOA) that balances the exploration and exploitation of the algorithm by introducing the mutation strategy based on Levy flight and a local search mechanism of pattern search. This combination enhances the algorithm's global search capability and local optimization ability, making it more effective in parameter identification and optimization for solar cells and PV modules under diverse conditions.
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Computer Science, Interdisciplinary Applications
Sanjoy Chakraborty et al.
Summary: An enhanced Whale Optimization Algorithm (WOAmM) is proposed in this work to overcome premature convergence issues by modifying the mutualism phase, leading to more comprehensive exploration of the search space. The method demonstrates improved performance and superiority over other algorithms in testing.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
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Computer Science, Artificial Intelligence
Yutao Yang et al.
Summary: The research proposes a population-based optimization technique called Hunger Games Search (HGS), designed based on the hunger-driven activities and behavioral choices of animals, with a simple structure, special stability features, and competitive performance to efficiently address constrained and unconstrained problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
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Computer Science, Artificial Intelligence
Jiao Hu et al.
Summary: This research introduces an enhanced variant of the GWO algorithm named GWOCMALOL, which outperforms other algorithms in terms of convergence speed and accuracy, showing better performance in solving complex problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
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Physics, Multidisciplinary
Hongwei Kang et al.
Summary: Dimension entropy is proposed as a measure of population diversity, and a diversity control mechanism is introduced to guide the updating of the swarm intelligence algorithm, maintaining diversity in the early stages and improving algorithm performance.
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Computer Science, Interdisciplinary Applications
Mohammad H. Nadimi-Shahraki et al.
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Mathematics, Interdisciplinary Applications
GuoChun Wang et al.
Summary: The MEWOA algorithm enhances efficiency by using elite strategy and spiral motion, outperforming other algorithms in global optimization.
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Computer Science, Interdisciplinary Applications
Mohamed Abdel-Basset et al.
Summary: The paper proposed a binary version of the slime mould algorithm, BSMA, for solving MKP, and developed a more efficient variant called IBSMA with the research on three different transfer function families and two improvement steps, along with the utilization of a repair mechanism to handle constraints and infeasible solutions.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
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Mohamed Abdel-Basset et al.
Summary: Efficient and accurate estimation of unidentified parameters in photovoltaic models is crucial for simulation. This study introduces two new variants of the whale optimization algorithm for identifying parameters in the three-diode PV model, demonstrating their superiority over existing algorithms in terms of accuracy and convergence speed.
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Computer Science, Interdisciplinary Applications
Zheping Yan et al.
Summary: The enhanced whale optimization algorithm adopts the Levy flight strategy and ranking-based mutation operator to overcome the drawbacks of the basic algorithm, achieving a balanced exploration and exploitation to improve search performance.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2021)
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Computer Science, Artificial Intelligence
Hua Peng et al.
Summary: Cloud services with elastic properties are always handling various loads at different times. Efficient cloud management can be achieved through accurate load prediction, which serves as a foundation for future network development. This paper proposes a novel cloud load prediction model based on an improved whale optimization algorithm and extreme learning machine, showing good convergence and outperforming conventional swarm intelligence optimizers. The model provides a competitive solution for efficient resource management and maximizing economic benefits in the cloud environment, offering a new idea for cloud load forecasting to research groups and practitioners.
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Biotechnology & Applied Microbiology
Peter Mule Kitonyi et al.
Summary: Feature selection is the process of reducing the number of features in a dataset by removing redundant, irrelevant, and randomly class-corrected data features. By applying feature selection, data complexity can be reduced and training time can be shortened, the proposed optimizer showed promise in balancing the objectives in feature selection.
BIOMED RESEARCH INTERNATIONAL
(2021)
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Computer Science, Artificial Intelligence
Sanjoy Chakraborty et al.
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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
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Mathematical & Computational Biology
Wenqi Ji et al.
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Shikai Wang et al.
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(2021)
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Xue Wang et al.
Summary: An improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer (MFGWO-PCNN) is proposed for multimodality medical image segmentation, achieving better results compared to other algorithms through a two-stage medical image segmentation method involving image fusion and segmentation.
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(2021)
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Betul Sultan Yildiz et al.
Summary: Vehicle component design is crucial and the use of metaheuristics for optimization is common. This paper demonstrates the effectiveness of the ecogeography-based optimization algorithm in vehicle bracket shape design, showing better results compared to other optimizers.
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