Zhang, Jincheng · Zenodo (CERN European Organization for Nuclear Research) · 2025
<p><span>This paper proposes an optimization algorithm based on the working mechanism of psychologists. In actual treatment, psychologists not only intervene in patients' psychological states through overt behavior but also rely on mechanisms such as empathy, subconscious exploration, and emotional feedback regulation to achieve personalized treatment. To address the problem that traditional optimization algorithms are prone to falling into local optimality and lack adaptive adjustment, this paper abstracts the psychologist's working mode into a mathematical model and designs a subconscious-inspired optimization mechanism. By constructing a subconscious search space, an empathy selection mechanism, emotional feedback adjustment, and a two-layer iterative mechanism, the algorithm achieves global exploration and local fine-tuning of complex optimization problems. This paper describes in detail the algorithm design principles and mathematical formulas, providing a theoretical foundation for psychologist-inspired optimization methods</span>.</p>
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Łukasz Pawela (419139), Piotr Gawron (419140), Zbigniew Puchała (419141) et al. · Figshare · 2015
<p>SDP optimization of a product channel with a random Jamio lkowski matrix <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064694#pone.0064694-Bruzda1" target="_blank">[20]</a> as input.</p>
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Emad Qasim · Zenodo (CERN European Organization for Nuclear Research) · 2025
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Tarun Kumar Sharma, Om Prakash Verma · SpringerBriefs in applied sciences and technology · 2022
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Zhang, Jincheng · Zenodo (CERN European Organization for Nuclear Research) · 2025
Calligraphy, a vital component of Chinese culture, emphasizes the mastery of brush and ink, the compositional structure, and the vividness of artistic expression. Drawing inspiration from the creative practices of calligraphers, this paper proposes a novel intelligent optimization algorithm, the Calligrapher Optimization Algorithm (COA). This algorithm maps artistic creative concepts into computational intelligence methods by simulating the three core processes of calligraphers: "stroke refinement," "compositional structure," and "ink rhyme and aura." The algorithm incorporates an "ink rhyme field" mechanism to characterize the rhythmic and global coordination of the group search process. This paper systematically expounds on the algorithm's design principles, provides detailed mathematical derivations, and theoretically analyzes its convergence and applicability. Results demonstrate that the algorithm exhibits unique cultural inspiration value and potential computational performance at the modeling level. This paper does not involve specific experiments, but instead focuses on proposing a new optimization approach from a theoretical and mechanistic perspective, providing a reference for interdisciplinary artificial intelligence research.
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Zhang, Jincheng · Zenodo (CERN European Organization for Nuclear Research) · 2026
In recent years, optimization algorithms based on natural biological behaviors have been widely used in solving complex optimization problems, such as ant colony optimization, particle swarm optimization, and wolf pack optimization. This paper proposes a novel heuristic optimization algorithm—the Hamster Optimization Algorithm (HOA). Based on the foraging, storage, alertness, and social interaction behaviors of hamsters, this algorithm achieves efficient global exploration and local refinement of complex optimization problems by constructing a multi-behavior fusion mechanism, a dynamic weight adjustment mechanism, and a nonlinear perturbation mechanism. This paper systematically derives the mathematical model of the algorithm and describes the algorithm flow and parameter design in detail. Experimental results show that HOA outperforms traditional optimization algorithms in convergence speed and optimization accuracy on the standard test function, exhibiting good global search capabilities and the ability to avoid local optima, providing a new method for solving practical engineering optimization problems.
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Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
To address the problem that traditional swarm intelligence optimization algorithms generally rely on optimal individual guidance, visual assumptions, or explicit velocity models, a novel swarm intelligence optimization method—the hagfish optimization algorithm—is proposed. This algorithm is based on the non-visual survival behavior of deep-sea hagfish as a biological inspiration, constructing a novel optimization model from four aspects: information perception, memory mechanisms, group structure, and search dynamics. The hagfish optimization algorithm does not rely on optimal solution attraction, but instead achieves non-target-driven search through fitness change perception. Simultaneously, it introduces a mucus field memory mechanism in continuous space to achieve environment-level shared memory. At the group level, entanglement, breakage, and recombination mechanisms are designed to enhance the ability to escape local optima. At the dynamic level, a mucus-damped search model is constructed to suppress search oscillations. This algorithm significantly differs from existing classical swarm intelligence algorithms in its mechanism design, providing a new modeling approach for complex continuous optimization problems.
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Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
Swarm intelligence optimization algorithms have been widely studied in continuous optimization, combinatorial optimization, and engineering applications due to their lack of gradient information requirements, simple structure, and strong global search capabilities. However, most existing swarm intelligence algorithms suffer from severe strategy homogenization during the search process, reliance on manual parameter scheduling for switching between the exploration and exploitation phases, and susceptibility to getting trapped in local optima. To address these shortcomings, this paper proposes a novel swarm intelligence optimization method—the wolverine hyperevolutionary optimization algorithm with dual-awareness and structure destruction mechanisms. Based on the independent predation behavior of wolverines, this algorithm introduces a dual-awareness search mechanism, enabling individuals to adaptively switch between self-experience-driven and environmental information-driven approaches. Simultaneously, a destruction-reconstruction operator based on historical optimal structures is designed to effectively escape local optima. Furthermore, a nonlinear predation path model is proposed to enhance the algorithm's convergence stability in later stages. The algorithm achieves a natural transition from exploration to exploitation capabilities through the dynamic evolution of attack energy, thereby reducing dependence on external parameter scheduling strategies. Theoretical analysis shows that the algorithm has good interpretability in terms of search dynamics and energy allocation. Experimental results (omitted) demonstrate that this method exhibits strong global search capabilities and stability on multiple benchmark optimization problems.
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Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
Swarm optimization algorithms have been widely studied in continuous optimization, combinatorial optimization, and engineering optimization due to their simple structure, strong robustness, and good adaptability to complex nonlinear problems. However, most existing swarm optimization algorithms still rely on a single optimal individual as a global guide, which easily leads to premature convergence and search stagnation in multi-peak or complex search spaces. To address these shortcomings, this paper proposes a novel swarm optimization method—the Jackal Optimization Algorithm. Inspired by the opportunistic predation behavior of jackals in nature, this algorithm constructs a search mechanism from multiple levels, including group decision-making, opportunity perception, memory evolution, and changes in group structure. The algorithm guides the individual search direction through an opportunity potential field, introduces a false prey mechanism to enhance search diversity, and combines a memory decay containment strategy with a group fission-reorganization mechanism to improve local development performance while maintaining global exploration capabilities. Theoretically, this method differs from existing wolf pack, whale pod, and particle swarm optimization algorithms, providing a new modeling paradigm for swarm optimization.
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Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
Swarm intelligence optimization algorithms have been widely studied in continuous optimization, combinatorial optimization, and engineering applications due to their lack of gradient information requirements, simple structure, and strong global search capabilities. However, most existing swarm intelligence algorithms suffer from severe strategy homogenization during the search process, reliance on manual parameter scheduling for switching between the exploration and exploitation phases, and susceptibility to getting trapped in local optima. To address these shortcomings, this paper proposes a novel swarm intelligence optimization method—the wolverine hyperevolutionary optimization algorithm with dual-awareness and structure destruction mechanisms. Based on the independent predation behavior of wolverines, this algorithm introduces a dual-awareness search mechanism, enabling individuals to adaptively switch between self-experience-driven and environmental information-driven approaches. Simultaneously, a destruction-reconstruction operator based on historical optimal structures is designed to effectively escape local optima. Furthermore, a nonlinear predation path model is proposed to enhance the algorithm's convergence stability in later stages. The algorithm achieves a natural transition from exploration to exploitation capabilities through the dynamic evolution of attack energy, thereby reducing dependence on external parameter scheduling strategies. Theoretical analysis shows that the algorithm has good interpretability in terms of search dynamics and energy allocation. Experimental results (omitted) demonstrate that this method exhibits strong global search capabilities and stability on multiple benchmark optimization problems.
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Dadang Setiawan, Suyanto Suyanto, Bayu Erfianto et al. · Expert Systems with Applications · 2024
Cited by 7
Heming Jia, Honghua Rao, Changsheng Wen et al. · Artificial Intelligence Review · 2023
Cited by 544
Zhang, Jincheng · Zenodo (CERN European Organization for Nuclear Research) · 2026
Swarm optimization algorithms have demonstrated significant performance in continuous optimization, multimodal function solving, and complex constraint problems in recent years, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Ant Colony Optimization (ACO). This paper proposes a novel swarm optimization algorithm—the Macaw Optimization Algorithm (MOA)—inspired by the foraging behavior, social learning, and environmental alertness of macaw colonies. MOA achieves an effective balance between global search and local fine-grained search by introducing adaptive exploration-development weights, a multi-leader parrot mechanism, dynamic social learning, an alertness mechanism, and an adaptive perturbation refinement strategy. This improves search efficiency and effectively avoids premature convergence. This paper provides a detailed mathematical model of the algorithm's principles and conducts experimental verification on a classic optimization test function. The results show that MOA outperforms traditional algorithms in both convergence speed and global optimum finding capability.
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Zhang, Jincheng · Zenodo (CERN European Organization for Nuclear Research) · 2026
Swarm intelligence optimization algorithms are a class of methods that solve complex optimization problems by simulating the collective behavior of organisms in nature. This paper proposes a novel swarm intelligence optimization algorithm—the Hillstar Optimization Algorithm (HSOA), inspired by the foraging, singing, and social behaviors of myna birds in their natural environment. HSOA achieves a balance between global search and local exploitation by simulating the myna bird's multiple behavioral strategies, group cooperation mechanisms, and experience memory mechanisms. The algorithm introduces random foraging behavior, local concentrated search behavior, defensive escape behavior, contrastive learning mechanisms, and experience base memory mechanisms, which can effectively improve the convergence speed and global search capability of the algorithm in high-dimensional complex optimization problems. This paper theoretically analyzes the convergence, swarm intelligence, and adaptive cooperation capabilities of the algorithm, and provides detailed mathematical formulas describing the algorithm's update rules, providing a theoretical foundation for subsequent experiments and applications.
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Martin Pelikán · Studies in fuzziness and soft computing · 2005
Cited by 205
Parsa Parsafar, Iman Esmaili Paeen Afrakoti · SSRN Electronic Journal · 2025
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Zhihao Li (359073), Ping Xiao (1600990), Jiabao Pan (5318612) et al. · Figshare · 2025
<div><p>In order to solve the problem of poor adaptability and robustness of the rule-based energy management strategy (EMS) in hybrid commercial vehicles, leading to suboptimal vehicle economy, this paper proposes an improved dung beetle algorithm (DBO) optimized multi-fuzzy control EMS. First, the rule-based EMS is established by dividing the efficient working areas of the methanol engine and power battery. The Tent chaotic mapping is then used to integrate strategies of cosine, Lévy flight, and Cauchy Gaussian mutation, improving the DBO. This integration compensates for the traditional dung beetle algorithm’s tendency to fall into local optima and enhances its global search capability. Subsequently, fuzzy controllers for the driving charging mode and hybrid driving mode are designed under this rule-based EMS. Finally, the improved DBO is used to obtain the optimal control of the fuzzy controller by taking the fuel consumption of the whole vehicle and the fluctuation change of the battery state of charge (<i>SOC</i>) as the optimization objectives. Compared to traditional rule-based energy management strategies, the optimized fuzzy control using the enhanced DBO continuously adjusts the torque distribution between the engine and motor based on the vehicle’s real-time state, resulting in a 9.07% reduction in fuel consumption and a 3.43% decrease in battery <i>SOC</i> fluctuations.</p></div>
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Emad Qasim · Zenodo (CERN European Organization for Nuclear Research) · 2025
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Fevrier Valdez, Hector Carreon-Ortiz, Oscar Castillo · SpringerBriefs in applied sciences and technology · 2023
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Zhang, Jincheng · Zenodo (CERN European Organization for Nuclear Research) · 2025
This paper proposes an optimization algorithm inspired by cobra behavior—the Cobra Optimization Algorithm (COA). This algorithm leverages the cobra's hunting strategy, neck extension deterrent behavior, venom concentration adaptive mechanism, and group collaboration as its core inspiration to achieve an effective balance between local search and global exploration. This paper mathematically models the algorithm, deriving in detail the position update formula, local and global search mechanisms, group collaboration mechanism, and multimodal global search strategy. The algorithm's convergence and search characteristics are analyzed. This algorithm possesses unique biological behavior mapping capabilities and multimodal optimization potential, providing a new solution strategy for complex optimization problems.
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